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In stepped wedge cluster randomized trials (SW-CRTs), observations collectedunder the control condition are, on average, from an earlier time thanobservations collected under the intervention condition. In a cohort design,participants are followed up throughout the study, so correlations betweenmeasurements within a participant are dependent of the timing in which theobservations are made. Therefore, changes in participants' characteristics overtime must be taken into account when estimating intervention effects. Forexample, participants' age progresses, which may impact the outcome over thestudy period. Motivated by an SW-CRT of a geriatric care intervention toimprove quality of life, we conducted a simulation study to compare modelformulations analysing data from an SW-CRT under different scenarios in whichtime was related to the covariates and the outcome. The aim was to find a modelspecification that produces reliable estimates of the intervention effect. Sixlinear mixed effects (LME) models with different specification of fixed effectswere fitted. Across 1000 simulations per parameter combination, we computedmean and standard error of the estimated intervention effects. We found thatLME models with fixed categorical time effects additional to the fixedintervention effect and two random effects used to account for clustering(within-cluster correlation) and multiple measurements on participants(within-individual correlation) seem to produce unbiased estimates of theintervention effect even if time-varying confounders or their functionalinfluence on outcome were unknown or unmeasured and if secular time trendsoccurred. Therefore, including (time-varying) covariates describing the studycohort seems to be avoidable.

Bias through time-varying covariates in the analysis of cohort stepped wedge trials: a simulation study