  In stepped wedge cluster randomized trials (SW-CRTs), observations collected
under the control condition are, on average, from an earlier time than
observations collected under the intervention condition. In a cohort design,
participants are followed up throughout the study, so correlations between
measurements within a participant are dependent of the timing in which the
observations are made. Therefore, changes in participants' characteristics over
time must be taken into account when estimating intervention effects. For
example, participants' age progresses, which may impact the outcome over the
study period. Motivated by an SW-CRT of a geriatric care intervention to
improve quality of life, we conducted a simulation study to compare model
formulations analysing data from an SW-CRT under different scenarios in which
time was related to the covariates and the outcome. The aim was to find a model
specification that produces reliable estimates of the intervention effect. Six
linear mixed effects (LME) models with different specification of fixed effects
were fitted. Across 1000 simulations per parameter combination, we computed
mean and standard error of the estimated intervention effects. We found that
LME models with fixed categorical time effects additional to the fixed
intervention 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 the
intervention effect even if time-varying confounders or their functional
influence on outcome were unknown or unmeasured and if secular time trends
occurred. Therefore, including (time-varying) covariates describing the study
cohort seems to be avoidable.
