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Summarizing the extent of visit irregularity in longitudinal data.
Armend Lokku1,2, Lily S Lim3,4, Catherine S Birken5,6,7,8
1Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, Ontario, Canada. Armend.Lokku@mail.utoronto.ca.
BMC Medical Research Methodology
|May 31, 2020
Summary
We developed methods to measure visit irregularity in longitudinal studies. These measures help researchers choose the best analysis approach for their data, distinguishing between planned and irregular patient visits.
Area of Science:
- Biostatistics
- Longitudinal Data Analysis
- Clinical Research Methodology
Background:
- Observational longitudinal studies frequently involve irregularly timed patient visits.
- The timing of these visits can be informative, influencing data analysis strategies.
- Quantifying visit irregularity is crucial for selecting appropriate analytical methods.
Purpose of the Study:
- To propose descriptive measures for quantifying visit irregularity in longitudinal data.
- To aid in selecting suitable analytic outcome approaches based on visit patterns.
- To differentiate between protocol-driven and irregular visit schedules.
Main Methods:
- Divided study periods into time bins.
- Calculated mean proportions of individuals with 0, 1, or >1 visits per bin.
- Applied methods to the TARGet Kids! and childhood-onset Systemic Lupus Erythematosus (cSLE) studies.
Main Results:
- TARGet Kids! study showed high proportions of 0 visits, indicating missingness in repeated measures.
- cSLE study, with 6-month bins, revealed substantial proportions of individuals with 1 and >1 visits, suggesting irregular attendance.
- Proportions of >1 visits were consistently low (<0.03) in TARGet Kids!, contrasting with cSLE.
Conclusions:
- The proposed methods effectively describe the extent of visit irregularity.
- These measures help distinguish between scheduled and irregular visits.
- Identifying visit patterns is a critical step in choosing the correct statistical analysis for longitudinal outcomes.
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