title: Different methods to analyze stepped wedge trial designs revealed different aspects of intervention effects.
abstract: OBJECTIVE
  Within epidemiology, a stepped wedge trial design (i.e., a one-way crossover trial in which several arms start the intervention at different time points) is increasingly popular as an alternative to a classical cluster randomized controlled trial. Despite this increasing popularity, there is a huge variation in the methods used to analyze data from a stepped wedge trial design.
  
  
  METHODS
  Four linear mixed models were used to analyze data from a stepped wedge trial design on two example data sets. The four methods were chosen because they have been (frequently) used in practice. Method 1 compares all the intervention measurements with the control measurements. Method 2 treats the intervention variable as a time-independent categorical variable comparing the different arms with each other. In method 3, the intervention variable is a time-dependent categorical variable comparing groups with different number of intervention measurements, whereas in method 4, the changes in the outcome variable between subsequent measurements are analyzed.
  
  
  RESULTS
  Regarding the results in the first example data set, methods 1 and 3 showed a strong positive intervention effect, which disappeared after adjusting for time. Method 2 showed an inverse intervention effect, whereas method 4 did not show a significant effect at all. In the second example data set, the results were the opposite. Both methods 2 and 4 showed significant intervention effects, whereas the other two methods did not. For method 4, the intervention effect attenuated after adjustment for time.
  
  
  CONCLUSIONS
  Different methods to analyze data from a stepped wedge trial design reveal different aspects of a possible intervention effect. The choice of a method partly depends on the type of the intervention and the possible time-dependent effect of the intervention. Furthermore, it is advised to combine the results of the different methods to obtain an interpretable overall result.
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Centre, Amsterdam, The Netherlands; EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands. Electronic address: jwr.twisk@vumc.nl.
authors.forename: J W R
authors.initials: JW
authors.lastname: Twisk
authors.affiliation: EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands; Department of General Practice & Elderly Care Medicine, VU University Medical Center, Amsterdam, The Netherlands; Gérontopôle, Toulouse University Hospital, Toulouse, France.
authors.forename: E O
authors.initials: EO
authors.lastname: Hoogendijk
authors.affiliation: EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands; Department of General Practice & Elderly Care Medicine, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: S A
authors.initials: SA
authors.lastname: Zwijsen
authors.affiliation: EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands; Section Methodology and Applied Biostatistics, Department of Health Sciences, VU University, Amsterdam, The Netherlands.
authors.forename: M R
authors.initials: MR
authors.lastname: de Boer
mesh_terms: D002986:Clinical Trials as Topic; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D016021:Epidemiologic Studies; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D013997:Time Factors
keywords: Baseline adjustment; Intervention; Longitudinal studies; Statistical methods; Stepped wedge trial design; Time adjustment