title: Systematic review of methods for individual patient data meta- analysis with binary outcomes.
abstract: BACKGROUND
  Meta-analyses (MA) based on individual patient data (IPD) are regarded as the gold standard for meta-analyses and are becoming increasingly common, having several advantages over meta-analyses of summary statistics. These analyses are being undertaken in an increasing diversity of settings, often having a binary outcome. In a previous systematic review of articles published between 1999-2001, the statistical approach was seldom reported in sufficient detail, and the outcome was binary in 32% of the studies considered. Here, we explore statistical methods used for IPD-MA of binary outcomes only, a decade later.
  
  
  METHODS
  We selected 56 articles, published in 2011 that presented results from an individual patient data meta-analysis. Of these, 26 considered a binary outcome. Here, we review 26 IPD-MA published during 2011 to consider: the goal of the study and reason for conducting an IPD-MA, whether they obtained all the data they sought, the approach used in their analysis, for instance, a two-stage or a one stage model, and the assumption of fixed or random effects. We also investigated how heterogeneity across studies was described and how studies investigated the effects of covariates.
  
  
  RESULTS
  19 of the 26 IPD-MA used a one-stage approach. 9 IPD-MA used a one-stage random treatment-effect logistic regression model, allowing the treatment effect to vary across studies. Twelve IPD-MA presented some form of statistic to measure heterogeneity across studies, though these were usually calculated using two-stage approach. Subgroup analyses were undertaken in all IPD-MA that aimed to estimate a treatment effect or safety of a treatment,. Sixteen meta-analyses obtained 90% or more of the patients sought.
  
  
  CONCLUSIONS
  Evidence from this systematic review shows that the use of binary outcomes in assessing the effects of health care problems has increased, with random effects logistic regression the most common method of analysis. Methods are still often not reported in enough detail. Results also show that heterogeneity of treatment effects is discussed in most applications.
authors.forename: Doneal
authors.initials: D
authors.lastname: Thomas
authors.forename: Sanyath
authors.initials: S
authors.lastname: Radji
authors.affiliation: Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada. andrea.benedetti@mcgill.ca.
authors.forename: Andrea
authors.initials: A
authors.lastname: Benedetti
mesh_terms: D001699:Biometry; D003627:Data Interpretation, Statistical; D055991:Health Records, Personal; D006801:Humans; D016015:Logistic Models; D012107:Research Design; D016896:Treatment Outcome