Non-Parametric Estimation Of Probability In Disease States, Restricted Mean Time In Disease States, And Mean Cumulative Marker Process
2020-05
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Non-Parametric Estimation Of Probability In Disease States, Restricted Mean Time In Disease States, And Mean Cumulative Marker Process
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2020-05
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Many clinical studies focus on time to event outcomes, which pose unique analysis and interpretation challenges due to incomplete observation. This dissertation focuses on novel endpoints for time to event variables, some of which integrate additional outcomes such as non-fatal events or other measures of patients' health state while alive. The most commonly-used measure of treatment benefit for time to event outcomes, the hazard ratio, can be hard to interpret, especially under non-proportional hazards. We present theoretical results and software tools to help investigators plan trials using the difference in restricted mean survival time as the primary endpoint, which can be interpreted as the years of life gained by taking the treatment, versus control, out of the next fixed number of years. In some disease settings, researchers wish to study a composite endpoint defined as the time to the earliest of either death or a non-fatal event (for example, cancer progression). However, if death is right censored and cancer progression is measured periodically at visits, the resulting composite displays a mix of interval censoring and right censoring known as component-wise censoring, and standard survival analysis methods cannot be applied. We propose novel estimators for the event free survival curve and the restricted mean event free survival time using component-wise censored data by combining existing non-parametric estimators to circumvent the component-wise censoring problem. We derive the large sample properties of the estimators and compare their performance to standard methods using a simulation study. Finally, our proposed estimators require independence between the visit process and the clinical outcomes of interest, which may be violated in some real datasets. We use inverse visit rate weights to develop estimators that work under a relaxed assumption, that the visit rate is conditionally independent of the clinical outcomes, given (possibly unobserved) baseline covariates. We explore the performance of our proposed estimators under various scenarios.
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University of Minnesota Ph.D. dissertation. May 2020. Major: Biostatistics. Advisor: Xianghua Luo. 1 computer file (PDF); x, 140 pages.
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Eaton, Anne. (2020). Non-Parametric Estimation Of Probability In Disease States, Restricted Mean Time In Disease States, And Mean Cumulative Marker Process. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215150.
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