Different methods to analyze stepped wedge trial designs revealed different aspects of intervention effects

J W R Twisk, E O Hoogendijk, Sandra A. Zwijsen, M R de Boer

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

OBJECTIVES: 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.

STUDY DESIGN AND SETTING: 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.

CONCLUSION: 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.

Original languageEnglish
Pages (from-to)75-83
Number of pages9
JournalJournal of Clinical Epidemiology
Volume72
DOIs
Publication statusPublished - Apr 2016

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Cross-Over Studies
Linear Models
Epidemiology
Randomized Controlled Trials
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Keywords

  • Clinical Trials as Topic
  • Cross-Over Studies
  • Data Interpretation, Statistical
  • Epidemiologic Studies
  • Humans
  • Linear Models
  • Longitudinal Studies
  • Models, Statistical
  • Randomized Controlled Trials as Topic
  • Research Design
  • Time Factors
  • Comparative Study
  • Journal Article

Cite this

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title = "Different methods to analyze stepped wedge trial designs revealed different aspects of intervention effects",
abstract = "OBJECTIVES: 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.STUDY DESIGN AND SETTING: 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.CONCLUSION: 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.",
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Different methods to analyze stepped wedge trial designs revealed different aspects of intervention effects. / Twisk, J W R; Hoogendijk, E O; Zwijsen, Sandra A.; de Boer, M R.

In: Journal of Clinical Epidemiology, Vol. 72, 04.2016, p. 75-83.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Different methods to analyze stepped wedge trial designs revealed different aspects of intervention effects

AU - Twisk, J W R

AU - Hoogendijk, E O

AU - Zwijsen, Sandra A.

AU - de Boer, M R

N1 - Copyright © 2016 Elsevier Inc. All rights reserved.

PY - 2016/4

Y1 - 2016/4

N2 - OBJECTIVES: 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.STUDY DESIGN AND SETTING: 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.CONCLUSION: 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.

AB - OBJECTIVES: 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.STUDY DESIGN AND SETTING: 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.CONCLUSION: 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.

KW - Clinical Trials as Topic

KW - Cross-Over Studies

KW - Data Interpretation, Statistical

KW - Epidemiologic Studies

KW - Humans

KW - Linear Models

KW - Longitudinal Studies

KW - Models, Statistical

KW - Randomized Controlled Trials as Topic

KW - Research Design

KW - Time Factors

KW - Comparative Study

KW - Journal Article

U2 - 10.1016/j.jclinepi.2015.11.004

DO - 10.1016/j.jclinepi.2015.11.004

M3 - Article

VL - 72

SP - 75

EP - 83

JO - Journal of Clinical Epidemiology

JF - Journal of Clinical Epidemiology

SN - 0895-4356

ER -