Joint Estimation of Treatment and Placebo Effects in Clinical Trials with Longitudinal Blinding Assessments.
In some therapeutic areas, treatment evaluation is frequently complicated by a possible placebo effect (i.e., the psychobiological effect of a patient's knowledge or belief of being treated). When a substantial placebo effect is likely to exist, it is important to distinguish the treatment and placebo effects in quantifying the clinical benefit of a new treatment. These causal effects can be formally defined in a joint causal model that includes treatment (e.g., new versus placebo) and treatmentality (i.e., a patient's belief or mentality about which treatment she or he has received) as separate exposures. Information about the treatmentality exposure can be obtained from blinding assessments, which are increasingly common in clinical trials where blinding success is in question. Assuming that treatmentality has a lagged effect and is measured at multiple time points, this article is concerned with joint evaluation of treatment and placebo effects in clinical trials with longitudinal follow-up, possibly with monotone missing data. We describe and discuss several methods adapted from the longitudinal causal inference literature, apply them to a weight loss study, and compare them in simulation experiments that mimic the weight loss study.
Department of Mathematics, Harbin Institute of Technology, Harbin, P. R. China; Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Wei
W
Liu
Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Zhiwei
Z
Zhang
Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
R Jason
RJ
Schroeder
Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Martin
M
Ho
Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Bo
B
Zhang
Division of Reproductive, Gastro-Renal, and Urological Devices, Office of Device Evaluation, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Cynthia
C
Long
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
Hui
H
Zhang
Office of Biostatistics and Epidemiology, Center for Biologic Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
Telba Z
TZ
Irony
G-computation; causal inference; confounding; double robustness; inverse probability weighting; sequential regression; targeted minimum loss based estimation; treatmentality