There has been significant attention given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this through a sequence of decision rules that map patient information to a suggested treatment. The data for estimating and evaluating treatment regimes are ideally gathered through the use of Sequential Multiple Assignment Randomized Trials (SMARTs) though longitudinal observational studies are commonly used due to the potentially prohibitive costs of conducting a SMART. These studies are typically sized for simple comparisons of fixed treatment sequences or, in the case of observational studies, a priori sample size calculations are often not performed. We develop sample size procedures for the estimation of dynamic treatment regimes from observational studies. Our approach uses pilot data to ensure a study will have sufficient power for comparing the value of the optimal regime, i.e. the expected outcome if all patients in the population were treated by following the optimal regime, with a known comparison mean. Our approach also ensures the value of the estimated optimal treatment regime is within an a priori set range of the value of the true optimal regime with a high probability. We examine the performance of the proposed procedure with a simulation study and use it to size a study for reducing depressive symptoms using data from electronic health records.
翻译:对根据个别病人特点制定由数据驱动的病人护理方法给予了很大重视。动态治疗制度通过一系列决定规则使这一点正规化,这些决定规则将病人信息映射成建议治疗。估计和评价治疗制度的数据最好通过使用序列性多分配随机测试(SMARTs)来收集,尽管由于进行SMART的费用可能令人望而生畏,通常采用纵向观察性观察研究,这些研究通常用于简单比较固定治疗序列,或者在观察性研究中,通常不进行先验性抽样规模计算。我们开发了从观察性研究中估算动态治疗制度的抽样规模程序。我们使用试点数据确保一项研究,将有足够的权力来比较最佳治疗制度的价值,即如果人口中的所有病人都按照最佳治疗制度接受治疗,预期的结果是已知的比较平均值。我们的方法还确保估计的最佳治疗制度的价值在真正最佳制度价值的先订范围以很高的概率进行。我们通过模拟研究来审查拟议程序的执行情况,并使用模拟性研究,将它用于缩小健康症状的研究。