Patient-Reported Outcomes & Quality of Life Analysis

Usage

patientreported(
  data,
  scale_items,
  patient_id,
  time_var,
  group_var,
  demographic_vars,
  scale_type = "generic_qol",
  instrument_name = "custom",
  scoring_method = "sum_score",
  reverse_coded_items,
  response_scale_min = 1,
  response_scale_max = 5,
  reliability_analysis = TRUE,
  validity_analysis = TRUE,
  factor_analysis = FALSE,
  irt_analysis = FALSE,
  dimensionality_test = TRUE,
  measurement_invariance = FALSE,
  missing_data_method = "pro_rata_scoring",
  min_items_required = 2,
  missing_threshold = 0.5,
  clinical_interpretation = TRUE,
  normative_comparison = FALSE,
  minimal_important_difference = TRUE,
  mid_value = 5,
  ceiling_floor_effects = TRUE,
  longitudinal_analysis = FALSE,
  change_analysis = "simple_change",
  trajectory_analysis = FALSE,
  time_to_deterioration = FALSE,
  group_comparisons = FALSE,
  comparison_method = "t_test",
  effect_size_analysis = TRUE,
  multiple_comparisons = "fdr",
  responder_analysis = FALSE,
  responder_threshold = 10,
  anchor_based_analysis = FALSE,
  anchor_variables,
  distribution_based_analysis = TRUE,
  data_quality_assessment = TRUE,
  response_patterns = TRUE,
  acquiescence_analysis = FALSE,
  detailed_output = TRUE,
  summary_report = TRUE,
  individual_profiles = FALSE,
  save_scores = FALSE,
  regulatory_documentation = TRUE
)

Arguments

data

the data as a data frame

scale_items

Items/questions that comprise the PRO scale or questionnaire

patient_id

Patient identifier for longitudinal analysis

time_var

Time point variable for longitudinal PRO analysis (visit, week, etc.)

group_var

Grouping variable for between-group PRO comparisons (treatment, disease stage, etc.)

demographic_vars

Demographic variables for subgroup analysis and validation

scale_type

Type of PRO scale being analyzed

instrument_name

Standardized PRO instrument being used

scoring_method

Method for calculating PRO scale scores

reverse_coded_items

Items that need to be reverse-coded before scoring

response_scale_min

Minimum value of the response scale (e.g., 1 for 1-5 Likert scale)

response_scale_max

Maximum value of the response scale (e.g., 5 for 1-5 Likert scale)

reliability_analysis

Perform reliability analysis (Cronbach's alpha, item-total correlations)

validity_analysis

Perform validity analysis (construct validity, concurrent validity)

factor_analysis

Perform exploratory and confirmatory factor analysis

irt_analysis

Perform IRT analysis for item characteristics and scale properties

dimensionality_test

Test scale dimensionality and factor structure

measurement_invariance

Test measurement invariance across groups and time points

missing_data_method

Method for handling missing item responses

min_items_required

Minimum number of non-missing items required for scale scoring

missing_threshold

Maximum proportion of missing items allowed for scoring (50\

clinical_interpretationProvide clinical interpretation of PRO scores

normative_comparisonCompare scores to published normative data

minimal_important_differenceAnalyze changes relative to minimal important difference (MID)

mid_valueMinimal important difference value for clinical significance

ceiling_floor_effectsAnalyze ceiling and floor effects in PRO responses

longitudinal_analysisPerform longitudinal PRO analysis

change_analysisMethod for analyzing PRO changes over time

trajectory_analysisAnalyze PRO trajectories and patterns over time

time_to_deteriorationAnalyze time to meaningful PRO deterioration

group_comparisonsCompare PRO scores between groups

comparison_methodStatistical method for group comparisons

effect_size_analysisCalculate effect sizes for group differences

multiple_comparisonsMethod for multiple comparisons correction

responder_analysisAnalyze proportion of patients with meaningful improvements

responder_thresholdThreshold for defining treatment responders

anchor_based_analysisUse anchor variables to interpret PRO changes

anchor_variablesExternal anchor variables for interpreting PRO changes

distribution_based_analysisUse distribution-based methods for interpreting changes

data_quality_assessmentAssess PRO data quality and completion patterns

response_patternsAnalyze response patterns and potential response bias

acquiescence_analysisAnalyze acquiescence and extreme response styles

detailed_outputInclude detailed psychometric and clinical analysis results

summary_reportGenerate comprehensive PRO analysis summary report

individual_profilesGenerate individual patient PRO profiles

save_scoresSave calculated PRO scores to dataset

regulatory_documentationInclude documentation for regulatory submissions

A results object containing:

results$scale_overview					a table
results$data_summary					a table
results$item_statistics					a table
results$item_correlations					a table
results$reliability_results					a table
results$item_reliability					a table
results$validity_results					a table
results$factor_analysis_results					a table
results$factor_loadings					a table
results$scale_scores					a table
results$score_distribution					a table
results$group_comparison_results					a table
results$group_descriptives					a table
results$longitudinal_results					a table
results$change_analysis_results					a table
results$clinical_interpretation_results					a table
results$minimal_important_difference_results					a table
results$data_quality_results					a table
results$missing_data_patterns					a table
results$response_patterns					a table
results$responder_analysis_results					a table
results$regulatory_summary					a table
results$item_distribution_plot					an image
results$scale_distribution_plot					an image
results$reliability_plot					an image
results$factor_plot					an image
results$group_comparison_plot					an image
results$longitudinal_plot					an image
results$change_plot					an image
results$responder_plot					an image
results$missing_data_plot					an image
results$correlation_heatmap					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:results$scale_overview$asDFas.data.frame(results$scale_overview) Comprehensive analysis of Patient-Reported Outcomes (PRO) and Quality of Life (QoL) data for clinical research. Includes psychometric validation, score calculation, change analysis, and clinical interpretation. Supports standardized instruments (SF-36, EORTC QLQ-C30, FACT-G, etc.) and custom questionnaires with missing data handling and longitudinal analysis. Essential for patient-centered outcomes research and clinical trials. data('pro_data')patientreported( data = pro_data, scale_items = c("item1", "item2", "item3"), patient_id = "patient_id", time_var = "visit_number", reliability_analysis = TRUE, validity_analysis = TRUE )