title: An ensemble survival model for estimating relative residual longevity following stroke: Application to mortality data in the chronic dialysis population.
abstract: Time-dependent covariates can be modeled within the Cox regression framework and can allow both proportional and nonproportional hazards for the risk factor of research interest. However, in many areas of health services research, interest centers on being able to estimate residual longevity after the occurrence of a particular event such as stroke. The survival trajectory of patients experiencing a stroke can be potentially influenced by stroke type (hemorrhagic or ischemic), time of the stroke (relative to time zero), time since the stroke occurred, or a combination of these factors. In such situations, researchers are more interested in estimating lifetime lost due to stroke rather than merely estimating the relative hazard due to stroke. To achieve this, we propose an ensemble approach using the generalized gamma distribution by means of a semi-Markov type model with an additive hazards extension. Our modeling framework allows stroke as a time-dependent covariate to affect all three parameters (location, scale, and shape) of the generalized gamma distribution. Using the concept of relative times, we answer the research question by estimating residual life lost due to ischemic and hemorrhagic stroke in the chronic dialysis population.
authors.affiliation: 1 Departments of Biostatistics, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Milind A
authors.initials: MA
authors.lastname: Phadnis
authors.affiliation: 2 Division of Nephrology, Hennepin County Medical Center, Minneapolis, MN, USA.|3 Chronic Disease Research Group, Hennepin County Medical Center, Minneapolis, MN, USA.
authors.forename: James B
authors.initials: JB
authors.lastname: Wetmore
authors.affiliation: 4 Preventive Medicine and Public Health, University of Kansas School of Medicine, Kansas City, KS, USA.|5 The Kidney Institute, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Theresa I
authors.initials: TI
authors.lastname: Shireman
authors.affiliation: 4 Preventive Medicine and Public Health, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Edward F
authors.initials: EF
authors.lastname: Ellerbeck
authors.affiliation: 1 Departments of Biostatistics, University of Kansas School of Medicine, Kansas City, KS, USA.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Mahnken
mesh_terms: D056808:Biostatistics / Q000379:methods; D006801:Humans; D008136:Longevity; D008390:Markov Chains; D015233:Models, Statistical; D016016:Proportional Hazards Models; D006435:Renal Dialysis / Q000009:adverse effects / Q000401:mortality*; D012307:Risk Factors; D020521:Stroke / Q000209:etiology / Q000401:mortality*; D016019:Survival Analysis
keywords: Additive hazards; generalized gamma; relative times; residual median life; semi-Markov model