title: The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.
abstract: BACKGROUND
  Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach.
  
  
  METHODS
  We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study.
  
  
  RESULTS
  Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC.
  
  
  CONCLUSIONS
  This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.
authors.affiliation: Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, P.O. Box 71345-1874, Shiraz, Iran.
authors.forename: Tania
authors.initials: T
authors.lastname: Dehesh
authors.affiliation: Department of Biostatistics, Infertility Research Center, Shiraz University of Medical Sciences, P.O. Box 71345-1874, Shiraz, Iran.
authors.forename: Najaf
authors.initials: N
authors.lastname: Zare
authors.affiliation: Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, P.O. Box 71345-1874, Shiraz, Iran.
authors.forename: Seyyed Mohammad Taghi
authors.initials: SM
authors.lastname: Ayatollahi
mesh_terms: D015982:Bias; D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D016018:Least-Squares Analysis; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012307:Risk Factors