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Full-Text Articles in Biostatistics
Cox Regression Models With Functional Covariates For Survival Data, Jonathan E. Gellar, Elizabeth Colantuoni, Dale M. Needham, Ciprian M. Crainiceanu
Cox Regression Models With Functional Covariates For Survival Data, Jonathan E. Gellar, Elizabeth Colantuoni, Dale M. Needham, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge …
The Use Of Propensity Score Methods With Survival Or Time-To-Event Outcomes: Reporting Measures Of Effect Similar To Those Used In Randomized Experiments, Peter C. Austin
The Use Of Propensity Score Methods With Survival Or Time-To-Event Outcomes: Reporting Measures Of Effect Similar To Those Used In Randomized Experiments, Peter C. Austin
Peter Austin
Propensity score methods are increasingly being used to estimate causal treatment effects in observational studies. In medical and epidemiological studies, outcomes are frequently time-to-event in nature. Propensity-score methods are often applied incorrectly when estimating the effect of treatment on time-to-event outcomes. This article describes how two different propensity score methods (matching and inverse probability of treatment weighting) can be used to estimate the measures of effect that are frequently reported in randomized controlled trials: (i) marginal survival curves, which describe survival in the population if all subjects were treated or if all subjects were untreated; and (ii) marginal hazard ratios. …
The Performance Of Different Propensity Score Methods For Estimating Absolute Effects Of Treatments On Survival Outcomes: A Simulation Study, Peter C. Austin
The Performance Of Different Propensity Score Methods For Estimating Absolute Effects Of Treatments On Survival Outcomes: A Simulation Study, Peter C. Austin
Peter Austin
Observational studies are increasingly being used to estimate the effect of treatments, interventions and exposures on outcomes that can occur over time. Historically, the hazard ratio, which is a relative measure of effect, has been reported. However, medical decision making is best informed when both relative and absolute measures of effect are reported. When outcomes are time-to-event in nature, the effect of treatment can also be quantified as the change in mean or median survival time due to treatment and the absolute reduction in the probability of the occurrence of an event within a specified duration of follow-up. We describe …
Survival Prediction For Brain Tumor Patients Using Gene Expression Data, Vinicius Bonato
Survival Prediction For Brain Tumor Patients Using Gene Expression Data, Vinicius Bonato
Dissertations & Theses (Open Access)
Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. …