title: Estimation of Drug Effectiveness by Modeling Three Time-dependent Covariates: An Application to Data on Cardioprotective Medications in the Chronic Dialysis Population.
abstract: In a population of chronic dialysis patients with an extensive burden of cardiovascular disease, estimation of the effectiveness of cardioprotective medication in literature is based on calculation of a hazard ratio comparing hazard of mortality for two groups (with or without drug exposure) measured at a single point in time or through the cumulative metric of proportion of days covered (PDC) on medication. Though both approaches can be modeled in a time-dependent manner using a Cox regression model, we propose a more complete time-dependent metric for evaluating cardioprotective medication efficacy. We consider that drug effectiveness is potentially the result of interactions between three time-dependent covariate measures, current drug usage status (ON versus OFF), proportion of cumulative exposure to drug at a given point in time, and the patient's switching behavior between taking and not taking the medication. We show that modeling of all three of these time-dependent measures illustrates more clearly how varying patterns of drug exposure affect drug effectiveness, which could remain obscured when modeled by the more standard single time-dependent covariate approaches. We propose that understanding the nature and directionality of these interactions will help the biopharmaceutical industry in better estimating drug efficacy.
authors.affiliation: Department of Biostatistics, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Milind A
authors.initials: MA
authors.lastname: Phadnis
authors.affiliation: Department of Preventive Medicine and Public Health, University of Kansas School of Medicine, Kansas City, KS, USA ; The Landon Center on Aging, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Theresa I
authors.initials: TI
authors.lastname: Shireman
authors.affiliation: Department of Medicine, Division of Nephrology and Hypertension, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: James B
authors.initials: JB
authors.lastname: Wetmore
authors.affiliation: Department of Medicine, University of Kansas School of Medicine, Kansas City, KS, USA ; The Landon Center on Aging, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Sally K
authors.initials: SK
authors.lastname: Rigler
authors.affiliation: Department of Biostatistics, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Xinhua
authors.initials: X
authors.lastname: Zhou
authors.affiliation: Saint Luke's Mid America Heart Institute, Kansas City, MO, USA ; University of Missouri-Kansas City, Kansas City, MO, USA.
authors.forename: John A
authors.initials: JA
authors.lastname: Spertus
authors.affiliation: Department of Preventive Medicine and Public Health, University of Kansas School of Medicine, Kansas City, KS, USA ; Department of Medicine, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Edward F
authors.initials: EF
authors.lastname: Ellerbeck
authors.affiliation: Department of Biostatistics, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Mahnken
keywords: Markov model; anti-hypertensive drugs; drug interactions; medication patterns; state transition model