Estimating drug effects in the presence of placebo response: causal inference using growth mixture modeling.
Estimating drug effects in the presence of placebo response: causal inference using growth mixture modeling.
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DOI：
10.1002/sim.3721
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发表时间：
2009-11-30
影响因子：
2
通讯作者：
Brown, Hendricks C.
中科院分区：
文献类型：
--
作者：
Muthen, Bengt;Brown, Hendricks C.
针对抗抑郁药和其他药物的安慰剂对照随机试验通常显示安慰剂组中相当大比例的受试者有反应。可以假设潜在的安慰剂反应者也存在于药物治疗组中，这使得评估药物效果变得困难。药物研究的一个关键重点应该是估计对药物有反应而对安慰剂没有反应的个体的百分比（“仅药物反应者”）。本文研究了有限混合模型方法，以揭示最多四种潜在混合成分的百分比：从不响应者、仅药物响应者、仅安慰剂响应者和始终响应者。使用两个示例来说明该模型：一项为期 12 周的抗抑郁药试验，具有连续结果（汉密尔顿 D 评分）；一项为期 7 周的精神分裂症试验，具有二元结果（疾病水平）。该方法是使用潜在结果和主要分层以休闲建模术语制定的。具有最大似然估计的增长混合模型 (GMM) 用于揭示不同的混合成分。结果指出了比较药物组和安慰剂组边际反应率的传统方法的局限性。利用 GMM 估计的四类受试者患病率和仅药物反应者药物效应估计来增强此类报告是有用的。
Placebo-controlled randomized trials for antidepressants and other drugs often show a response for a sizeable percentage of the subjects in the placebo group. Potential placebo responders can be assumed to exist also in the drug treatment group, making it difficult to assess the drug effect. A key drug research focus should be to estimate the percentage of individuals among those who responded to the drug who would not have responded to the placebo (”Drug Only Responders”). This paper investigates a finite mixture model approach to uncover percentages of up to four potential mixture components: Never Responders, Drug Only Responders, Placebo Only Responders, and Always responders. Two examples are used to illustrate the modeling, a twelve-week antidepressant trial with a continuous outcome (Hamilton D score) and a 7-week schizophrenia trial with a binary outcome (illness level). The approach is formulated in casual modeling terms using potential outcomes and principal stratification. Growth mixture modeling (GMM) with maximum-likelihood estimation is used to uncover the different mixture components. The results point to the limitations of the conventional approach of comparing marginal response rates for drug and placebo groups. It is useful to augment such reporting with the GMM estimated prevalences for the four classes of subjects and the Drug Only Responder drug effect estimate.
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影响因子：
1.2
作者：
Petkova, Eva;Tarpey, Thaddeus;Govindarajulu, Usha
通讯作者：
Govindarajulu, Usha
影响因子：
1
作者：
Rubin, DB
通讯作者：
Rubin, DB
影响因子：
1.9
作者：
Muthén, B;Shedden, K
通讯作者：
Shedden, K
影响因子：
3.7
作者：
Angrist, JD;Imbens, GW;Rubin, DB
通讯作者：
Rubin, DB
DOI：
10.1198/016214503000000783
发表时间：
2003-12-01
影响因子：
3.7
作者：
Tarpey, T;Petkova, E;Ogden, RT
通讯作者：
Ogden, RT