Skip to contents

Time Series Data for Longitudinal Analysis

Source: R/data_toolssummary_docs.R
toolssummary_timeseries_data.Rd

Longitudinal dataset with repeated measures per subject across multiple timepoints, realistic dropout patterns, and outcome tracking. Designed to test time-based summaries, longitudinal data presentation, missing data patterns over time, and grouped analysis capabilities using summarytools enhanced features.

Usage

toolssummary_timeseries_data

Format

A data frame with 200 observations and 12 variables:

subject_id

Character. Subject identifier (TS_001 to TS_040)

timepoint

Factor. Assessment timepoint ("T1", "T2", "T3", "T4", "T5")

months_from_baseline

Integer. Time since baseline (0, 3, 6, 12, 24 months)

assessment_date

Date. Date of assessment

primary_outcome

Numeric. Primary outcome score (0-100) with time-dependent missing

secondary_outcome_1

Numeric. Secondary outcome correlated with primary

secondary_outcome_2

Numeric. Independent secondary outcome

response_status

Factor. Treatment response ("Responder", "Non-responder")

compliance_percent

Numeric. Treatment compliance percentage (75-100%)

dose_level

Ordered Factor. Dose level ("Low" < "Medium" < "High")

adverse_events

Integer. Count of adverse events (0-5)

biomarker_level

Numeric. Biomarker measurement (log-normal distribution)

Source

Simulated data generated using create_toolssummary_test_data.R

Details

This dataset represents a longitudinal clinical study with 40 subjects followed over 5 timepoints (24 months total). It includes realistic patterns of outcome changes, dropout over time, and missing data that increase with follow-up duration, making it ideal for testing summarytools longitudinal capabilities.

Key Features:

  • 40 subjects with up to 5 timepoints each (200 total observations)

  • Time-dependent outcome patterns and trends

  • Realistic dropout patterns (increasing missing data over time)

  • Multiple correlated and independent outcomes

  • Compliance and adverse event tracking

  • Response status categorization

summarytools Integration Testing:

  • dfSummary: Longitudinal data overview with temporal patterns

  • freq: Time-based frequency analysis and response categorization

  • descr: Outcome statistics across timepoints with trend assessment

  • ctable: Cross-tabulations by timepoint and response status

Recommended Usage Scenarios:

  • Longitudinal outcome analysis by timepoint

  • Dropout pattern assessment over time

  • Treatment response analysis

  • Compliance and safety monitoring

See also

toolssummary, toolssummary_datasets

Examples

if (FALSE) { # \dontrun{
# Load the dataset
data(toolssummary_timeseries_data)

# Longitudinal outcomes analysis
result <- toolssummary(
  data = toolssummary_timeseries_data,
  vars = c("primary_outcome", "secondary_outcome_1", "compliance_percent"),
  useSummarytools = TRUE,
  showDescr = TRUE,
  showDfSummary = TRUE
)

# Analysis by timepoint
result_time <- toolssummary(
  data = toolssummary_timeseries_data,
  vars = c("primary_outcome", "response_status", "adverse_events"),
  groupVar = "timepoint",
  useSummarytools = TRUE,
  showCrosstabs = TRUE
)
} # }