mariposa (version 0.7.3)

longitudinal_data: Longitudinal Study Data (Synthetic)

Description

A synthetic repeated measures dataset suitable for testing longitudinal analysis functions. Contains realistic treatment effects, missing data patterns, and within-subject correlations.

Usage

longitudinal_data

Format

A data frame with 480 rows and 10 variables:

subject_id

Subject identifier (factor, 1-120)

group

Treatment group (Control, Treatment)

age

Age in years (numeric)

gender

Gender (Male, Female)

time

Time point (T1, T2, T3, T4)

time_numeric

Time point as numeric (1-4)

outcome_score

Primary outcome measure (continuous)

secondary_outcome

Secondary outcome measure (continuous)

physio_measure

Physiological measure (continuous)

questionnaire_score

Questionnaire rating (ordered factor, 1-7)

Details

Study design features:

• 120 subjects (60 per group)

• 4 measurement occasions

• Balanced treatment assignment

• Realistic dropout patterns (0% at T1, 35% by T4)

• Treatment effect grows over time (interaction effect)

• Autoregressive error structure

• Effect size approximately Cohen's d = 0.6

Missing data patterns reflect realistic longitudinal study attrition with higher dropout in later time points. The treatment effect demonstrates a clear Group x Time interaction suitable for repeated measures testing.

See Also

longitudinal_data_wide for the wide format version.

survey_data for the cross-sectional survey dataset.

Examples

# Load required packages and data
library(dplyr)
data(longitudinal_data)

# Simple example analysis with wide format data
# data(longitudinal_data_wide)
# (Repeated measures functions coming in future version)

# Descriptive statistics by group and time
longitudinal_data %>%
  group_by(group, time) %>%
  describe(outcome_score)