Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, U.S.A.
Vaccine and Infectious Disease Division and Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A.
Stat Med. 2018 Apr 30;37(9):1439-1453. doi: 10.1002/sim.7608. Epub 2018 Feb 14.
Biomarkers that predict treatment effects may be used to guide treatment decisions, thus improving patient outcomes. A meta-analysis of individual participant data (IPD) is potentially more powerful than a single-study data analysis in evaluating markers for treatment selection. Our study was motivated by the IPD that were collected from 2 randomized controlled trials of hypertension and preeclampsia among pregnant women to evaluate the effect of labor induction over expectant management of the pregnancy in preventing progression to severe maternal disease. The existing literature on statistical methods for biomarker evaluation in IPD meta-analysis have evaluated a marker's performance in terms of its ability to predict risk of disease outcome, which do not directly apply to the treatment selection problem. In this study, we propose a statistical framework for evaluating a marker for treatment selection given IPD from a small number of individual clinical trials. We derive marker-based treatment rules by minimizing the average expected outcome across studies. The application of the proposed methods to the IPD from 2 studies in women with hypertension in pregnancy is presented.
生物标志物可预测治疗效果，有助于指导治疗决策，从而改善患者预后。与针对治疗选择的标志物进行单研究数据分析相比，基于个体参与者数据（individual participant data，IPD）的荟萃分析可能更具说服力。本研究源于两项评估孕妇高血压和子痫前期的随机对照试验的 IPD，旨在评估分娩诱导对妊娠期待管理在预防严重母婴疾病进展方面的效果。目前，关于 IPD 荟萃分析中生物标志物评估的统计方法文献已经评估了标志物预测疾病结局风险的能力，但这些方法并不能直接应用于治疗选择问题。在这项研究中，我们针对从少数个体临床试验中获得的 IPD，提出了一种用于评估治疗选择标志物的统计框架。我们通过最小化研究间的平均预期结局来推导基于标志物的治疗规则。本文介绍了将所提出的方法应用于妊娠高血压妇女的两项研究的 IPD 的情况。
