Efficient design and inference for multistage randomized trials of individualized treatment policies
Efficient design and inference for multistage randomized trials of individualized treatment policies
复制标题
DOI：
10.1093/biostatistics/kxr016
复制
发表时间：
2012-01-01
期刊：
影响因子：
2.1
通讯作者：
Lavori, Philip W.
中科院分区：
文献类型：
--
作者：
Dawson, Ree;Lavori, Philip W.
在不同领域的个性化“适应性”治疗政策的临床需求已经催生了临床试验方法的发展，通过多阶段设计进行实验评估，建立在用于分析自然观察策略的方法基础上。由于通常不需要对多阶段试验数据进行参数平滑（与适应性策略的观察数据相反），因此可以在不同的方法学方法之间建立直接联系。我们证明了代数的最大似然（ML）和最优半参数（SP）估计的人口平均值的结果的治疗政策和它的标准误差是相等的，在一定的实验条件下。这一结果是用来开发一个统一的和有效的方法来设计和推理的多阶段试验的政策，适应治疗根据离散的反应。我们推导出一个样本量公式表示的最佳SP人口方差的参数版本。非参数（基于样本）ML估计在模拟研究中表现良好，就达到的功效而言，对于真实的研究中最可能发生的场景，即使样本量基于参数公式。ML优于SP估计量;实现功效的差异主要反映了其群体平均值估计值（而不是估计标准误）的差异。当故意模拟某些离散结局的强非线性时，两种方法都不能减轻高估样本量的可能性;然而，对于许多临床背景，这种偏离线性的情况可能不是一个问题，这使得竞争性治疗政策的评价有意义。
Clinical demand for individualized "adaptive" treatment policies in diverse fields has spawned development of clinical trial methodology for their experimental evaluation via multistage designs, building upon methods intended for the analysis of naturalistically observed strategies. Because often there is no need to parametrically smooth multistage trial data (in contrast to observational data for adaptive strategies), it is possible to establish direct connections among different methodological approaches. We show by algebraic proof that the maximum likelihood (ML) and optimal semiparametric (SP) estimators of the population mean of the outcome of a treatment policy and its standard error are equal under certain experimental conditions. This result is used to develop a unified and efficient approach to design and inference for multistage trials of policies that adapt treatment according to discrete responses. We derive a sample size formula expressed in terms of a parametric version of the optimal SP population variance. Nonparametric (sample-based) ML estimation performed well in simulation studies, in terms of achieved power, for scenarios most likely to occur in real studies, even though sample sizes were based on the parametric formula. ML outperformed the SP estimator; differences in achieved power predominately reflected differences in their estimates of the population mean (rather than estimated standard errors). Neither methodology could mitigate the potential for overestimated sample sizes when strong nonlinearity was purposely simulated for certain discrete outcomes; however, such departures from linearity may not be an issue for many clinical contexts that make evaluation of competitive treatment policies meaningful.