Analysis of data including repeated events from the same individual ?

10 November 2024 1 3K Report

I am investigating complications associated with bowel preparation for colonoscopy. I will analyze data from approximately 10,000 cases, some of which involve the same individual undergoing colonoscopy multiple times (up to 10), though most individuals have only had the procedure once. Are the analyses I plan to perform (e.g., t-test, chi-square test, ANOVA, logistic regression, etc.) applicable to this dataset, or should I consider a different analytical method?

Mahmoud Elshahawy

Given the presence of repeated measures for some individuals in your dataset, a few considerations are necessary to ensure accurate analysis.

Repeated Measures Analysis: Since some individuals have undergone multiple colonoscopies, the assumption of independence between observations in tests like the t-test, chi-square test, or standard ANOVA may be violated. A mixed-effects model or a generalized estimating equations (GEE) approach could better account for the within-subject correlation.

Logistic Regression Adjustments: For logistic regression, incorporating a random intercept for each individual (in a mixed-effects logistic regression) or using GEE can account for the correlation in repeated observations, leading to more reliable estimates of odds ratios related to complications.

Alternative Analyses for Non-Independence: If analyzing continuous outcomes (e.g., severity of complications), consider a linear mixed-effects model. For categorical outcomes (e.g., presence of complications), generalized linear mixed models (GLMMs) or GEE will be useful.

ANOVA: If you plan to use ANOVA, switch to a repeated-measures ANOVA or, more flexibly, a mixed-effects model if comparing complication rates across groups while accounting for repeated measures.

These methods will provide more accurate estimates by addressing the nested structure of your data, helping to avoid inflated Type I errors due to the repeated measures.

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