title: Meta-regression detected associations between heterogeneous treatment effects and study-level, but not patient-level, factors.
abstract: OBJECTIVE
  Two investigations evaluate Bayesian meta-regression for detecting treatment interactions.
  
  
  METHODS
  The first compares analyses of aggregate and individual patient data on 1,860 subjects from 11 trials testing angiotensin converting enzyme (ACE) inhibitors for nondiabetic kidney disease. The second explores meta-regression for detecting treatment interaction on 671 covariates, including the baseline risk, from 232 meta-analyses of binary outcomes compiled from the Cochrane Collaboration and the medical literature.
  
  
  RESULTS
  In the ACE inhibitor study, treatment effects were homogeneous so meta-regression identified no interactions. Analysis of individual patient data using a multilevel model, however, discovered that treatment reduced glomerular filtration rate (GFR) more among patients with higher baseline proteinuria. The second investigation found meta-regression most effective for detecting treatment interactions with study-level factors in meta-analyses with >10 studies, heterogeneous treatment effects, or significant overall treatment effects. Under all three conditions, 46% of meta-regressions produced strong interactions (posterior probability >0.995) compared with 6% otherwise. Baseline risk was associated with the odds ratio in 6% of meta-analyses, half the rate found using maximum likelihood.
  
  
  CONCLUSIONS
  Meta-regression can detect interactions of treatment with study-level factors when treatment effects are heterogeneous. Individual patient data are needed for patient-level factors and homogeneous effects.
authors.affiliation: Institute for Clinical Research and Health Policy Studies, Tufts-New England Medical Center and Tufts University School of Medicine, 750 Washington Street, Boston, MA 02111, USA. cschmid@tufts-nemc.org
authors.forename: Christopher H
authors.initials: CH
authors.lastname: Schmid
authors.forename: Paul C
authors.initials: PC
authors.lastname: Stark
authors.forename: Jesse A
authors.initials: JA
authors.lastname: Berlin
authors.forename: Paul
authors.initials: P
authors.lastname: Landais
authors.forename: Joseph
authors.initials: J
authors.lastname: Lau
mesh_terms: D000328:Adult; D000465:Algorithms; D000806:Angiotensin-Converting Enzyme Inhibitors / Q000627:therapeutic use; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D007674:Kidney Diseases / Q000188:drug therapy; D008297:Male; D015201:Meta-Analysis as Topic; D008875:Middle Aged; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012044:Regression Analysis; D016896:Treatment Outcome
chemical_list: D000806:Angiotensin-Converting Enzyme Inhibitors