TY - JOUR
T1 - Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects
JF - Annals of Applied Statistics
Y1 - 2017
A1 - Nguyen, T. Q.
A1 - Ebnesajjad, C.
A1 - Cole, S. R.
A1 - Stuart, E. A.
KW - Generalization
KW - sensitivity analysis
KW - Treatment effect heterogeneity
KW - Unobserved effect modifier
KW - Unobserved moderator
AB - In the presence of treatment effect heterogeneity, the average treatment effect (ATE) in a randomized controlled trial (RCT) may differ from the average effect of the same treatment if applied to a target population of interest. If all treatment effect moderators are observed in the RCT and in a dataset representing the target population, then we can obtain an estimate for the target population ATE by adjusting for the difference in the distribution of the moderators between the two samples. This paper considers sensitivity analyses for two situations: (1) where we cannot adjust for a specific moderator V observed in the RCT because we do not observe it in the target population; and (2) where we are concerned that the treatment effect may be moderated by factors not observed even in the RCT, which we represent as a composite moderator U. In both situations, the outcome is not observed in the target population. For situation (1), we offer three sensitivity analysis methods based on (i) an outcome model, (ii) full weighting adjustment and (iii) partial weighting combined with an outcome model. For situation (2), we offer two sensitivity analyses based on (iv) a bias formula and (v) partial weighting combined with a bias formula. We apply methods (i) and (iii) to an example where the interest is to generalize from a smoking cessation RCT conducted with participants of alcohol/illicit drug use treatment programs to the target population of people who seek treatment for alcohol/illicit drug use in the US who are also cigarette smokers. In this case a treatment effect moderator is observed in the RCT but not in the target population dataset. © Institute of Mathematical Statistics, 2017.
VL - 11
N1 - Export Date: 24 May 2017Funding details: DRL-1335843, NSF, National Science FoundationFunding details: ME-150227794, PCORI, Patient-Centered Outcomes Research InstituteFunding details: R01-DA036520, NIDA, National Institute on Drug AbuseFunding details: R01AI100654, NIAID, National Institute of Allergy and Infectious DiseasesFunding details: T32DA007292, NIDA, National Institute on Drug AbuseFunding text: Supported in part by NIDA Grant T32DA007292 (PI R. M. Johnson) Supported in part by NSF Grant DRL-1335843 (co-PIs E. A. Stuart and R. B. Olsen), PCORI Grant ME-150227794 (co-PIs I. Dahabreh and E. A. Stuart) and NIDA Grant R01-DA036520 (PI R. Mojtabai) Supported in part by NIAID Grant R01AI100654 (PI S. R. Cole). We thank Ryoko Susukida and Ramin Mojtabai at Johns Hopkins Bloomberg School of Public Health for their assistance in identifying the data example, and thank the Area Editor, the Associate Editor and the Reviewer for their very helpful comments on the manuscript.
U1 - casual inference methods
ER -