Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes.

Background: The fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns.

The fixed-effects model for robust analysis of stepped-wedge cluster trials with a small number of clusters and continuous outcomes: a simulation study.

Background: Stepped-wedge cluster trials (SW-CTs) describe a cluster trial design where treatment rollout is staggered over the course of the trial. Clusters are commonly randomized to receive treatment beginning at different time points in this study design (commonly referred to as a Stepped-wedge cluster randomized trial; SW-CRT), but they can also be non-randomized. Trials with this design regularly have a low number of clusters and can be vulnerable to covariate imbalance.

Estimation and reduction of bias in self-controlled case series with non-rare event dependent outcomes and heterogeneous populations.

The self-controlled case series (SCCS) is a commonly adopted study design in the assessment of vaccine and drug safety. Recurrent event data collected from SCCS studies are typically analyzed using the conditional Poisson model which assumes event times are independent within-cases. This assumption is violated in the presence of event dependence, where the occurrence of an event influences the probability and timing of subsequent events.

Cluster randomized trial designs for modeling time-varying intervention effects.

Stepped-wedge cluster randomized trials (SW-CRTs) are typically analyzed assuming a constant intervention effect. In practice, the intervention effect may vary as a function of exposure time, leading to biased results. The estimation of time-on-intervention (TOI) effects specifies separate discrete intervention effects for each elapsed period of exposure time since the intervention was first introduced.

Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials.

Stepped-wedge cluster randomized trials (SW-CRTs) are typically analyzed using mixed effects models. The fixed effects model is a useful alternative that controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. In principle, all clusters in SW-CRTs are designed to eventually receive the intervention, but in real-world research, some trials can end with unexposed clusters (clusters that never received the intervention), such as when a trial is terminated early based on interim analysis results.