Joint Estimation of Treatment and Placebo Effects in Clinical Trials With Longitudinal Blinding Assessments

被引:2
|
作者
Liu, Wei [1 ]
Zhang, Zhiwei [1 ]
Schroeder, R. Jason [1 ]
Ho, Martin [1 ]
Zhang, Bo [1 ]
Long, Cynthia [1 ]
Zhang, Hui [1 ]
Irony, Telba Z. [1 ]
机构
[1] US FDA, Off Surveillance & Biometr, Ctr Devices & Radiol Hlth, Silver Spring, MD 20993 USA
关键词
Causal inference; Confounding; Double robustness; G-computation; Inverse probability weighting; Sequential regression; Targeted minimum loss based estimation; Treatmentality; CAUSAL INFERENCE; DISULFIRAM; MODEL;
D O I
10.1080/01621459.2015.1130633
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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 vs. 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. Supplementary materials for this article are available online.
引用
收藏
页码:538 / 548
页数:11
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