title: Estimation of conditional and marginal odds ratios using the prognostic score.
abstract: Introduced by Hansen in 2008, the prognostic score (PGS) has been presented as 'the prognostic analogue of the propensity score' (PPS). PPS-based methods are intended to estimate marginal effects. Most previous studies evaluated the performance of existing PGS-based methods (adjustment, stratification and matching using the PGS) in situations in which the theoretical conditional and marginal effects are equal (i.e., collapsible situations). To support the use of PGS framework as an alternative to the PPS framework, applied researchers must have reliable information about the type of treatment effect estimated by each method. We propose four new PGS-based methods, each developed to estimate a specific type of treatment effect. We evaluated the ability of existing and new PGS-based methods to estimate the conditional treatment effect (CTE), the (marginal) average treatment effect on the whole population (ATE), and the (marginal) average treatment effect on the treated population (ATT), when the odds ratio (a non-collapsible estimator) is the measure of interest. The performance of PGS-based methods was assessed by Monte Carlo simulations and compared with PPS-based methods and multivariate regression analysis. Existing PGS-based methods did not allow for estimating the ATE and showed unacceptable performance when the proportion of exposed subjects was large. When estimating marginal effects, PPS-based methods were too conservative, whereas the new PGS-based methods performed better with low prevalence of exposure, and had coverages closer to the nominal value. When estimating CTE, the new PGS-based methods performed as well as traditional multivariate regression. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: APHP, Hôpital Pitié-Salpêtrière, Département de Biostatistiques, Santé publique et Information médicale, Paris, F-75013, France.|APHP, Centre de Pharmacoépidémiologie (Cephepi), Paris, F-75013, France.|Univ Paris Diderot, Sorbonne Paris Cité, UMR 1123 ECEVE, Paris, F-75010, France.|INSERM, UMR 1123 ECEVE, Paris, F-75018, France.
authors.forename: David
authors.initials: D
authors.lastname: Hajage
authors.affiliation: APHP, Hôpital Pitié-Salpêtrière, Département de Biostatistiques, Santé publique et Information médicale, Paris, F-75013, France.|APHP, Centre de Pharmacoépidémiologie (Cephepi), Paris, F-75013, France.|Univ Paris Diderot, Sorbonne Paris Cité, UMR 1123 ECEVE, Paris, F-75010, France.|INSERM, UMR 1123 ECEVE, Paris, F-75018, France.
authors.forename: Yann
authors.initials: Y
authors.lastname: De Rycke
authors.affiliation: Ecole Nationale de la Statistique et de IAnalyse de l'Information (ENSAI), Bruz, F-35170, France.|IRMAR, UMR CNRS 6625, Rennes, France.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Chauvet
authors.affiliation: APHP, Hôpital Pitié-Salpêtrière, Département de Biostatistiques, Santé publique et Information médicale, Paris, F-75013, France.|APHP, Centre de Pharmacoépidémiologie (Cephepi), Paris, F-75013, France.|Université Pierre et Marie Curie Ű Paris 6, Sorbonne Universités, Paris, France.|INSERM, UMR 1123 ECEVE, Paris, F-75018, France.
authors.forename: Florence
authors.initials: F
authors.lastname: Tubach
mesh_terms: D000293:Adolescent; D000328:Adult; D018927:Anti-Asthmatic Agents / Q000627:therapeutic use; D001249:Asthma / Q000175:diagnosis / Q000188:drug therapy; D003362:Cost-Benefit Analysis; D005260:Female; D006801:Humans; D008297:Male; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D064887:Observational Studies as Topic; D016017:Odds Ratio; D011379:Prognosis; D057216:Propensity Score; D011446:Prospective Studies; D013223:Statistics as Topic; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D018927:Anti-Asthmatic Agents
keywords: causal inference; confounding; observational study; prognostic score; propensity score