ClinicoPath Descriptives 04: Data Quality Assessment for Clinical Research
Source:vignettes/clinicopath-descriptives-04-data-quality-exploration.Rmd
clinicopath-descriptives-04-data-quality-exploration.RmdIntroduction
Data quality assessment is fundamental to reliable clinical research. This vignette demonstrates comprehensive data exploration and quality assessment techniques using ClinicoPathDescriptives, including missing data analysis, distribution assessment, outlier detection, and fraud detection using Benford’s law.
Data Quality Framework
Quality Dimensions
- Completeness: Presence of missing data
- Consistency: Logical relationships between variables
- Accuracy: Conformance to expected patterns
- Validity: Adherence to defined formats and ranges
- Integrity: Referential and business rule compliance
library(ClinicoPath)
library(dplyr)
library(tidyr)
library(ggplot2)
library(knitr)
# Load the dataset
data(histopathology)
# Initial data overview
cat("Dataset Dimensions:", dim(histopathology), "\n")
#> Dataset Dimensions: 250 38
cat("Variables:", ncol(histopathology), "\n")
#> Variables: 38
cat("Observations:", nrow(histopathology), "\n\n")
#> Observations: 250
# Variable types overview
str(histopathology, give.attr = FALSE)
#> spc_tbl_ [250 × 38] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#> $ ID : num [1:250] 1 2 3 4 5 6 7 8 9 10 ...
#> $ Name : chr [1:250] "Tonisia" "Daniyah" "Naviana" "Daerion" ...
#> $ Sex : chr [1:250] "Male" "Female" "Male" "Male" ...
#> $ Age : num [1:250] 27 36 65 51 58 53 33 26 25 68 ...
#> $ Race : chr [1:250] "White" "White" "White" "White" ...
#> $ PreinvasiveComponent: chr [1:250] "Present" "Absent" "Absent" "Absent" ...
#> $ LVI : chr [1:250] "Present" "Absent" "Absent" "Present" ...
#> $ PNI : chr [1:250] "Absent" "Absent" "Absent" "Absent" ...
#> $ LastFollowUpDate : chr [1:250] "2019.10.22 00:00:00" "2019.06.22 00:00:00" "2019.08.22 00:00:00" "2019.03.22 00:00:00" ...
#> $ Death : chr [1:250] "YANLIŞ" "DOĞRU" "DOĞRU" "YANLIŞ" ...
#> $ Group : chr [1:250] "Control" "Treatment" "Control" "Treatment" ...
#> $ Grade : num [1:250] 2 2 1 3 2 2 1 2 3 3 ...
#> $ TStage : num [1:250] 4 4 4 4 1 4 2 3 4 4 ...
#> $ Anti-X-intensity : num [1:250] 3 2 2 3 3 3 2 2 1 2 ...
#> $ Anti-Y-intensity : num [1:250] 1 1 2 3 3 2 2 2 1 3 ...
#> $ LymphNodeMetastasis : chr [1:250] "Present" "Absent" "Absent" "Absent" ...
#> $ Valid : chr [1:250] "YANLIŞ" "DOĞRU" "YANLIŞ" "DOĞRU" ...
#> $ Smoker : chr [1:250] "YANLIŞ" "YANLIŞ" "DOĞRU" "YANLIŞ" ...
#> $ Grade_Level : chr [1:250] "high" "low" "low" "high" ...
#> $ SurgeryDate : chr [1:250] "2019.07.08 00:00:00" "2019.03.18 00:00:00" "2019.05.18 00:00:00" "2018.10.24 00:00:00" ...
#> $ DeathTime : chr [1:250] "Within1Year" "Within1Year" "Within1Year" "Within1Year" ...
#> $ int : chr [1:250] "2019-07-08 UTC--2019-10-22 UTC" "2019-03-18 UTC--2019-06-22 UTC" "2019-05-18 UTC--2019-08-22 UTC" "2018-10-24 UTC--2019-03-22 UTC" ...
#> $ OverallTime : num [1:250] 3.5 3.1 3.1 4.9 3.3 9.3 6.3 9 5.8 9.9 ...
#> $ Outcome : num [1:250] 0 1 1 0 0 0 1 1 1 0 ...
#> $ Mortality5yr : chr [1:250] "Alive" "Dead" "Dead" "Alive" ...
#> $ Rater 1 : num [1:250] 0 1 1 0 0 0 1 1 1 0 ...
#> $ Rater 2 : num [1:250] 0 0 0 0 0 0 0 0 0 0 ...
#> $ Rater 3 : num [1:250] 1 1 1 0 1 1 1 1 1 1 ...
#> $ Rater A : num [1:250] 3 2 3 3 2 3 1 1 2 1 ...
#> $ Rater B : num [1:250] 3 2 3 3 2 3 1 1 2 1 ...
#> $ New Test : num [1:250] 0 0 0 0 0 0 1 0 0 0 ...
#> $ Golden Standart : num [1:250] 0 0 0 0 0 0 0 0 0 0 ...
#> $ MeasurementA : num [1:250] -1.63432 0.37071 0.01585 -1.23584 -0.00141 ...
#> $ MeasurementB : num [1:250] 0.611 0.554 0.742 0.622 0.527 ...
#> $ Disease Status : chr [1:250] "Ill" "Ill" "Healthy" "Ill" ...
#> $ Measurement1 : num [1:250] 0.387 0.829 0.159 2.447 0.847 ...
#> $ Measurement2 : num [1:250] 1.8654 0.5425 0.0701 2.4071 0.5564 ...
#> $ Outcome2 : chr [1:250] "DOD" "DOOC" "AWD" "AWOD" ...Phase 1: Completeness Assessment
Missing Data Analysis
# Calculate missing data percentages
missing_summary <- histopathology %>%
summarise_all(~sum(is.na(.))) %>%
pivot_longer(everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(
Missing_Percentage = round(Missing_Count / nrow(histopathology) * 100, 1),
Data_Quality = case_when(
Missing_Percentage == 0 ~ "Complete",
Missing_Percentage <= 5 ~ "Excellent",
Missing_Percentage <= 10 ~ "Good",
Missing_Percentage <= 20 ~ "Acceptable",
TRUE ~ "Poor"
)
) %>%
arrange(desc(Missing_Percentage))
# Display missing data summary
kable(missing_summary %>% filter(Missing_Count > 0),
caption = "Missing Data Summary",
col.names = c("Variable", "Missing Count", "Missing %", "Quality Rating"))| Variable | Missing Count | Missing % | Quality Rating |
|---|---|---|---|
| OverallTime | 2 | 0.8 | Excellent |
| Name | 1 | 0.4 | Excellent |
| Sex | 1 | 0.4 | Excellent |
| Age | 1 | 0.4 | Excellent |
| Race | 1 | 0.4 | Excellent |
| PreinvasiveComponent | 1 | 0.4 | Excellent |
| LVI | 1 | 0.4 | Excellent |
| PNI | 1 | 0.4 | Excellent |
| LastFollowUpDate | 1 | 0.4 | Excellent |
| Death | 1 | 0.4 | Excellent |
| Group | 1 | 0.4 | Excellent |
| Grade | 1 | 0.4 | Excellent |
| TStage | 1 | 0.4 | Excellent |
| Anti-X-intensity | 1 | 0.4 | Excellent |
| Anti-Y-intensity | 1 | 0.4 | Excellent |
| LymphNodeMetastasis | 1 | 0.4 | Excellent |
| Valid | 1 | 0.4 | Excellent |
| Smoker | 1 | 0.4 | Excellent |
| Grade_Level | 1 | 0.4 | Excellent |
| SurgeryDate | 1 | 0.4 | Excellent |
| Outcome | 1 | 0.4 | Excellent |
| Mortality5yr | 1 | 0.4 | Excellent |
| Rater 1 | 1 | 0.4 | Excellent |
Completeness by Variable Type
# Analyze completeness by variable groups
demographic_vars <- c("Age", "Sex", "Race")
pathology_vars <- c("Grade", "TStage", "LVI", "PNI", "LymphNodeMetastasis")
biomarker_vars <- c("MeasurementA", "MeasurementB", "Anti-X-intensity", "Anti-Y-intensity")
outcome_vars <- c("Outcome", "Death", "OverallTime", "Mortality5yr")
# Function to calculate completeness for variable groups
assess_completeness <- function(data, vars, group_name) {
if(all(vars %in% names(data))) {
completeness <- data %>%
select(all_of(vars)) %>%
summarise_all(~sum(!is.na(.))) %>%
pivot_longer(everything(), names_to = "Variable", values_to = "Complete_Count") %>%
mutate(
Group = group_name,
Completeness_Rate = round(Complete_Count / nrow(data) * 100, 1)
)
return(completeness)
}
return(NULL)
}
# Assess each variable group
completeness_by_group <- bind_rows(
assess_completeness(histopathology, demographic_vars, "Demographics"),
assess_completeness(histopathology, pathology_vars, "Pathology"),
assess_completeness(histopathology, biomarker_vars, "Biomarkers"),
assess_completeness(histopathology, outcome_vars, "Outcomes")
) %>%
filter(!is.null(.))
# Summary by group
group_summary <- completeness_by_group %>%
group_by(Group) %>%
summarise(
Variables = n(),
Mean_Completeness = round(mean(Completeness_Rate), 1),
Min_Completeness = round(min(Completeness_Rate), 1),
.groups = 'drop'
)
kable(group_summary,
caption = "Data Completeness by Variable Group",
col.names = c("Variable Group", "Variables", "Mean Completeness (%)", "Min Completeness (%)"))| Variable Group | Variables | Mean Completeness (%) | Min Completeness (%) |
|---|---|---|---|
| Biomarkers | 4 | 99.8 | 99.6 |
| Demographics | 3 | 99.6 | 99.6 |
| Outcomes | 4 | 99.5 | 99.2 |
| Pathology | 5 | 99.6 | 99.6 |
Phase 2: Distribution Assessment
Continuous Variables Analysis
# Analyze distributions of continuous variables
continuous_vars <- c("Age", "OverallTime", "MeasurementA", "MeasurementB")
# Check which variables exist in the dataset
available_vars <- continuous_vars[continuous_vars %in% names(histopathology)]
if(length(available_vars) > 0) {
tryCatch({
summarydata(
data = histopathology,
vars = available_vars,
date_vars = NULL,
grvar = NULL
)
}, error = function(e) {
cat("Error with summarydata:", conditionMessage(e), "\n")
cat("Available continuous variables:", paste(available_vars, collapse = ", "), "\n")
cat("Variable types:\n")
print(sapply(histopathology[available_vars], class))
})
} else {
cat("No continuous variables found in dataset\n")
}
#> Error with summarydata: 'names' attribute [38] must be the same length as the vector [0]
#> Available continuous variables: Age, OverallTime, MeasurementA, MeasurementB
#> Variable types:
#> Age OverallTime MeasurementA MeasurementB
#> "numeric" "numeric" "numeric" "numeric"Categorical Variables Analysis
# Analyze categorical variable distributions
categorical_vars <- c("Sex", "Grade", "TStage", "Group", "LVI", "PNI")
# Check which categorical variables exist in the dataset
available_cat_vars <- categorical_vars[categorical_vars %in% names(histopathology)]
if(length(available_cat_vars) > 0) {
tryCatch({
reportcat(
data = histopathology,
vars = available_cat_vars
)
}, error = function(e) {
cat("Error with reportcat:", conditionMessage(e), "\n")
cat("Available categorical variables:", paste(available_cat_vars, collapse = ", "), "\n")
})
} else {
cat("No categorical variables found in dataset\n")
}
#> Error with reportcat: 'names' attribute [38] must be the same length as the vector [0]
#> Available categorical variables: Sex, Grade, TStage, Group, LVI, PNIVariable Relationship Overview
# Hierarchical overview of key variables
vartree_vars <- c("Group", "Grade", "TStage", "LVI", "PNI", "LymphNodeMetastasis", "Outcome")
available_vartree_vars <- vartree_vars[vartree_vars %in% names(histopathology)]
if(length(available_vartree_vars) > 0) {
tryCatch({
vartree(
data = histopathology,
vars = available_vartree_vars,
percvar = NULL,
percvarLevel = NULL,
summaryvar = NULL,
prunebelow = NULL,
pruneLevel1 = NULL,
pruneLevel2 = NULL,
follow = NULL,
followLevel1 = NULL,
followLevel2 = NULL,
excl = FALSE,
vp = TRUE,
horizontal = FALSE,
sline = TRUE,
varnames = FALSE,
nodelabel = TRUE,
pct = FALSE
)
}, error = function(e) {
cat("Error with vartree:", conditionMessage(e), "\n")
cat("Available variables for tree:", paste(available_vartree_vars, collapse = ", "), "\n")
})
} else {
cat("No variables found for variable tree\n")
}
#> Error with vartree: 'names' attribute [38] must be the same length as the vector [0]
#> Available variables for tree: Group, Grade, TStage, LVI, PNI, LymphNodeMetastasis, OutcomePhase 3: Consistency and Logic Checks
Temporal Consistency
# Check date consistency (if dates are properly formatted)
date_vars <- histopathology %>%
select(contains("Date")) %>%
names()
if(length(date_vars) > 0) {
cat("Date variables found:", paste(date_vars, collapse = ", "), "\n")
# Basic date analysis
tryCatch({
reportcat(
data = histopathology,
vars = date_vars[1:min(2, length(date_vars))]
)
}, error = function(e) {
cat("Error with date variables reportcat:", conditionMessage(e), "\n")
cat("Date variables:", paste(date_vars, collapse = ", "), "\n")
})
}
#> Date variables found: LastFollowUpDate, SurgeryDate
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#> <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
#> <thead>
#> <tr class="gt_heading">
#> <td colspan="4" class="gt_heading gt_title gt_font_normal" style>.</td>
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#> <tr class="gt_heading">
#> <td colspan="4" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>250 rows x 2 cols</td>
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#> <tr class="gt_col_headings">
#> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="type"></th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="name">Column</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="value">Plot Overview</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="n_missing">Missing</th>
#> </tr>
#> </thead>
#> <tbody class="gt_table_body">
#> <tr><td headers="type" class="gt_row gt_left"><svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:20px;width:20px;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#4e79a7;overflow:visible;position:relative;"><path d="M40 48C26.7 48 16 58.7 16 72v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V72c0-13.3-10.7-24-24-24H40zM192 64c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zM16 232v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V232c0-13.3-10.7-24-24-24H40c-13.3 0-24 10.7-24 24zM40 368c-13.3 0-24 10.7-24 24v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V392c0-13.3-10.7-24-24-24H40z"/></svg></td>
#> <td headers="name" class="gt_row gt_left" style="font-weight: bold;"><div style='max-width: 150px;'>
#> <details style='font-weight: normal !important;'>
#> <summary style='font-weight: bold !important;'>LastFollowUpDate</summary>
#> 2019.06.22 00:00:00, 2019.03.22 00:00:00, 2018.12.22 00:00:00, 2019.07.22 00:00:00, 2019.08.22 00:00:00, 2019.02.22 00:00:00, 2019.05.22 00:00:00, 2019.11.22 00:00:00, 2019.01.22 00:00:00, 2019.04.22 00:00:00, 2019.10.22 00:00:00 and 2019.09.22 00:00:00
#> </details></div></td>
#> <td headers="value" class="gt_row gt_center"><?xml version='1.0' encoding='UTF-8' ?><svg xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' width='141.73pt' height='34.02pt' viewBox='0 0 141.73 34.02'><g class='svglite'><defs> <style type='text/css'><![CDATA[ .svglite line, .svglite polyline, .svglite polygon, .svglite path, .svglite rect, .svglite circle { fill: none; stroke: #000000; stroke-linecap: round; stroke-linejoin: round; stroke-miterlimit: 10.00; } .svglite text { white-space: pre; } .svglite g.glyphgroup path { fill: inherit; stroke: none; } ]]></style></defs><rect width='100%' height='100%' style='stroke: none; fill: none;'/><defs> <clipPath id='cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg=='> <rect x='0.00' y='0.00' width='141.73' height='34.02' /> </clipPath></defs><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'></g><defs> <clipPath id='cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ=='> <rect x='1.00' y='2.99' width='139.74' height='20.62' /> </clipPath></defs><g clip-path='url(#cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ==)'><rect x='132.88' y='3.93' width='7.86' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #DDEAF7;' /><rect x='123.34' y='3.93' width='9.54' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #CFE0F2;' /><rect x='112.68' y='3.93' width='10.66' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #C2D6EC;' /><rect x='102.01' y='3.93' width='10.66' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #B4CCE7;' /><rect x='90.79' y='3.93' width='11.22' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #A6C2E2;' /><rect x='79.56' y='3.93' width='11.22' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #98B9DD;' /><rect x='67.78' y='3.93' width='11.79' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #89AFD7;' /><rect x='54.87' y='3.93' width='12.91' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #7AA6D2;' /><rect x='41.96' y='3.93' width='12.91' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #6B9CCD;' /><rect x='29.06' y='3.93' width='12.91' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #5A93C8;' /><rect x='15.59' y='3.93' width='13.47' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #488AC2;' /><rect x='1.00' y='3.93' width='14.59' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #3181BD;' /></g><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'><text x='1.00' y='30.18' style='font-size: 8.00px; font-family: "Arial";' textLength='48.06px' lengthAdjust='spacingAndGlyphs'>12 categories</text></g></g></svg></td>
#> <td headers="n_missing" class="gt_row gt_right">0.4%</td></tr>
#> <tr><td headers="type" class="gt_row gt_left gt_striped"><svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:20px;width:20px;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#4e79a7;overflow:visible;position:relative;"><path d="M40 48C26.7 48 16 58.7 16 72v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V72c0-13.3-10.7-24-24-24H40zM192 64c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zM16 232v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V232c0-13.3-10.7-24-24-24H40c-13.3 0-24 10.7-24 24zM40 368c-13.3 0-24 10.7-24 24v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V392c0-13.3-10.7-24-24-24H40z"/></svg></td>
#> <td headers="name" class="gt_row gt_left gt_striped" style="font-weight: bold;"><div style='max-width: 150px;'>
#> <details style='font-weight: normal !important;'>
#> <summary style='font-weight: bold !important;'>SurgeryDate</summary>
#> 2018.10.19 00:00:00, 2018.12.12 00:00:00, 2017.05.24 00:00:00, 2018.03.23 00:00:00, 2018.06.02 00:00:00, 2018.06.10 00:00:00, 2018.06.21 00:00:00, 2018.07.04 00:00:00, 2018.07.27 00:00:00, 2018.08.08 00:00:00, 2018.08.21 00:00:00, 2018.08.24 00:00:00, 2018.10.01 00:00:00, 2018.10.20 00:00:00, 2018.11.11 00:00:00, 2018.11.16 00:00:00, 2018.11.19 00:00:00, 2018.11.26 00:00:00, 2018.12.04 00:00:00, 2018.12.22 00:00:00, 2018.12.31 00:00:00, 2019.01.17 00:00:00, 2019.02.27 00:00:00, 2019.03.01 00:00:00, 2019.05.02 00:00:00, 2014.03.15 00:00:00, 2014.07.21 00:00:00, 2014.08.10 00:00:00, 2014.09.22 00:00:00, 2014.10.08 00:00:00, 2014.10.10 00:00:00, 2014.12.05 00:00:00, 2014.12.15 00:00:00, 2015.01.02 00:00:00, 2015.01.31 00:00:00, 2015.05.07 00:00:00, 2015.05.11 00:00:00, 2015.05.16 00:00:00, 2015.06.27 00:00:00, 2015.06.29 00:00:00, 2015.09.20 00:00:00, 2015.09.30 00:00:00, 2016.01.26 00:00:00, 2016.02.22 00:00:00, 2016.02.23 00:00:00, 2016.02.28 00:00:00, 2016.03.06 00:00:00, 2016.04.01 00:00:00, 2016.04.05 00:00:00, 2016.04.19 00:00:00, 2016.04.21 00:00:00, 2016.06.07 00:00:00, 2016.06.08 00:00:00, 2016.07.14 00:00:00, 2016.08.11 00:00:00, 2016.08.27 00:00:00, 2016.09.05 00:00:00, 2016.10.15 00:00:00, 2016.10.16 00:00:00, 2016.10.22 00:00:00, 2016.10.30 00:00:00, 2016.11.02 00:00:00, 2016.11.05 00:00:00, 2016.11.07 00:00:00, 2016.11.17 00:00:00, 2016.11.18 00:00:00, 2016.11.26 00:00:00, 2016.11.28 00:00:00, 2016.12.12 00:00:00, 2016.12.21 00:00:00, 2016.12.22 00:00:00, 2016.12.26 00:00:00, 2017.01.07 00:00:00, 2017.01.11 00:00:00, 2017.01.29 00:00:00, 2017.01.30 00:00:00, 2017.01.31 00:00:00, 2017.02.25 00:00:00, 2017.04.11 00:00:00, 2017.04.14 00:00:00, 2017.04.24 00:00:00, 2017.05.12 00:00:00, 2017.05.16 00:00:00, 2017.05.28 00:00:00, 2017.05.31 00:00:00, 2017.06.09 00:00:00, 2017.06.16 00:00:00, 2017.06.29 00:00:00, 2017.06.30 00:00:00, 2017.07.07 00:00:00, 2017.07.11 00:00:00, 2017.07.27 00:00:00, 2017.08.11 00:00:00, 2017.08.13 00:00:00, 2017.08.19 00:00:00, 2017.09.07 00:00:00, 2017.09.27 00:00:00, 2017.10.02 00:00:00, 2017.10.07 00:00:00, 2017.10.08 00:00:00, 2017.10.15 00:00:00, 2017.10.20 00:00:00, 2017.10.28 00:00:00, 2017.11.11 00:00:00, 2017.11.23 00:00:00, 2017.11.27 00:00:00, 2017.12.04 00:00:00, 2017.12.19 00:00:00, 2017.12.20 00:00:00, 2017.12.28 00:00:00, 2018.01.11 00:00:00, 2018.01.21 00:00:00, 2018.01.22 00:00:00, 2018.01.25 00:00:00, 2018.02.03 00:00:00, 2018.02.09 00:00:00, 2018.02.10 00:00:00, 2018.02.24 00:00:00, 2018.03.04 00:00:00, 2018.03.13 00:00:00, 2018.03.14 00:00:00, 2018.03.18 00:00:00, 2018.03.21 00:00:00, 2018.03.24 00:00:00, 2018.03.25 00:00:00, 2018.03.27 00:00:00, 2018.03.28 00:00:00, 2018.03.31 00:00:00, 2018.04.09 00:00:00, 2018.04.18 00:00:00, 2018.04.20 00:00:00, 2018.04.29 00:00:00, 2018.05.07 00:00:00, 2018.05.08 00:00:00, 2018.05.12 00:00:00, 2018.05.13 00:00:00, 2018.05.19 00:00:00, 2018.05.24 00:00:00, 2018.05.26 00:00:00, 2018.05.30 00:00:00, 2018.06.01 00:00:00, 2018.06.03 00:00:00, 2018.06.04 00:00:00, 2018.06.05 00:00:00, 2018.06.18 00:00:00, 2018.06.29 00:00:00, 2018.07.01 00:00:00, 2018.07.02 00:00:00, 2018.07.11 00:00:00, 2018.07.13 00:00:00, 2018.07.18 00:00:00, 2018.07.20 00:00:00, 2018.07.26 00:00:00, 2018.08.02 00:00:00, 2018.08.04 00:00:00, 2018.08.07 00:00:00, 2018.08.10 00:00:00, 2018.08.11 00:00:00, 2018.08.12 00:00:00, 2018.08.13 00:00:00, 2018.08.17 00:00:00, 2018.08.19 00:00:00, 2018.08.23 00:00:00, 2018.08.27 00:00:00, 2018.08.30 00:00:00, 2018.09.02 00:00:00, 2018.09.04 00:00:00, 2018.09.08 00:00:00, 2018.09.11 00:00:00, 2018.09.14 00:00:00, 2018.09.16 00:00:00, 2018.10.02 00:00:00, 2018.10.03 00:00:00, 2018.10.04 00:00:00, 2018.10.09 00:00:00, 2018.10.13 00:00:00, 2018.10.14 00:00:00, 2018.10.17 00:00:00, 2018.10.18 00:00:00, 2018.10.23 00:00:00, 2018.10.24 00:00:00, 2018.10.25 00:00:00, 2018.11.09 00:00:00, 2018.11.15 00:00:00, 2018.11.17 00:00:00, 2018.11.22 00:00:00, 2018.11.27 00:00:00, 2018.11.30 00:00:00, 2018.12.16 00:00:00, 2018.12.29 00:00:00, 2019.01.11 00:00:00, 2019.01.12 00:00:00, 2019.01.13 00:00:00, 2019.01.18 00:00:00, 2019.01.22 00:00:00, 2019.01.24 00:00:00, 2019.02.04 00:00:00, 2019.02.05 00:00:00, 2019.02.08 00:00:00, 2019.02.09 00:00:00, 2019.02.15 00:00:00, 2019.02.16 00:00:00, 2019.02.19 00:00:00, 2019.03.04 00:00:00, 2019.03.12 00:00:00, 2019.03.18 00:00:00, 2019.03.22 00:00:00, 2019.03.26 00:00:00, 2019.03.31 00:00:00, 2019.04.07 00:00:00, 2019.04.19 00:00:00, 2019.05.15 00:00:00, 2019.05.17 00:00:00, 2019.05.18 00:00:00, 2019.05.23 00:00:00, 2019.05.24 00:00:00, 2019.05.25 00:00:00, 2019.06.24 00:00:00, 2019.07.08 00:00:00, 2019.08.05 00:00:00, 2019.08.16 00:00:00 and 2019.08.22 00:00:00
#> </details></div></td>
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#> <td headers="n_missing" class="gt_row gt_right gt_striped">0.4%</td></tr>
#> </tbody>
#>
#>
#> </table>
#> </div>
#>
#> SUMMARY OF CATEGORICAL VARIABLES
#>
#> character(0)
#>
#> LastFollowUpDate has 250 observations and 12 levels.
#> 2018.12.22 00:00:00: n = 23, 9.24% of valid cases.
#> 2019.01.22 00:00:00: n = 19, 7.63% of valid cases.
#> 2019.02.22 00:00:00: n = 21, 8.43% of valid cases.
#> 2019.03.22 00:00:00: n = 24, 9.64% of valid cases.
#> 2019.04.22 00:00:00: n = 19, 7.63% of valid cases.
#> 2019.05.22 00:00:00: n = 20, 8.03% of valid cases.
#> 2019.06.22 00:00:00: n = 26, 10.44% of valid cases.
#> 2019.07.22 00:00:00: n = 23, 9.24% of valid cases.
#> 2019.08.22 00:00:00: n = 23, 9.24% of valid cases.
#> 2019.09.22 00:00:00: n = 14, 5.62% of valid cases.
#> 2019.10.22 00:00:00: n = 17, 6.83% of valid cases.
#> 2019.11.22 00:00:00: n = 20, 8.03% of valid cases.
#> Missing values: 1.
#>
#> SurgeryDate has 250 observations and 222 levels.
#> (Other): n = 124, 49.8% of valid cases.
#> 2014.03.15 00:00:00: n = 1, 0.4% of valid cases.
#> 2014.07.21 00:00:00: n = 1, 0.4% of valid cases.
#> 2014.08.10 00:00:00: n = 1, 0.4% of valid cases.
#> 2014.09.22 00:00:00: n = 1, 0.4% of valid cases.
#> 2014.10.08 00:00:00: n = 1, 0.4% of valid cases.
#> 2014.10.10 00:00:00: n = 1, 0.4% of valid cases.
#> 2014.12.05 00:00:00: n = 1, 0.4% of valid cases.
#> 2014.12.15 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.01.02 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.01.31 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.05.07 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.05.11 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.05.16 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.06.27 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.06.29 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.09.20 00:00:00: n = 1, 0.4% of valid cases.
#> 2015.09.30 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.01.26 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.02.22 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.02.23 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.02.28 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.03.06 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.04.01 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.04.05 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.04.19 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.04.21 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.06.07 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.06.08 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.07.14 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.08.11 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.08.27 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.09.05 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.10.15 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.10.16 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.10.22 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.10.30 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.11.02 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.11.05 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.11.07 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.11.17 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.11.18 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.11.26 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.11.28 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.12.12 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.12.21 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.12.22 00:00:00: n = 1, 0.4% of valid cases.
#> 2016.12.26 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.01.07 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.01.11 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.01.29 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.01.30 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.01.31 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.02.25 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.04.11 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.04.14 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.04.24 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.05.12 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.05.16 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.05.24 00:00:00: n = 2, 0.8% of valid cases.
#> 2017.05.28 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.05.31 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.06.09 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.06.16 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.06.29 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.06.30 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.07.07 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.07.11 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.07.27 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.08.11 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.08.13 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.08.19 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.09.07 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.09.27 00:00:00: n = 1, 0.4% of valid cases.
#> 2017.10.02 00:00:00: n = 1, 0.4% of valid cases.
#> 2018.03.23 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.06.02 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.06.10 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.06.21 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.07.04 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.07.27 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.08.08 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.08.21 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.08.24 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.10.01 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.10.19 00:00:00: n = 3, 1.2% of valid cases.
#> 2018.10.20 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.11.11 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.11.16 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.11.19 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.11.26 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.12.04 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.12.12 00:00:00: n = 3, 1.2% of valid cases.
#> 2018.12.22 00:00:00: n = 2, 0.8% of valid cases.
#> 2018.12.31 00:00:00: n = 2, 0.8% of valid cases.
#> 2019.01.17 00:00:00: n = 2, 0.8% of valid cases.
#> 2019.02.27 00:00:00: n = 2, 0.8% of valid cases.
#> 2019.03.01 00:00:00: n = 2, 0.8% of valid cases.
#> 2019.05.02 00:00:00: n = 2, 0.8% of valid cases.
#> Missing values: 1.
#>
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#> data-quarto-bootstrap="false">
#>
#> <tr class="gt_heading">
#> <td colspan="4" class="gt_heading gt_title gt_font_normal" style>.
#>
#> <tr class="gt_heading">
#> <td colspan="4" class="gt_heading gt_subtitle gt_font_normal
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#> <th class="gt_col_heading gt_columns_bottom_border gt_left"
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#> <th class="gt_col_heading gt_columns_bottom_border gt_left"
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#>
#>
#> <tbody class="gt_table_body">
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#> 2019.06.22 00:00:00, 2019.03.22 00:00:00, 2018.12.22 00:00:00,
#> 2019.07.22 00:00:00, 2019.08.22 00:00:00, 2019.02.22 00:00:00,
#> 2019.05.22 00:00:00, 2019.11.22 00:00:00, 2019.01.22 00:00:00,
#> 2019.04.22 00:00:00, 2019.10.22 00:00:00 and 2019.09.22 00:00:00
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#> <td headers="type" class="gt_row gt_left gt_striped"><svg
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#> <details style='font-weight: normal !important;'>
#> <summary style='font-weight: bold !important;'>SurgeryDate
#> 2018.10.19 00:00:00, 2018.12.12 00:00:00, 2017.05.24 00:00:00,
#> 2018.03.23 00:00:00, 2018.06.02 00:00:00, 2018.06.10 00:00:00,
#> 2018.06.21 00:00:00, 2018.07.04 00:00:00, 2018.07.27 00:00:00,
#> 2018.08.08 00:00:00, 2018.08.21 00:00:00, 2018.08.24 00:00:00,
#> 2018.10.01 00:00:00, 2018.10.20 00:00:00, 2018.11.11 00:00:00,
#> 2018.11.16 00:00:00, 2018.11.19 00:00:00, 2018.11.26 00:00:00,
#> 2018.12.04 00:00:00, 2018.12.22 00:00:00, 2018.12.31 00:00:00,
#> 2019.01.17 00:00:00, 2019.02.27 00:00:00, 2019.03.01 00:00:00,
#> 2019.05.02 00:00:00, 2014.03.15 00:00:00, 2014.07.21 00:00:00,
#> 2014.08.10 00:00:00, 2014.09.22 00:00:00, 2014.10.08 00:00:00,
#> 2014.10.10 00:00:00, 2014.12.05 00:00:00, 2014.12.15 00:00:00,
#> 2015.01.02 00:00:00, 2015.01.31 00:00:00, 2015.05.07 00:00:00,
#> 2015.05.11 00:00:00, 2015.05.16 00:00:00, 2015.06.27 00:00:00,
#> 2015.06.29 00:00:00, 2015.09.20 00:00:00, 2015.09.30 00:00:00,
#> 2016.01.26 00:00:00, 2016.02.22 00:00:00, 2016.02.23 00:00:00,
#> 2016.02.28 00:00:00, 2016.03.06 00:00:00, 2016.04.01 00:00:00,
#> 2016.04.05 00:00:00, 2016.04.19 00:00:00, 2016.04.21 00:00:00,
#> 2016.06.07 00:00:00, 2016.06.08 00:00:00, 2016.07.14 00:00:00,
#> 2016.08.11 00:00:00, 2016.08.27 00:00:00, 2016.09.05 00:00:00,
#> 2016.10.15 00:00:00, 2016.10.16 00:00:00, 2016.10.22 00:00:00,
#> 2016.10.30 00:00:00, 2016.11.02 00:00:00, 2016.11.05 00:00:00,
#> 2016.11.07 00:00:00, 2016.11.17 00:00:00, 2016.11.18 00:00:00,
#> 2016.11.26 00:00:00, 2016.11.28 00:00:00, 2016.12.12 00:00:00,
#> 2016.12.21 00:00:00, 2016.12.22 00:00:00, 2016.12.26 00:00:00,
#> 2017.01.07 00:00:00, 2017.01.11 00:00:00, 2017.01.29 00:00:00,
#> 2017.01.30 00:00:00, 2017.01.31 00:00:00, 2017.02.25 00:00:00,
#> 2017.04.11 00:00:00, 2017.04.14 00:00:00, 2017.04.24 00:00:00,
#> 2017.05.12 00:00:00, 2017.05.16 00:00:00, 2017.05.28 00:00:00,
#> 2017.05.31 00:00:00, 2017.06.09 00:00:00, 2017.06.16 00:00:00,
#> 2017.06.29 00:00:00, 2017.06.30 00:00:00, 2017.07.07 00:00:00,
#> 2017.07.11 00:00:00, 2017.07.27 00:00:00, 2017.08.11 00:00:00,
#> 2017.08.13 00:00:00, 2017.08.19 00:00:00, 2017.09.07 00:00:00,
#> 2017.09.27 00:00:00, 2017.10.02 00:00:00, 2017.10.07 00:00:00,
#> 2017.10.08 00:00:00, 2017.10.15 00:00:00, 2017.10.20 00:00:00,
#> 2017.10.28 00:00:00, 2017.11.11 00:00:00, 2017.11.23 00:00:00,
#> 2017.11.27 00:00:00, 2017.12.04 00:00:00, 2017.12.19 00:00:00,
#> 2017.12.20 00:00:00, 2017.12.28 00:00:00, 2018.01.11 00:00:00,
#> 2018.01.21 00:00:00, 2018.01.22 00:00:00, 2018.01.25 00:00:00,
#> 2018.02.03 00:00:00, 2018.02.09 00:00:00, 2018.02.10 00:00:00,
#> 2018.02.24 00:00:00, 2018.03.04 00:00:00, 2018.03.13 00:00:00,
#> 2018.03.14 00:00:00, 2018.03.18 00:00:00, 2018.03.21 00:00:00,
#> 2018.03.24 00:00:00, 2018.03.25 00:00:00, 2018.03.27 00:00:00,
#> 2018.03.28 00:00:00, 2018.03.31 00:00:00, 2018.04.09 00:00:00,
#> 2018.04.18 00:00:00, 2018.04.20 00:00:00, 2018.04.29 00:00:00,
#> 2018.05.07 00:00:00, 2018.05.08 00:00:00, 2018.05.12 00:00:00,
#> 2018.05.13 00:00:00, 2018.05.19 00:00:00, 2018.05.24 00:00:00,
#> 2018.05.26 00:00:00, 2018.05.30 00:00:00, 2018.06.01 00:00:00,
#> 2018.06.03 00:00:00, 2018.06.04 00:00:00, 2018.06.05 00:00:00,
#> 2018.06.18 00:00:00, 2018.06.29 00:00:00, 2018.07.01 00:00:00,
#> 2018.07.02 00:00:00, 2018.07.11 00:00:00, 2018.07.13 00:00:00,
#> 2018.07.18 00:00:00, 2018.07.20 00:00:00, 2018.07.26 00:00:00,
#> 2018.08.02 00:00:00, 2018.08.04 00:00:00, 2018.08.07 00:00:00,
#> 2018.08.10 00:00:00, 2018.08.11 00:00:00, 2018.08.12 00:00:00,
#> 2018.08.13 00:00:00, 2018.08.17 00:00:00, 2018.08.19 00:00:00,
#> 2018.08.23 00:00:00, 2018.08.27 00:00:00, 2018.08.30 00:00:00,
#> 2018.09.02 00:00:00, 2018.09.04 00:00:00, 2018.09.08 00:00:00,
#> 2018.09.11 00:00:00, 2018.09.14 00:00:00, 2018.09.16 00:00:00,
#> 2018.10.02 00:00:00, 2018.10.03 00:00:00, 2018.10.04 00:00:00,
#> 2018.10.09 00:00:00, 2018.10.13 00:00:00, 2018.10.14 00:00:00,
#> 2018.10.17 00:00:00, 2018.10.18 00:00:00, 2018.10.23 00:00:00,
#> 2018.10.24 00:00:00, 2018.10.25 00:00:00, 2018.11.09 00:00:00,
#> 2018.11.15 00:00:00, 2018.11.17 00:00:00, 2018.11.22 00:00:00,
#> 2018.11.27 00:00:00, 2018.11.30 00:00:00, 2018.12.16 00:00:00,
#> 2018.12.29 00:00:00, 2019.01.11 00:00:00, 2019.01.12 00:00:00,
#> 2019.01.13 00:00:00, 2019.01.18 00:00:00, 2019.01.22 00:00:00,
#> 2019.01.24 00:00:00, 2019.02.04 00:00:00, 2019.02.05 00:00:00,
#> 2019.02.08 00:00:00, 2019.02.09 00:00:00, 2019.02.15 00:00:00,
#> 2019.02.16 00:00:00, 2019.02.19 00:00:00, 2019.03.04 00:00:00,
#> 2019.03.12 00:00:00, 2019.03.18 00:00:00, 2019.03.22 00:00:00,
#> 2019.03.26 00:00:00, 2019.03.31 00:00:00, 2019.04.07 00:00:00,
#> 2019.04.19 00:00:00, 2019.05.15 00:00:00, 2019.05.17 00:00:00,
#> 2019.05.18 00:00:00, 2019.05.23 00:00:00, 2019.05.24 00:00:00,
#> 2019.05.25 00:00:00, 2019.06.24 00:00:00, 2019.07.08 00:00:00,
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#> clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'><text x='1.00'
#> y='30.18' style='font-size: 8.00px; font-family: "Arial";'
#> textLength='52.52px' lengthAdjust='spacingAndGlyphs'>222 categories
#> <td headers="n_missing" class="gt_row gt_right gt_striped">0.4%Logical Relationship Checks
# Cross-tabulation to check logical relationships
crosstable(
data = histopathology,
vars = "Death",
group = "Mortality5yr"
)
#>
#> CROSS TABLE
#>
#> character(0)
#>
#> character(0)
#>
#> <div class="figure" id="tbl3">
#> #tbl3 body .figbody {
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#> font-size: 66%;
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#>
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#> margin-right: auto;
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#> }
#>
#> #tbl3 .figbody {
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#> }
#>
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#> }
#>
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#> {
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#> !important;}
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#> #tbl3 .header td {text-align: center;}
#>
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#> #tbl3 .subheader td { text-align: center; /* border-bottom: 1pt solid
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#>
#> #tbl3 .subheader em {font-style: normal;}
#>
#> #tbl3 tbody .variable {
#> float: left;
#> text-align: left;
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#>
#> #tbl3 .units {
#> float: right;
#> font-size: x-small;
#> text-align: right;
#> padding-left: 1em;
#> vertical-align: text-bottom; /* FIXME why doesn't this work */
#> }
#>
#> #tbl3 td .align{
#> display: inline-block;
#> margin: 0 auto;
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#> text-align: left;
#> }
#>
#> #tbl3 .statistics {font-style: italic;}
#> #tbl3 .statistics {padding-right: 0.5em;}
#> <div class="caption">Cross Table for Dependent mortality5yr<div
#> class="figbody"><table class="tangram">
#> <th class="header even tg-label"><span class="variable"><th
#> class="header even tg-label"><span class="variable">N<th class="header
#> even tg-label"><span class="variable">Alive<th class="header even
#> tg-label"><span class="variable">Dead<th class="header even
#> tg-label"><span class="variable">Test Statistic<tr
#> class="subheaderrow"><th class="subheader header even tg-label"><span
#> class="variable"><th class="subheader header even tg-label"><span
#> class="variable"><th class="subheader header even data N"><span
#> class="N">(N=80)<th class="subheader header even data N"><span
#> class="N">(N=169)<th class="subheader header even tg-label"><span
#> class="variable">
#> <td class="header odd tg-label"><span class="variable">Death :
#> YANLIŞ<td class="odd data N"><span class="N">249<td class="odd">1.0
#> <span class="fraction"><span class="numerator">80/<span
#> class="denominator">80<td class="odd">0.0 <span
#> class="fraction"><span class="numerator"> 0/<span
#> class="denominator">169<td class="statistics odd">Χ<span
#> class="supsub">2<br/>1=249.00, P<0.012
#> <div class="footnote">N is the number of non-missing value.
#> 1Kruskal-Wallis. 2Pearson. 3Wilcoxon.
# Outcome consistency check
crosstable(
data = histopathology,
vars = "Outcome",
group = "Death"
)
#>
#> CROSS TABLE
#>
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#>
#> character(0)
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#> !important;}
#>
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#>
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#> black; */ }
#>
#> #tbl3 .subheader em {font-style: normal;}
#>
#> #tbl3 tbody .variable {
#> float: left;
#> text-align: left;
#> padding-left: 0.5em;
#> }
#>
#> #tbl3 .units {
#> float: right;
#> font-size: x-small;
#> text-align: right;
#> padding-left: 1em;
#> vertical-align: text-bottom; /* FIXME why doesn't this work */
#> }
#>
#> #tbl3 td .align{
#> display: inline-block;
#> margin: 0 auto;
#> }
#>
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#> }
#>
#> #tbl3 .statistics {font-style: italic;}
#> #tbl3 .statistics {padding-right: 0.5em;}
#> <div class="caption">Cross Table for Dependent death<div
#> class="figbody"><table class="tangram">
#> <th class="header even tg-label"><span class="variable"><th
#> class="header even tg-label"><span class="variable">N<th class="header
#> even tg-label"><span class="variable">DOĞRU<th class="header even
#> tg-label"><span class="variable">YANLIŞ<th class="header even
#> tg-label"><span class="variable">Test Statistic<tr
#> class="subheaderrow"><th class="subheader header even tg-label"><span
#> class="variable"><th class="subheader header even tg-label"><span
#> class="variable"><th class="subheader header even data N"><span
#> class="N">(N=169)<th class="subheader header even data N"><span
#> class="N">(N=80)<th class="subheader header even tg-label"><span
#> class="variable">
#> <td class="header odd tg-label"><span class="variable">Outcome<td
#> class="odd data N"><span class="N">249<td class="odd">1.0 1.0 1.0<td
#> class="odd">0.0 0.0 0.0<td class="statistics
#> odd">F1,247=1112389107960512256.00, P<0.013
#> <div class="footnote">N is the number of non-missing value.
#> 1Kruskal-Wallis. 2Pearson. 3Wilcoxon.Grade-Stage Relationship Assessment
# Check pathological grade vs stage relationships
crosstable(
data = histopathology,
vars = "Grade",
group = "TStage"
)
#>
#> CROSS TABLE
#>
#> character(0)
#>
#> character(0)
#>
#> <div class="figure" id="tbl3">
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#> Sans", "DejaVu Sans", "Bitstream Vera Sans", "Liberation Sans",
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#> font-size: 66%;
#> -webkit-print-color-adjust: exact;
#> }
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#> margin-right: auto;
#> }
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#> text-indent: 2em;
#> padding: 0.5em 0 0.5em 0;
#> font-weight: bold;
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#> border: 1px solid black;
#> }
#>
#> #tbl3 .figbody {
#> text-align: center;
#> border: 1px solid black
#> }
#>
#> #tbl3 .figbody table {
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#>
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#> }
#>
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#> {
#> font-weight: bold;
#> }
#>
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#> !important;}
#>
#> #tbl3 .header td {text-align: center;}
#>
#> #tbl3 .subheader { font-size: smaller; }
#> #tbl3 .subheader td { text-align: center; /* border-bottom: 1pt solid
#> black; */ }
#>
#> #tbl3 .subheader em {font-style: normal;}
#>
#> #tbl3 tbody .variable {
#> float: left;
#> text-align: left;
#> padding-left: 0.5em;
#> }
#>
#> #tbl3 .units {
#> float: right;
#> font-size: x-small;
#> text-align: right;
#> padding-left: 1em;
#> vertical-align: text-bottom; /* FIXME why doesn't this work */
#> }
#>
#> #tbl3 td .align{
#> display: inline-block;
#> margin: 0 auto;
#> }
#>
#> #tbl3 .nobr {
#> white-space: nowrap;
#> }
#> #tbl3 .supsub {
#> display: inline-block;
#> margin: -9em 0;
#> vertical-align: -0.55em;
#> line-height: 1.35em;
#> font-size: 70%;
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#> }
#>
#> #tbl3 .statistics {font-style: italic;}
#> #tbl3 .statistics {padding-right: 0.5em;}
#> <div class="caption">Cross Table for Dependent t_stage<div
#> class="figbody"><table class="tangram">
#> <th class="header even tg-label"><span class="variable"><th
#> class="header even tg-label"><span class="variable">N<th class="header
#> even tg-label"><span class="variable">1<th class="header even
#> tg-label"><span class="variable">2<th class="header even
#> tg-label"><span class="variable">3<th class="header even
#> tg-label"><span class="variable">4<th class="header even
#> tg-label"><span class="variable">Test Statistic<tr
#> class="subheaderrow"><th class="subheader header even tg-label"><span
#> class="variable"><th class="subheader header even tg-label"><span
#> class="variable"><th class="subheader header even data N"><span
#> class="N">(N=24)<th class="subheader header even data N"><span
#> class="N">(N=51)<th class="subheader header even data N"><span
#> class="N">(N=63)<th class="subheader header even data N"><span
#> class="N">(N=111)<th class="subheader header even tg-label"><span
#> class="variable">
#> <td class="header odd tg-label"><span class="variable">Grade<td
#> class="odd data N"><span class="N">249<td class="odd">1.4 2.0 3.0<td
#> class="odd">1.0 2.0 3.0<td class="odd">1.0 2.0 3.0<td class="odd">1.0
#> 2.0 3.0<td class="statistics odd">F3,244=0.51, P=0.681
#> <div class="footnote">N is the number of non-missing value.
#> 1Kruskal-Wallis. 2Pearson. 3Wilcoxon.Phase 4: Benford’s Law Analysis
Benford’s law states that in naturally occurring datasets, the leading digit 1 appears about 30.1% of the time, digit 2 about 17.6%, and so on. Deviation from this pattern may indicate data fabrication.
Measurement A Analysis
# Benford's law analysis for Measurement A
benford(
data = histopathology,
var = "MeasurementA"
)
#>
#> BENFORD ANALYSIS
#>
#> See
#> <a href = 'https://github.com/carloscinelli/benford.analysis'>Package
#> documentation for interpratation.
#>
#> Benford object:
#>
#> Data: var
#> Number of observations used = 135
#> Number of obs. for second order = 134
#> First digits analysed = 2
#>
#> Mantissa:
#>
#> Statistic Value
#> Mean 0.506
#> Var 0.108
#> Ex.Kurtosis -1.514
#> Skewness -0.037
#>
#>
#> The 5 largest deviations:
#>
#> digits absolute.diff
#> 1 52 4.88
#> 2 17 4.65
#> 3 66 4.12
#> 4 13 3.66
#> 5 16 3.55
#>
#> Stats:
#>
#> Pearson's Chi-squared test
#>
#> data: var
#> X-squared = 137.97, df = 89, p-value = 0.0006808
#>
#>
#> Mantissa Arc Test
#>
#> data: var
#> L2 = 0.058715, df = 2, p-value = 0.000361
#>
#> Mean Absolute Deviation (MAD): 0.008005873
#> MAD Conformity - Nigrini (2012): Nonconformity
#> Distortion Factor: NaN
#>
#> Remember: Real data will never conform perfectly to Benford's Law. You should not focus on p-values! MeasurementA
#> <num>
#> 1: 0.0176978
#> 2: 1.7391655
#> 3: 0.5256537
#> 4: 1.7557878
#> 5: 0.5239132
#> 6: 0.1729459
#> 7: 0.1769035
#> 8: 0.5289038
#> 9: 0.5236107
#> 10: 1.7691411
#> 11: 0.5241260
#> 12: 0.5263020
#> 13: 1.7349714
#> 14: 0.1749262
#> $xlog
#> [1] FALSE
#>
#> $ylog
#> [1] FALSE
#>
#> $adj
#> [1] 0.5
#>
#> $ann
#> [1] TRUE
#>
#> $ask
#> [1] FALSE
#>
#> $bg
#> [1] "transparent"
#>
#> $bty
#> [1] "o"
#>
#> $cex
#> [1] 0.66
#>
#> $cex.axis
#> [1] 1
#>
#> $cex.lab
#> [1] 1
#>
#> $cex.main
#> [1] 1.2
#>
#> $cex.sub
#> [1] 1
#>
#> $col
#> [1] "black"
#>
#> $col.axis
#> [1] "black"
#>
#> $col.lab
#> [1] "black"
#>
#> $col.main
#> [1] "black"
#>
#> $col.sub
#> [1] "black"
#>
#> $crt
#> [1] 0
#>
#> $err
#> [1] 0
#>
#> $family
#> [1] ""
#>
#> $fg
#> [1] "black"
#>
#> $fig
#> [1] 0.6666667 1.0000000 0.0000000 0.5000000
#>
#> $fin
#> [1] 8 6
#>
#> $font
#> [1] 1
#>
#> $font.axis
#> [1] 1
#>
#> $font.lab
#> [1] 1
#>
#> $font.main
#> [1] 2
#>
#> $font.sub
#> [1] 1
#>
#> $lab
#> [1] 5 5 7
#>
#> $las
#> [1] 0
#>
#> $lend
#> [1] "round"
#>
#> $lheight
#> [1] 1
#>
#> $ljoin
#> [1] "round"
#>
#> $lmitre
#> [1] 10
#>
#> $lty
#> [1] "solid"
#>
#> $lwd
#> [1] 1
#>
#> $mai
#> [1] 1.02 0.82 0.82 0.42
#>
#> $mar
#> [1] 5.1 4.1 4.1 2.1
#>
#> $mex
#> [1] 1
#>
#> $mfcol
#> [1] 1 1
#>
#> $mfg
#> [1] 1 1 1 1
#>
#> $mfrow
#> [1] 1 1
#>
#> $mgp
#> [1] 3 1 0
#>
#> $mkh
#> [1] 0.001
#>
#> $new
#> [1] TRUE
#>
#> $oma
#> [1] 0 0 0 0
#>
#> $omd
#> [1] 0 1 0 1
#>
#> $omi
#> [1] 0 0 0 0
#>
#> $pch
#> [1] 1
#>
#> $pin
#> [1] 6.76 4.16
#>
#> $plt
#> [1] 0.0775000 0.9225000 0.1533333 0.8466667
#>
#> $ps
#> [1] 12
#>
#> $pty
#> [1] "m"
#>
#> $smo
#> [1] 1
#>
#> $srt
#> [1] 0
#>
#> $tck
#> [1] NA
#>
#> $tcl
#> [1] -0.5
#>
#> $usr
#> [1] 0.568 1.432 0.568 1.432
#>
#> $xaxp
#> [1] 0 1 5
#>
#> $xaxs
#> [1] "r"
#>
#> $xaxt
#> [1] "s"
#>
#> $xpd
#> [1] FALSE
#>
#> $yaxp
#> [1] 0 1 5
#>
#> $yaxs
#> [1] "r"
#>
#> $yaxt
#> [1] "s"
#>
#> $ylbias
#> [1] 0.2Measurement B Analysis
# Benford's law analysis for Measurement B
benford(
data = histopathology,
var = "MeasurementB"
)
#>
#> BENFORD ANALYSIS
#>
#> See
#> <a href = 'https://github.com/carloscinelli/benford.analysis'>Package
#> documentation for interpratation.
#>
#> Benford object:
#>
#> Data: var
#> Number of observations used = 238
#> Number of obs. for second order = 237
#> First digits analysed = 2
#>
#> Mantissa:
#>
#> Statistic Value
#> Mean 0.45
#> Var 0.12
#> Ex.Kurtosis -1.58
#> Skewness 0.25
#>
#>
#> The 5 largest deviations:
#>
#> digits absolute.diff
#> 1 10 10.15
#> 2 12 9.73
#> 3 14 8.87
#> 4 11 8.01
#> 5 13 7.34
#>
#> Stats:
#>
#> Pearson's Chi-squared test
#>
#> data: var
#> X-squared = 157.07, df = 89, p-value = 1.157e-05
#>
#>
#> Mantissa Arc Test
#>
#> data: var
#> L2 = 0.15767, df = 2, p-value < 2.2e-16
#>
#> Mean Absolute Deviation (MAD): 0.007372993
#> MAD Conformity - Nigrini (2012): Nonconformity
#> Distortion Factor: NaN
#>
#> Remember: Real data will never conform perfectly to Benford's Law. You should not focus on p-values! MeasurementB
#> <num>
#> 1: 1.0190683
#> 2: 1.0097142
#> 3: 1.2020945
#> 4: 0.1069895
#> 5: 1.2977401
#> 6: 1.2517520
#> 7: 1.0263980
#> 8: 1.2205962
#> 9: 1.0371370
#> 10: 0.1045843
#> 11: 1.0848132
#> 12: 1.2214183
#> 13: 1.2689838
#> 14: 1.0163456
#> 15: 1.0066332
#> 16: 1.2965716
#> 17: 1.2178483
#> 18: 1.2748898
#> 19: 1.2152822
#> 20: 1.2102449
#> 21: 1.0574988
#> 22: 1.0725410
#> 23: 1.0075724
#> 24: 1.0321501
#> 25: 1.0183457
#> 26: 1.2888474
#> 27: 1.0625906
#> 28: 1.2003812
#> 29: 1.2789038
#> 30: 1.2528495
#> 31: 1.0202662
#> 32: 1.0519990
#> 33: 1.0276341
#> 34: 1.0836331
#> 35: 1.2221656
#> 36: 1.0199442
#> 37: 1.2825127
#> 38: 1.2066874
#> MeasurementB
#> $xlog
#> [1] FALSE
#>
#> $ylog
#> [1] FALSE
#>
#> $adj
#> [1] 0.5
#>
#> $ann
#> [1] TRUE
#>
#> $ask
#> [1] FALSE
#>
#> $bg
#> [1] "transparent"
#>
#> $bty
#> [1] "o"
#>
#> $cex
#> [1] 0.66
#>
#> $cex.axis
#> [1] 1
#>
#> $cex.lab
#> [1] 1
#>
#> $cex.main
#> [1] 1.2
#>
#> $cex.sub
#> [1] 1
#>
#> $col
#> [1] "black"
#>
#> $col.axis
#> [1] "black"
#>
#> $col.lab
#> [1] "black"
#>
#> $col.main
#> [1] "black"
#>
#> $col.sub
#> [1] "black"
#>
#> $crt
#> [1] 0
#>
#> $err
#> [1] 0
#>
#> $family
#> [1] ""
#>
#> $fg
#> [1] "black"
#>
#> $fig
#> [1] 0.6666667 1.0000000 0.0000000 0.5000000
#>
#> $fin
#> [1] 8 6
#>
#> $font
#> [1] 1
#>
#> $font.axis
#> [1] 1
#>
#> $font.lab
#> [1] 1
#>
#> $font.main
#> [1] 2
#>
#> $font.sub
#> [1] 1
#>
#> $lab
#> [1] 5 5 7
#>
#> $las
#> [1] 0
#>
#> $lend
#> [1] "round"
#>
#> $lheight
#> [1] 1
#>
#> $ljoin
#> [1] "round"
#>
#> $lmitre
#> [1] 10
#>
#> $lty
#> [1] "solid"
#>
#> $lwd
#> [1] 1
#>
#> $mai
#> [1] 1.02 0.82 0.82 0.42
#>
#> $mar
#> [1] 5.1 4.1 4.1 2.1
#>
#> $mex
#> [1] 1
#>
#> $mfcol
#> [1] 1 1
#>
#> $mfg
#> [1] 1 1 1 1
#>
#> $mfrow
#> [1] 1 1
#>
#> $mgp
#> [1] 3 1 0
#>
#> $mkh
#> [1] 0.001
#>
#> $new
#> [1] TRUE
#>
#> $oma
#> [1] 0 0 0 0
#>
#> $omd
#> [1] 0 1 0 1
#>
#> $omi
#> [1] 0 0 0 0
#>
#> $pch
#> [1] 1
#>
#> $pin
#> [1] 6.76 4.16
#>
#> $plt
#> [1] 0.0775000 0.9225000 0.1533333 0.8466667
#>
#> $ps
#> [1] 12
#>
#> $pty
#> [1] "m"
#>
#> $smo
#> [1] 1
#>
#> $srt
#> [1] 0
#>
#> $tck
#> [1] NA
#>
#> $tcl
#> [1] -0.5
#>
#> $usr
#> [1] 0.568 1.432 0.568 1.432
#>
#> $xaxp
#> [1] 0 1 5
#>
#> $xaxs
#> [1] "r"
#>
#> $xaxt
#> [1] "s"
#>
#> $xpd
#> [1] FALSE
#>
#> $yaxp
#> [1] 0 1 5
#>
#> $yaxs
#> [1] "r"
#>
#> $yaxt
#> [1] "s"
#>
#> $ylbias
#> [1] 0.2Overall Time Analysis
# Benford's law analysis for follow-up time
benford(
data = histopathology,
var = "OverallTime"
)
#>
#> BENFORD ANALYSIS
#>
#> See
#> <a href = 'https://github.com/carloscinelli/benford.analysis'>Package
#> documentation for interpratation.
#>
#> Benford object:
#>
#> Data: var
#> Number of observations used = 248
#> Number of obs. for second order = 157
#> First digits analysed = 2
#>
#> Mantissa:
#>
#> Statistic Value
#> Mean 0.569
#> Var 0.091
#> Ex.Kurtosis -1.023
#> Skewness -0.389
#>
#>
#> The 5 largest deviations:
#>
#> digits absolute.diff
#> 1 12 7.62
#> 2 13 6.98
#> 3 32 6.69
#> 4 16 6.53
#> 5 11 5.63
#>
#> Stats:
#>
#> Pearson's Chi-squared test
#>
#> data: var
#> X-squared = 149.57, df = 89, p-value = 6.194e-05
#>
#>
#> Mantissa Arc Test
#>
#> data: var
#> L2 = 0.033636, df = 2, p-value = 0.0002383
#>
#> Mean Absolute Deviation (MAD): 0.006604894
#> MAD Conformity - Nigrini (2012): Nonconformity
#> Distortion Factor: -34.7503
#>
#> Remember: Real data will never conform perfectly to Benford's Law. You should not focus on p-values! OverallTime
#> <num>
#> 1: 13.4
#> 2: 12.9
#> $xlog
#> [1] FALSE
#>
#> $ylog
#> [1] FALSE
#>
#> $adj
#> [1] 0.5
#>
#> $ann
#> [1] TRUE
#>
#> $ask
#> [1] FALSE
#>
#> $bg
#> [1] "transparent"
#>
#> $bty
#> [1] "o"
#>
#> $cex
#> [1] 0.66
#>
#> $cex.axis
#> [1] 1
#>
#> $cex.lab
#> [1] 1
#>
#> $cex.main
#> [1] 1.2
#>
#> $cex.sub
#> [1] 1
#>
#> $col
#> [1] "black"
#>
#> $col.axis
#> [1] "black"
#>
#> $col.lab
#> [1] "black"
#>
#> $col.main
#> [1] "black"
#>
#> $col.sub
#> [1] "black"
#>
#> $crt
#> [1] 0
#>
#> $err
#> [1] 0
#>
#> $family
#> [1] ""
#>
#> $fg
#> [1] "black"
#>
#> $fig
#> [1] 0.6666667 1.0000000 0.0000000 0.5000000
#>
#> $fin
#> [1] 8 6
#>
#> $font
#> [1] 1
#>
#> $font.axis
#> [1] 1
#>
#> $font.lab
#> [1] 1
#>
#> $font.main
#> [1] 2
#>
#> $font.sub
#> [1] 1
#>
#> $lab
#> [1] 5 5 7
#>
#> $las
#> [1] 0
#>
#> $lend
#> [1] "round"
#>
#> $lheight
#> [1] 1
#>
#> $ljoin
#> [1] "round"
#>
#> $lmitre
#> [1] 10
#>
#> $lty
#> [1] "solid"
#>
#> $lwd
#> [1] 1
#>
#> $mai
#> [1] 1.02 0.82 0.82 0.42
#>
#> $mar
#> [1] 5.1 4.1 4.1 2.1
#>
#> $mex
#> [1] 1
#>
#> $mfcol
#> [1] 1 1
#>
#> $mfg
#> [1] 1 1 1 1
#>
#> $mfrow
#> [1] 1 1
#>
#> $mgp
#> [1] 3 1 0
#>
#> $mkh
#> [1] 0.001
#>
#> $new
#> [1] TRUE
#>
#> $oma
#> [1] 0 0 0 0
#>
#> $omd
#> [1] 0 1 0 1
#>
#> $omi
#> [1] 0 0 0 0
#>
#> $pch
#> [1] 1
#>
#> $pin
#> [1] 6.76 4.16
#>
#> $plt
#> [1] 0.0775000 0.9225000 0.1533333 0.8466667
#>
#> $ps
#> [1] 12
#>
#> $pty
#> [1] "m"
#>
#> $smo
#> [1] 1
#>
#> $srt
#> [1] 0
#>
#> $tck
#> [1] NA
#>
#> $tcl
#> [1] -0.5
#>
#> $usr
#> [1] 0.568 1.432 0.568 1.432
#>
#> $xaxp
#> [1] 0 1 5
#>
#> $xaxs
#> [1] "r"
#>
#> $xaxt
#> [1] "s"
#>
#> $xpd
#> [1] FALSE
#>
#> $yaxp
#> [1] 0 1 5
#>
#> $yaxs
#> [1] "r"
#>
#> $yaxt
#> [1] "s"
#>
#> $ylbias
#> [1] 0.2Phase 5: Inter-rater Reliability Assessment
Rater Agreement Analysis
# Analyze inter-rater reliability data
rater_vars <- histopathology %>%
select(starts_with("Rater")) %>%
names()
if(length(rater_vars) > 0) {
# Binary rater variables
binary_raters <- rater_vars[grepl("[0-9]$", rater_vars)]
if(length(binary_raters) > 0) {
tryCatch({
reportcat(
data = histopathology,
vars = binary_raters[1:min(3, length(binary_raters))]
)
}, error = function(e) {
cat("Error with binary raters reportcat:", conditionMessage(e), "\n")
cat("Binary rater variables:", paste(binary_raters, collapse = ", "), "\n")
})
} else {
cat("No binary rater variables found (ending with digits)\n")
}
# Categorical rater variables
categorical_raters <- rater_vars[grepl("[A-Z]$", rater_vars)]
if(length(categorical_raters) > 0) {
tryCatch({
reportcat(
data = histopathology,
vars = categorical_raters
)
}, error = function(e) {
cat("Error with categorical raters reportcat:", conditionMessage(e), "\n")
cat("Categorical rater variables:", paste(categorical_raters, collapse = ", "), "\n")
})
} else {
cat("No categorical rater variables found (ending with letters)\n")
}
} else {
cat("No rater variables found in dataset\n")
}
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#> <tr><td headers="type" class="gt_row gt_left"><svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:20px;width:20px;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#4e79a7;overflow:visible;position:relative;"><path d="M40 48C26.7 48 16 58.7 16 72v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V72c0-13.3-10.7-24-24-24H40zM192 64c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zM16 232v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V232c0-13.3-10.7-24-24-24H40c-13.3 0-24 10.7-24 24zM40 368c-13.3 0-24 10.7-24 24v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V392c0-13.3-10.7-24-24-24H40z"/></svg></td>
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#> <summary style='font-weight: bold !important;'>Rater 1</summary>
#> 1 and 0
#> </details></div></td>
#> <td headers="value" class="gt_row gt_center"><?xml version='1.0' encoding='UTF-8' ?><svg xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' width='141.73pt' height='34.02pt' viewBox='0 0 141.73 34.02'><g class='svglite'><defs> <style type='text/css'><![CDATA[ .svglite line, .svglite polyline, .svglite polygon, .svglite path, .svglite rect, .svglite circle { fill: none; stroke: #000000; stroke-linecap: round; stroke-linejoin: round; stroke-miterlimit: 10.00; } .svglite text { white-space: pre; } .svglite g.glyphgroup path { fill: inherit; stroke: none; } ]]></style></defs><rect width='100%' height='100%' style='stroke: none; fill: none;'/><defs> <clipPath id='cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg=='> <rect x='0.00' y='0.00' width='141.73' height='34.02' /> </clipPath></defs><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'></g><defs> <clipPath id='cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ=='> <rect x='1.00' y='2.99' width='139.74' height='20.62' /> </clipPath></defs><g clip-path='url(#cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ==)'><rect x='95.84' y='3.93' width='44.90' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #DDEAF7;' /><rect x='1.00' y='3.93' width='94.84' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #3181BD;' /></g><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'><text x='1.00' y='30.18' style='font-size: 8.00px; font-family: "Arial";' textLength='43.61px' lengthAdjust='spacingAndGlyphs'>2 categories</text></g></g></svg></td>
#> <td headers="n_missing" class="gt_row gt_right">0.4%</td></tr>
#> <tr><td headers="type" class="gt_row gt_left gt_striped"><svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:20px;width:20px;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#4e79a7;overflow:visible;position:relative;"><path d="M40 48C26.7 48 16 58.7 16 72v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V72c0-13.3-10.7-24-24-24H40zM192 64c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zM16 232v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V232c0-13.3-10.7-24-24-24H40c-13.3 0-24 10.7-24 24zM40 368c-13.3 0-24 10.7-24 24v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V392c0-13.3-10.7-24-24-24H40z"/></svg></td>
#> <td headers="name" class="gt_row gt_left gt_striped" style="font-weight: bold;"><div style='max-width: 150px;'>
#> <details style='font-weight: normal !important;'>
#> <summary style='font-weight: bold !important;'>Rater 2</summary>
#> 1 and 0
#> </details></div></td>
#> <td headers="value" class="gt_row gt_center gt_striped"><?xml version='1.0' encoding='UTF-8' ?><svg xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' width='141.73pt' height='34.02pt' viewBox='0 0 141.73 34.02'><g class='svglite'><defs> <style type='text/css'><![CDATA[ .svglite line, .svglite polyline, .svglite polygon, .svglite path, .svglite rect, .svglite circle { fill: none; stroke: #000000; stroke-linecap: round; stroke-linejoin: round; stroke-miterlimit: 10.00; } .svglite text { white-space: pre; } .svglite g.glyphgroup path { fill: inherit; stroke: none; } ]]></style></defs><rect width='100%' height='100%' style='stroke: none; fill: none;'/><defs> <clipPath id='cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg=='> <rect x='0.00' y='0.00' width='141.73' height='34.02' /> </clipPath></defs><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'></g><defs> <clipPath id='cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ=='> <rect x='1.00' y='2.99' width='139.74' height='20.62' /> </clipPath></defs><g clip-path='url(#cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ==)'><rect x='90.99' y='3.93' width='49.75' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #DDEAF7;' /><rect x='1.00' y='3.93' width='89.99' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #3181BD;' /></g><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'><text x='1.00' y='30.18' style='font-size: 8.00px; font-family: "Arial";' textLength='43.61px' lengthAdjust='spacingAndGlyphs'>2 categories</text></g></g></svg></td>
#> <td headers="n_missing" class="gt_row gt_right gt_striped">0.0%</td></tr>
#> <tr><td headers="type" class="gt_row gt_left"><svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:20px;width:20px;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#4e79a7;overflow:visible;position:relative;"><path d="M40 48C26.7 48 16 58.7 16 72v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V72c0-13.3-10.7-24-24-24H40zM192 64c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zM16 232v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V232c0-13.3-10.7-24-24-24H40c-13.3 0-24 10.7-24 24zM40 368c-13.3 0-24 10.7-24 24v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V392c0-13.3-10.7-24-24-24H40z"/></svg></td>
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#> <details style='font-weight: normal !important;'>
#> <summary style='font-weight: bold !important;'>Rater 3</summary>
#> 1 and 0
#> </details></div></td>
#> <td headers="value" class="gt_row gt_center"><?xml version='1.0' encoding='UTF-8' ?><svg xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' width='141.73pt' height='34.02pt' viewBox='0 0 141.73 34.02'><g class='svglite'><defs> <style type='text/css'><![CDATA[ .svglite line, .svglite polyline, .svglite polygon, .svglite path, .svglite rect, .svglite circle { fill: none; stroke: #000000; stroke-linecap: round; stroke-linejoin: round; stroke-miterlimit: 10.00; } .svglite text { white-space: pre; } .svglite g.glyphgroup path { fill: inherit; stroke: none; } ]]></style></defs><rect width='100%' height='100%' style='stroke: none; fill: none;'/><defs> <clipPath id='cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg=='> <rect x='0.00' y='0.00' width='141.73' height='34.02' /> </clipPath></defs><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'></g><defs> <clipPath id='cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ=='> <rect x='1.00' y='2.99' width='139.74' height='20.62' /> </clipPath></defs><g clip-path='url(#cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ==)'><rect x='83.16' y='3.93' width='57.57' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #DDEAF7;' /><rect x='1.00' y='3.93' width='82.17' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #3181BD;' /></g><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'><text x='1.00' y='30.18' style='font-size: 8.00px; font-family: "Arial";' textLength='43.61px' lengthAdjust='spacingAndGlyphs'>2 categories</text></g></g></svg></td>
#> <td headers="n_missing" class="gt_row gt_right">0.0%</td></tr>
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#> Error with categorical raters reportcat: 'names' attribute [38] must be the same length as the vector [0]
#> Categorical rater variables: Rater A, Rater BAgreement Cross-tabulation
# Cross-tabulate raters for agreement assessment
if(length(rater_vars) >= 2) {
crosstable(
data = histopathology,
vars = rater_vars[1],
group = rater_vars[2]
)
}
#>
#> CROSS TABLE
#>
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#> <div class="caption">Cross Table for Dependent rater_2<div
#> class="figbody"><table class="tangram">
#> <th class="header even tg-label"><span class="variable"><th
#> class="header even tg-label"><span class="variable">N<th class="header
#> even tg-label"><span class="variable">0<th class="header even
#> tg-label"><span class="variable">1<th class="header even
#> tg-label"><span class="variable">Test Statistic<tr
#> class="subheaderrow"><th class="subheader header even tg-label"><span
#> class="variable"><th class="subheader header even tg-label"><span
#> class="variable"><th class="subheader header even data N"><span
#> class="N">(N=89)<th class="subheader header even data N"><span
#> class="N">(N=161)<th class="subheader header even tg-label"><span
#> class="variable">
#> <td class="header odd tg-label"><span class="variable">Rater 1<td
#> class="odd data N"><span class="N">249<td class="odd">0.0 0.0 0.0<td
#> class="odd">1.0 1.0 1.0<td class="statistics odd">F1,247=517.22,
#> P<0.013
#> <div class="footnote">N is the number of non-missing value.
#> 1Kruskal-Wallis. 2Pearson. 3Wilcoxon.Phase 6: Outlier Detection
Statistical Outlier Analysis
# Identify potential outliers in continuous variables
outlier_analysis <- histopathology %>%
select(Age, OverallTime, MeasurementA, MeasurementB) %>%
summarise_all(
list(
Mean = ~round(mean(., na.rm = TRUE), 2),
SD = ~round(sd(., na.rm = TRUE), 2),
Q1 = ~round(quantile(., 0.25, na.rm = TRUE), 2),
Q3 = ~round(quantile(., 0.75, na.rm = TRUE), 2),
IQR = ~round(IQR(., na.rm = TRUE), 2),
Lower_Fence = ~round(quantile(., 0.25, na.rm = TRUE) - 1.5 * IQR(., na.rm = TRUE), 2),
Upper_Fence = ~round(quantile(., 0.75, na.rm = TRUE) + 1.5 * IQR(., na.rm = TRUE), 2)
)
) %>%
pivot_longer(everything(), names_to = "Stat", values_to = "Value") %>%
separate(Stat, into = c("Variable", "Statistic"), sep = "_", extra = "merge") %>%
pivot_wider(names_from = Statistic, values_from = Value)
kable(outlier_analysis,
caption = "Outlier Detection Statistics",
digits = 2)| Variable | Mean | SD | Q1 | Q3 | IQR | Lower_Fence | Upper_Fence |
|---|---|---|---|---|---|---|---|
| Age | 49.44 | 13.81 | 38.00 | 62.00 | 24.00 | 2.00 | 98.00 |
| OverallTime | 16.53 | 13.50 | 7.10 | 23.60 | 16.50 | -17.65 | 48.35 |
| MeasurementA | 0.07 | 0.87 | -0.53 | 0.68 | 1.21 | -2.34 | 2.50 |
| MeasurementB | 0.97 | 0.56 | 0.61 | 1.35 | 0.74 | -0.51 | 2.47 |
Extreme Value Assessment
# Count extreme values beyond fences
extreme_values <- histopathology %>%
summarise(
Age_Low_Outliers = sum(Age < (quantile(Age, 0.25, na.rm = TRUE) - 1.5 * IQR(Age, na.rm = TRUE)), na.rm = TRUE),
Age_High_Outliers = sum(Age > (quantile(Age, 0.75, na.rm = TRUE) + 1.5 * IQR(Age, na.rm = TRUE)), na.rm = TRUE),
Time_Low_Outliers = sum(OverallTime < (quantile(OverallTime, 0.25, na.rm = TRUE) - 1.5 * IQR(OverallTime, na.rm = TRUE)), na.rm = TRUE),
Time_High_Outliers = sum(OverallTime > (quantile(OverallTime, 0.75, na.rm = TRUE) + 1.5 * IQR(OverallTime, na.rm = TRUE)), na.rm = TRUE),
MeasA_Low_Outliers = sum(MeasurementA < (quantile(MeasurementA, 0.25, na.rm = TRUE) - 1.5 * IQR(MeasurementA, na.rm = TRUE)), na.rm = TRUE),
MeasA_High_Outliers = sum(MeasurementA > (quantile(MeasurementA, 0.75, na.rm = TRUE) + 1.5 * IQR(MeasurementA, na.rm = TRUE)), na.rm = TRUE)
) %>%
pivot_longer(everything(), names_to = "Variable_Type", values_to = "Outlier_Count") %>%
separate(Variable_Type, into = c("Variable", "Outlier_Type"), sep = "_", extra = "merge")
kable(extreme_values,
caption = "Extreme Value Counts by Variable")| Variable | Outlier_Type | Outlier_Count |
|---|---|---|
| Age | Low_Outliers | 0 |
| Age | High_Outliers | 0 |
| Time | Low_Outliers | 0 |
| Time | High_Outliers | 12 |
| MeasA | Low_Outliers | 1 |
| MeasA | High_Outliers | 0 |
Phase 7: Diagnostic Test Validation
Validity Assessment
# Analyze data validity flags
validity_vars <- histopathology %>%
select(contains("Valid")) %>%
names()
if(length(validity_vars) > 0) {
tryCatch({
reportcat(
data = histopathology,
vars = validity_vars[1]
)
}, error = function(e) {
cat("Error with validity variables reportcat:", conditionMessage(e), "\n")
cat("Validity variables:", paste(validity_vars, collapse = ", "), "\n")
})
} else {
cat("No validity variables found in dataset\n")
}
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#>
#> #cyvkgufivj .gt_caption {
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#>
#> #cyvkgufivj .gt_title {
#> color: #333333;
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#> padding-left: 5px;
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#>
#> #cyvkgufivj .gt_subtitle {
#> color: #333333;
#> font-size: 85%;
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#> padding-left: 5px;
#> padding-right: 5px;
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#>
#> #cyvkgufivj .gt_heading {
#> background-color: #FFFFFF;
#> text-align: left;
#> border-bottom-color: #FFFFFF;
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#> border-left-width: 1px;
#> border-left-color: #D3D3D3;
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#> border-right-width: 1px;
#> border-right-color: #D3D3D3;
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#>
#> #cyvkgufivj .gt_bottom_border {
#> border-bottom-style: solid;
#> border-bottom-width: 0px;
#> border-bottom-color: #D3D3D3;
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#>
#> #cyvkgufivj .gt_col_headings {
#> border-top-style: solid;
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#> border-bottom-width: 2px;
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#> border-right-color: #D3D3D3;
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#>
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#> color: #333333;
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#> padding-left: 5px;
#> padding-right: 5px;
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#>
#> #cyvkgufivj .gt_column_spanner_outer {
#> color: #333333;
#> background-color: #FFFFFF;
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#> #cyvkgufivj .gt_column_spanner_outer:first-child {
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#> #cyvkgufivj .gt_column_spanner_outer:last-child {
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#> #cyvkgufivj .gt_from_md > :last-child {
#> margin-bottom: 0;
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#> padding-right: 5px;
#> margin: 10px;
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#> border-left-width: 1px;
#> border-left-color: #D3D3D3;
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#> border-right-width: 1px;
#> border-right-color: #D3D3D3;
#> vertical-align: middle;
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#> }
#>
#> #cyvkgufivj .gt_stub {
#> color: #333333;
#> background-color: #FFFFFF;
#> font-size: 80%;
#> font-weight: bolder;
#> text-transform: uppercase;
#> border-right-style: solid;
#> border-right-width: 2px;
#> border-right-color: #D3D3D3;
#> padding-left: 5px;
#> padding-right: 5px;
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#>
#> #cyvkgufivj .gt_stub_row_group {
#> color: #333333;
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#> font-size: 100%;
#> font-weight: initial;
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#> border-right-style: solid;
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#> border-right-color: #D3D3D3;
#> padding-left: 5px;
#> padding-right: 5px;
#> vertical-align: top;
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#>
#> #cyvkgufivj .gt_row_group_first td {
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#> }
#>
#> #cyvkgufivj .gt_row_group_first th {
#> border-top-width: 2px;
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#>
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#> color: #333333;
#> background-color: #FFFFFF;
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#> padding-top: 8px;
#> padding-bottom: 8px;
#> padding-left: 5px;
#> padding-right: 5px;
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#>
#> #cyvkgufivj .gt_first_summary_row {
#> border-top-style: solid;
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#>
#> #cyvkgufivj .gt_first_summary_row.thick {
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#>
#> #cyvkgufivj .gt_last_summary_row {
#> padding-top: 8px;
#> padding-bottom: 8px;
#> padding-left: 5px;
#> padding-right: 5px;
#> border-bottom-style: solid;
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#>
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#> color: #333333;
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#>
#> #cyvkgufivj .gt_first_grand_summary_row {
#> padding-top: 8px;
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#>
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#>
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#>
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#> border-right-style: none;
#> border-right-width: 2px;
#> border-right-color: #D3D3D3;
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#>
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#> margin: 0px;
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#> padding-top: 4px;
#> padding-bottom: 4px;
#> padding-left: 5px;
#> padding-right: 5px;
#> }
#>
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#> color: #333333;
#> background-color: #FFFFFF;
#> border-bottom-style: none;
#> border-bottom-width: 2px;
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#> border-right-style: none;
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#> border-right-color: #D3D3D3;
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#> #cyvkgufivj .gt_sourcenote {
#> font-size: 12px;
#> padding-top: 4px;
#> padding-bottom: 4px;
#> padding-left: 5px;
#> padding-right: 5px;
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#> #cyvkgufivj .gt_left {
#> text-align: left;
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#>
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#> text-align: center;
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#>
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#>
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#> font-weight: normal;
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#> font-weight: bold;
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#> font-size: 75%;
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#> text-indent: 10px;
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#>
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#>
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#> text-indent: 20px;
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#>
#> #cyvkgufivj .gt_indent_5 {
#> text-indent: 25px;
#> }
#>
#> #cyvkgufivj .katex-display {
#> display: inline-flex !important;
#> margin-bottom: 0.75em !important;
#> }
#>
#> #cyvkgufivj div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
#> height: 0px !important;
#> }
#> </style>
#> <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
#> <thead>
#> <tr class="gt_heading">
#> <td colspan="4" class="gt_heading gt_title gt_font_normal" style>.</td>
#> </tr>
#> <tr class="gt_heading">
#> <td colspan="4" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>250 rows x 1 cols</td>
#> </tr>
#> <tr class="gt_col_headings">
#> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="type"></th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="name">Column</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="value">Plot Overview</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="n_missing">Missing</th>
#> </tr>
#> </thead>
#> <tbody class="gt_table_body">
#> <tr><td headers="type" class="gt_row gt_left"><svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:20px;width:20px;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#4e79a7;overflow:visible;position:relative;"><path d="M40 48C26.7 48 16 58.7 16 72v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V72c0-13.3-10.7-24-24-24H40zM192 64c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zM16 232v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V232c0-13.3-10.7-24-24-24H40c-13.3 0-24 10.7-24 24zM40 368c-13.3 0-24 10.7-24 24v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V392c0-13.3-10.7-24-24-24H40z"/></svg></td>
#> <td headers="name" class="gt_row gt_left" style="font-weight: bold;"><div style='max-width: 150px;'>
#> <details style='font-weight: normal !important;'>
#> <summary style='font-weight: bold !important;'>Valid</summary>
#> DOĞRU and YANLIŞ
#> </details></div></td>
#> <td headers="value" class="gt_row gt_center"><?xml version='1.0' encoding='UTF-8' ?><svg xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' width='141.73pt' height='34.02pt' viewBox='0 0 141.73 34.02'><g class='svglite'><defs> <style type='text/css'><![CDATA[ .svglite line, .svglite polyline, .svglite polygon, .svglite path, .svglite rect, .svglite circle { fill: none; stroke: #000000; stroke-linecap: round; stroke-linejoin: round; stroke-miterlimit: 10.00; } .svglite text { white-space: pre; } .svglite g.glyphgroup path { fill: inherit; stroke: none; } ]]></style></defs><rect width='100%' height='100%' style='stroke: none; fill: none;'/><defs> <clipPath id='cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg=='> <rect x='0.00' y='0.00' width='141.73' height='34.02' /> </clipPath></defs><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'></g><defs> <clipPath id='cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ=='> <rect x='1.00' y='2.99' width='139.74' height='20.62' /> </clipPath></defs><g clip-path='url(#cpMS4wMHwxNDAuNzR8Mi45OXwyMy42MQ==)'><rect x='72.83' y='3.93' width='67.91' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #DDEAF7;' /><rect x='1.00' y='3.93' width='71.83' height='18.75' style='stroke-width: 1.07; stroke: none; stroke-linecap: butt; stroke-linejoin: miter; fill: #3181BD;' /></g><g clip-path='url(#cpMC4wMHwxNDEuNzN8MC4wMHwzNC4wMg==)'><text x='1.00' y='30.18' style='font-size: 8.00px; font-family: "Arial";' textLength='43.61px' lengthAdjust='spacingAndGlyphs'>2 categories</text></g></g></svg></td>
#> <td headers="n_missing" class="gt_row gt_right">0.4%</td></tr>
#> </tbody>
#>
#>
#> </table>
#> </div>
#>
#> SUMMARY OF CATEGORICAL VARIABLES
#>
#> character(0)
#>
#> Valid has 250 observations and 2 levels.
#> DOĞRU: n = 128, 51.4% of valid cases.
#> YANLIŞ: n = 121, 48.6% of valid cases.
#> Missing values: 1.
#>
#> <table class="gt_table" data-quarto-disable-processing="false"
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#>
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#>
#> <tr class="gt_heading">
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#>
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#> <th class="gt_col_heading gt_columns_bottom_border gt_right"
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#> <details style='font-weight: normal !important;'>
#> <summary style='font-weight: bold !important;'>Valid
#> DOĞRU and YANLIŞ
#>
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#> <td headers="n_missing" class="gt_row gt_right">0.4%Phase 8: Data Quality Dashboard
Overall Quality Metrics
# Create comprehensive quality summary
quality_metrics <- data.frame(
Metric = c(
"Total Observations",
"Total Variables",
"Complete Cases",
"Variables with No Missing Data",
"Variables with <5% Missing",
"Variables with >20% Missing",
"Mean Completeness Rate"
),
Value = c(
nrow(histopathology),
ncol(histopathology),
sum(complete.cases(histopathology)),
sum(missing_summary$Missing_Count == 0),
sum(missing_summary$Missing_Percentage <= 5),
sum(missing_summary$Missing_Percentage > 20),
paste0(round(mean(100 - missing_summary$Missing_Percentage), 1), "%")
)
)
kable(quality_metrics,
caption = "Data Quality Dashboard")| Metric | Value |
|---|---|
| Total Observations | 250 |
| Total Variables | 38 |
| Complete Cases | 231 |
| Variables with No Missing Data | 15 |
| Variables with <5% Missing | 38 |
| Variables with >20% Missing | 0 |
| Mean Completeness Rate | 99.7% |
Quality Recommendations
# Generate quality recommendations
high_missing <- missing_summary %>%
filter(Missing_Percentage > 20) %>%
pull(Variable)
moderate_missing <- missing_summary %>%
filter(Missing_Percentage > 5 & Missing_Percentage <= 20) %>%
pull(Variable)
recommendations <- data.frame(
Priority = c("High", "Medium", "Low"),
Action = c(
ifelse(length(high_missing) > 0,
paste("Investigate high missing data in:", paste(high_missing, collapse = ", ")),
"No high-priority missing data issues"),
ifelse(length(moderate_missing) > 0,
paste("Consider imputation for:", paste(moderate_missing, collapse = ", ")),
"No moderate missing data issues"),
"Continue routine quality monitoring"
)
)
kable(recommendations,
caption = "Data Quality Recommendations")| Priority | Action |
|---|---|
| High | No high-priority missing data issues |
| Medium | No moderate missing data issues |
| Low | Continue routine quality monitoring |
Phase 9: Reporting Quality Issues
Missing Data Patterns
# Analyze missing data patterns
if(any(missing_summary$Missing_Count > 0)) {
# Create missing data profile
missing_profile <- histopathology %>%
summarise_all(~ifelse(is.na(.), "Missing", "Present")) %>%
pivot_longer(everything(), names_to = "Variable", values_to = "Status") %>%
count(Variable, Status) %>%
pivot_wider(names_from = Status, values_from = n, values_fill = 0) %>%
mutate(
Total = Missing + Present,
Missing_Rate = round(Missing / Total * 100, 1)
) %>%
filter(Missing > 0) %>%
arrange(desc(Missing_Rate))
kable(missing_profile,
caption = "Missing Data Profile")
}| Variable | Missing | Present | Total | Missing_Rate |
|---|---|---|---|---|
| OverallTime | 2 | 248 | 250 | 0.8 |
| Age | 1 | 249 | 250 | 0.4 |
| Anti-X-intensity | 1 | 249 | 250 | 0.4 |
| Anti-Y-intensity | 1 | 249 | 250 | 0.4 |
| Death | 1 | 249 | 250 | 0.4 |
| Grade | 1 | 249 | 250 | 0.4 |
| Grade_Level | 1 | 249 | 250 | 0.4 |
| Group | 1 | 249 | 250 | 0.4 |
| LVI | 1 | 249 | 250 | 0.4 |
| LastFollowUpDate | 1 | 249 | 250 | 0.4 |
| LymphNodeMetastasis | 1 | 249 | 250 | 0.4 |
| Mortality5yr | 1 | 249 | 250 | 0.4 |
| Name | 1 | 249 | 250 | 0.4 |
| Outcome | 1 | 249 | 250 | 0.4 |
| PNI | 1 | 249 | 250 | 0.4 |
| PreinvasiveComponent | 1 | 249 | 250 | 0.4 |
| Race | 1 | 249 | 250 | 0.4 |
| Rater 1 | 1 | 249 | 250 | 0.4 |
| Sex | 1 | 249 | 250 | 0.4 |
| Smoker | 1 | 249 | 250 | 0.4 |
| SurgeryDate | 1 | 249 | 250 | 0.4 |
| TStage | 1 | 249 | 250 | 0.4 |
| Valid | 1 | 249 | 250 | 0.4 |
Benford’s Law Compliance Summary
# Summary of Benford's law results
benford_summary <- data.frame(
Variable = c("MeasurementA", "MeasurementB", "OverallTime"),
Expected_Pattern = "Natural logarithmic distribution",
Assessment = c(
"Evaluate first digit distribution",
"Evaluate first digit distribution",
"Evaluate first digit distribution"
),
Recommendation = "Review any significant deviations from expected pattern"
)
kable(benford_summary,
caption = "Benford's Law Assessment Summary")| Variable | Expected_Pattern | Assessment | Recommendation |
|---|---|---|---|
| MeasurementA | Natural logarithmic distribution | Evaluate first digit distribution | Review any significant deviations from expected pattern |
| MeasurementB | Natural logarithmic distribution | Evaluate first digit distribution | Review any significant deviations from expected pattern |
| OverallTime | Natural logarithmic distribution | Evaluate first digit distribution | Review any significant deviations from expected pattern |
Implementation in jamovi
Data quality assessment functions are available in jamovi:
-
Benford’s Law:
Exploration > ClinicoPath Descriptives > Benford Analysis -
Variable Summary:
Exploration > ClinicoPath Descriptives > Summary of Continuous/Categorical Variables -
Cross-tabulations:
Exploration > ClinicoPath Comparisons > Cross Tables -
Variable Tree:
Exploration > ClinicoPath Descriptive Plots > Variable Tree
Conclusions
This comprehensive data quality assessment provides:
- Completeness Evaluation: Systematic missing data analysis
- Consistency Checks: Logical relationship validation
- Accuracy Assessment: Statistical distribution analysis and outlier detection
- Fraud Detection: Benford’s law analysis for data integrity
- Reliability Testing: Inter-rater agreement evaluation
Regular data quality assessment using these techniques ensures reliable and trustworthy clinical research results.
Best Practices
- Perform quality assessment before any statistical analysis
- Document and investigate any quality issues found
- Establish quality thresholds appropriate for your research context
- Implement ongoing quality monitoring throughout data collection
- Use multiple quality assessment methods for comprehensive evaluation