title: Meta-analysis of a continuous outcome combining individual patient data and aggregate data: a method based on simulated individual patient data.
abstract: When some trials provide individual patient data (IPD) and the others provide only aggregate data (AD), meta-analysis methods for combining IPD and AD are required. We propose a method that reconstructs the missing IPD for AD trials by a Bayesian sampling procedure and then applies an IPD meta-analysis model to the mixture of simulated IPD and collected IPD. The method is applicable when a treatment effect can be assumed fixed across trials. We focus on situations of a single continuous outcome and covariate and aim to estimate treatment-covariate interactions separated into within-trial and across-trial effect. An illustration with hypertension data which has similar mean covariates across trials indicates that the method substantially reduces mean square error of the pooled within-trial interaction estimate in comparison with existing approaches. A simulation study supposing there exists one IPD trial and nine AD trials suggests that the method has suitable type I error rate and approximately zero bias as long as the available IPD contains at least 10% of total patients, where the average gain in mean square error is up to about 40%. However, the method is currently restricted by the fixed effect assumption, and extension to random effects to allow heterogeneity is required.
authors.affiliation: Division of Mathematical Science, Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan.
authors.forename: Yusuke
authors.initials: Y
authors.lastname: Yamaguchi
authors.affiliation: Division of Human Ecology, Graduate School of Environmental and Life Science, Okayama University, 3-1-1, Tsushima-naka, Kita-ku, Okayama, 700-8530, Japan.
authors.forename: Wataru
authors.initials: W
authors.lastname: Sakamoto
authors.affiliation: NPO, Biostatistical Research Association, 2-22-10-A411, Kamishinden, Toyonaka, Osaka, 560-0085, Japan.
authors.forename: Masashi
authors.initials: M
authors.lastname: Goto
authors.affiliation: Studies Coordinating Centre, Division of Hypertension and Cardiovascular Rehabilitation, Department of Cardiovascular Diseases, University of Leuven, Campus Gasthuisberg, Herestraat 49/702, B-3000, Leuven, Belgium.|Department of Epidemiology, Maastricht University, Maastricht, Netherlands.
authors.forename: Jan A
authors.initials: JA
authors.lastname: Staessen
authors.affiliation: Centre for Epidemiological Studies and Clinical Trials, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Ruijin 2nd Road 197, Shanghai, 200025, China.
authors.forename: Jiguang
authors.initials: J
authors.lastname: Wang
authors.affiliation: INSERM, CIC201, Lyon, F-69000, France.
authors.forename: Francois
authors.initials: F
authors.lastname: Gueyffier
authors.affiliation: Public Health, Epidemiology and Biostatistics, Public Health Building, University of Birmingham, Edgbaston, B15 2TT, UK.
authors.forename: Richard D
authors.initials: RD
authors.lastname: Riley
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity
keywords: individual patient data; meta-analysis; statistical simulation; treatment-covariate interaction