TY - JOUR
T1 - Improving precision by adjusting for prognostic baseline variables in randomized trials with binary outcomes, without regression model assumptions
AU - Steingrimsson, Jon Arni
AU - Hanley, Daniel F.
AU - Rosenblum, Michael
N1 - Publisher Copyright:
© 2016
PY - 2017/3/1
Y1 - 2017/3/1
N2 - In randomized clinical trials with baseline variables that are prognostic for the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables. A major challenge is that there are multiple statistical methods to adjust for baseline variables, but little guidance on which is best to use in a given context. The choice of method can have important consequences. For example, one commonly used method leads to uninterpretable estimates if there is any treatment effect heterogeneity, which would jeopardize the validity of trial conclusions. We give practical guidance on how to avoid this problem, while retaining the advantages of covariate adjustment. This can be achieved by using simple (but less well-known) standardization methods from the recent statistics literature. We discuss these methods and give software in R and Stata implementing them. A data example from a recent stroke trial is used to illustrate these methods.
AB - In randomized clinical trials with baseline variables that are prognostic for the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables. A major challenge is that there are multiple statistical methods to adjust for baseline variables, but little guidance on which is best to use in a given context. The choice of method can have important consequences. For example, one commonly used method leads to uninterpretable estimates if there is any treatment effect heterogeneity, which would jeopardize the validity of trial conclusions. We give practical guidance on how to avoid this problem, while retaining the advantages of covariate adjustment. This can be achieved by using simple (but less well-known) standardization methods from the recent statistics literature. We discuss these methods and give software in R and Stata implementing them. A data example from a recent stroke trial is used to illustrate these methods.
KW - Covariate adjustment
KW - Post-stratification
UR - http://www.scopus.com/inward/record.url?scp=85009223747&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009223747&partnerID=8YFLogxK
U2 - 10.1016/j.cct.2016.12.026
DO - 10.1016/j.cct.2016.12.026
M3 - Article
C2 - 28064029
AN - SCOPUS:85009223747
SN - 1551-7144
VL - 54
SP - 18
EP - 24
JO - Contemporary Clinical Trials
JF - Contemporary Clinical Trials
ER -