Abstract
In epidemiological or clinical studies with follow-ups, data tables generated and processed for statistical analysis are often of the "wide-format" type, consisting of one row per individual. However, depending on the situation and purpose of the study, they may need to be transformed into the "long-format" type, which allows for multiple rows per individual. This tutorial clarifies the typical situations wherein researchers are recommended to split follow-up times to generate long-format data tables. In such applications, the major analytical aims consist of (i) estimating the outcome incidence rates or their ratios between ≥2 groups, according to specific follow-up time periods; (ii) examining the interaction between the exposure status and follow-up time to assess the proportional hazards assumption in Cox models; (iii) dealing with time-varying exposures for descriptive or predictive purposes; (iv) estimating the causal effects of time-varying exposures while adjusting for time-varying confounders that may be affected by past exposures; and (v) comparing different time periods within the same individual in self-controlled case-series analyses. This tutorial also discusses how to split follow-up times according to their purposes in practical settings, providing example codes in Stata, R, and SAS.