Baker Stuart G, Kramer Barnett S
Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20892-7354, USA.
Stat Methods Med Res. 2005 Aug;14(4):349-67. doi: 10.1191/0962280205sm404oa.
Efficacy, which we define as the effect of receiving intervention on health outcomes among a group of subjects, is the quantity of interest for many investigators. In contrast, intent-to-treat analyses in randomized trials and their analogue for observational before-and-after studies compare outcomes between randomization groups or before-and-after time periods. When there is switching of interventions, estimates based on intent-to-treat are biased for estimating efficacy. By constructing a model based on potential outcomes, one can make reasonable assumptions to estimate efficacy under 'all-or-none' switching of interventions in which switching occurs immediately after randomization or at the start of the time period. This paper reviews the basic methodology, with emphasis on simple maximum likelihood estimates that arise with completely observed outcomes, partially missing binary outcomes, and discrete-time survival outcomes. Particular attention is paid to estimating efficacy in meta-analysis, where the interpretation is much more straightforward than with intent-to-treat analyses.
疗效是许多研究者关注的核心指标，我们将其定义为一组受试者接受干预后对健康结果产生的影响。相比之下，随机试验中的意向性分析以及观察性前后对照研究的类似分析，是比较随机分组或前后时间段之间的结果。当存在干预措施切换时，基于意向性分析的估计对于疗效估计会产生偏差。通过构建基于潜在结果的模型，人们可以做出合理假设，以估计在“全有或全无”干预切换情况下的疗效，这种切换发生在随机化之后或时间段开始时。本文回顾了基本方法，重点是在完全观察到的结果、部分缺失的二元结果和离散时间生存结果情况下出现的简单最大似然估计。特别关注在荟萃分析中估计疗效，其解释比意向性分析更为直接。
