title: Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach.
abstract: Propensity score (Pscore) matching and inverse probability of treatment weighting (IPTW) can remove bias due to observed confounders, if the Pscore is correctly specified. Genetic Matching (GenMatch) matches on the Pscore and individual covariates using an automated search algorithm to balance covariates. This paper compares common ways of implementing Pscore matching and IPTW, with Genmatch for balancing time-constant baseline covariates}. The methods are considered when estimates of treatment effectiveness are required for patient subgroups, and the treatment allocation process differs by subgroup. We apply these methods in a prospective cohort study that estimates the effectiveness of Drotrecogin alfa activated, for subgroups of patients with severe sepsis. In a simulation study we compare the methods when the Pscore is correctly specified, and then misspecified by ignoring the subgroup-specific treatment allocation. The simulations also consider poor overlap in baseline covariates, and different sample sizes. In the case study, GenMatch reports better covariate balance than IPTW or Pscore matching. In the simulations with correctly specified Pscores, good overlap and reasonable sample sizes, all methods report minimal bias. When the Pscore is misspecified, GenMatch reports the least imbalance and bias. With small sample sizes, IPTW is the most efficient approach, but all methods report relatively high bias of treatment effects. This study shows that overall GenMatch achieves the best covariate balance for each subgroup, and is more robust to Pscore misspecification than common alternative Pscore approaches.
authors.affiliation: CLondon School of Hygiene & Tropical Medicine, UK.
authors.forename: Rosalba
authors.initials: R
authors.lastname: Radice
authors.forename: Roland
authors.initials: R
authors.lastname: Ramsahai
authors.forename: Richard
authors.initials: R
authors.lastname: Grieve
authors.forename: Noemi
authors.initials: N
authors.lastname: Kreif
authors.forename: Zia
authors.initials: Z
authors.lastname: Sadique
authors.forename: Jasjeet S
authors.initials: JS
authors.lastname: Sekhon
mesh_terms: D000368:Aged; D000890:Anti-Infective Agents / Q000627:therapeutic use; D001331:Automation; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D008875:Middle Aged; D009010:Monte Carlo Method; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data*; D057216:Propensity Score; D011446:Prospective Studies; D011486:Protein C / Q000627:therapeutic use; D011994:Recombinant Proteins / Q000627:therapeutic use; D018805:Sepsis / Q000188:drug therapy; D012720:Severity of Illness Index
chemical_list: D000890:Anti-Infective Agents; D011486:Protein C; D011994:Recombinant Proteins; C421124:drotrecogin alfa activated