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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.

To evaluate whether progression to a high-risk situation is predictable in women with gestational hypertension (GH) or mild pre-eclampsia (PE) at term. Women with a singleton pregnancy, a fetus in cephalic position, between 36 and 41 weeks of gestation, complicated by GH or mild PE that were managed expectantly, were selected from the HYPITAT trial. We evaluated the predictability of progression to a high-risk situation. Logistic regression was used to determine the predictive value of clinical characteristics or laboratory findings and to generate a prediction model for progression to a high-risk situation. The predictive value of this model was assessed with receiver-operating characteristic (ROC) analysis, calibration and internal validation. We included 703 women, of whom 244 (34.7%) had progression to a high-risk situation. After multivariable analysis, nulliparity (OR 1.87), maternal age (OR 1.05 per year), gestational age (OR 0.88 per week), previous abortion (OR 1.26), ethnicity (OR 2.05 for non-Caucasian ethnicity), diastolic (OR 1.04 per mmHg), systolic blood pressure (OR 1.02 per mmHg) and the laboratory parameters proteinuria, haemoglobin, platelets, uric acid and alanine aminotransferase were included in the final model. The area under the ROC curve of this model was 0.71 (95% CI, 0.67-0.74). Even though the goodness of fit was moderate (P=0.40), internal validation showed the model could hold in the overall population. In the prediction of progression to a high-risk situation, in women with GH or mild PE at term, a distinction can be made between women with a low risk and women with high risk