BIROn - Birkbeck Institutional Research Online

    Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach

    Radice, Rosalba and Ramsahai, R. and Grieve, R. and Kreif, N. and Sadique, Z. and Sekhon, J.S. (2012) Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach. International Journal of Biostatistics 8 (1), ISSN 1557-4679.

    Full text not available from this repository.

    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.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): confounding, observational studies, matching, propensity score methods, subgroup analysis
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Sarah Hall
    Date Deposited: 15 Apr 2014 14:38
    Last Modified: 15 Apr 2014 14:38
    URI: http://eprints.bbk.ac.uk/id/eprint/9613

    Statistics

    Downloads
    Activity Overview
    0Downloads
    202Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item