title: Estimating parsimonious models of longitudinal causal effects using regressions on propensity scores.
abstract: Parsimony is important for the interpretation of causal effect estimates of longitudinal treatments on subsequent outcomes. One method for parsimonious estimates fits marginal structural models by using inverse propensity scores as weights. This method leads to generally large variability that is uncommon in more likelihood-based approaches. A more recent method fits these models by using simulations from a fitted g-computation, but requires the modeling of high-dimensional longitudinal relations that are highly susceptible to misspecification. We propose a new method that, first, uses longitudinal propensity scores as regressors to reduce the dimension of the problem and then uses the approximate likelihood for the first estimates to fit parsimonious models. We demonstrate the methods by estimating the effect of anticoagulant therapy on survival for cancer and non-cancer patients who have inferior vena cava filters.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
authors.forename: Russell T
authors.initials: RT
authors.lastname: Shinohara
authors.forename: Anand K
authors.initials: AK
authors.lastname: Narayan
authors.forename: Kelvin
authors.initials: K
authors.lastname: Hong
authors.forename: Hyun S
authors.initials: HS
authors.lastname: Kim
authors.forename: Josef
authors.initials: J
authors.lastname: Coresh
authors.forename: Michael B
authors.initials: MB
authors.lastname: Streiff
authors.forename: Constantine E
authors.initials: CE
authors.lastname: Frangakis
mesh_terms: D000925:Anticoagulants / Q000008:administration & dosage; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008137:Longitudinal Studies / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000150:complications; D057216:Propensity Score; D016896:Treatment Outcome; D016306:Vena Cava Filters / Q000009:adverse effects
chemical_list: D000925:Anticoagulants
keywords: causal inference; causal models; propensity scores; survival analysis