title: Propensity Score-Based Approaches to Confounding by Indication in Individual Patient Data Meta-Analysis: Non-Standardized Treatment for Multidrug Resistant Tuberculosis.
abstract: BACKGROUND
  In the absence of randomized clinical trials, meta-analysis of individual patient data (IPD) from observational studies may provide the most accurate effect estimates for an intervention. However, confounding by indication remains an important concern that can be addressed by incorporating individual patient covariates in different ways. We compared different analytic approaches to account for confounding in IPD from patients treated for multi-drug resistant tuberculosis (MDR-TB).
  
  
  METHODS
  Two antibiotic classes were evaluated, fluoroquinolones--considered the cornerstone of effective MDR-TB treatment--and macrolides, which are known to be safe, yet are ineffective in vitro. The primary outcome was treatment success against treatment failure, relapse or death. Effect estimates were obtained using multivariable and propensity-score based approaches.
  
  
  RESULTS
  Fluoroquinolone antibiotics were used in 28 included studies, within which 6,612 patients received a fluoroquinolone and 723 patients did not. Macrolides were used in 15 included studies, within which 459 patients received this class of antibiotics and 3,670 did not. Both standard multivariable regression and propensity score-based methods resulted in similar effect estimates for early and late generation fluoroquinolones, while macrolide antibiotics use was associated with reduced treatment success.
  
  
  CONCLUSIONS
  In this individual patient data meta-analysis, standard multivariable and propensity-score based methods of adjusting for individual patient covariates for observational studies yielded produced similar effect estimates. Even when adjustment is made for potential confounding, interpretation of adjusted estimates must still consider the potential for residual bias.
authors.affiliation: Respiratory Epidemiology and Clinical Research Unit, McGill University, 2155 Guy St, Montreal, Quebec, H3H 2R9, Canada.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Fox
authors.affiliation: Respiratory Epidemiology and Clinical Research Unit, McGill University, 2155 Guy St, Montreal, Quebec, H3H 2R9, Canada.
authors.forename: Andrea
authors.initials: A
authors.lastname: Benedetti
authors.affiliation: Harvard Medical School, 641 Huntington Avenue Boston, MA, 02115, United States of America.
authors.forename: Carole D
authors.initials: CD
authors.lastname: Mitnick
authors.affiliation: Department of Epidemiology, Biostatistics & Occupational Health, McGill University, 1020 Pine Avenue West, Montreal, H3A 1A2, Canada.
authors.forename: Madhukar
authors.initials: M
authors.lastname: Pai
authors.affiliation: Respiratory Epidemiology and Clinical Research Unit, McGill University, 2155 Guy St, Montreal, Quebec, H3H 2R9, Canada.
authors.forename: Dick
authors.initials: D
authors.lastname: Menzies
mesh_terms: D000995:Antitubercular Agents / Q000627:therapeutic use*; D015986:Confounding Factors, Epidemiologic; D024841:Fluoroquinolones / Q000627:therapeutic use; D006801:Humans; D018942:Macrolides / Q000627:therapeutic use; D008962:Models, Theoretical; D018579:Patient Selection; D057285:Precision Medicine; D057216:Propensity Score; D016896:Treatment Outcome; D018088:Tuberculosis, Multidrug-Resistant / Q000188:drug therapy*
chemical_list: D000995:Antitubercular Agents; D024841:Fluoroquinolones; D018942:Macrolides