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Nonparametric and semiparametric inference on quantile lost lifespan

Balmert, Lauren (2017) Nonparametric and semiparametric inference on quantile lost lifespan. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

A new summary measure for time-to-event data, termed lost lifespan, is proposed in which the existing concept of reversed percentile residual life, or percentile inactivity time, is recast to show that it can be used for routine analysis to summarize life lost. The lost lifespan infers the distribution of time lost due to experiencing an event of interest before some specified time point. An estimating equation approach is adopted to avoid estimation of the probability density function of the underlying time-to-event distribution to estimate the variance of the quantile estimator. A K-sample test statistic is proposed to test the ratio of quantile lost lifespans. Simulation studies are performed to assess finite properties of the proposed statistic in terms of coverage probability and power. The concept of life lost is then extended to a regression setting to analyze covariate effects on the quantiles of the distribution of the lost lifespan under right censoring. An estimating equation, variance estimator, and minimum dispersion statistic for testing the significance of regression parameters are proposed and evaluated via simulation studies. The proposed approach reveals several advantages over existing methods for analyzing time-to-event data, which is illustrated with a breast cancer dataset from a Phase III clinical trial conducted by the National Surgical Adjuvant Breast and Bowel Project.
Public Health Significance: The analysis of time-to-event data can provide important information about new treatments and therapies, particularly in clinical trial settings. The methods provided in this dissertation will allow public health researchers to analyze effectiveness of new treatments in terms of a new summary measure, life loss. In addition to providing statistical advantages over existing methods, analyzing time-to-event data in terms of the lost lifespan provides a more straightforward interpretation beneficial to clinicians, patients, and other stakeholders.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Balmert, Laurenlab165@pitt.edulab165
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairJeong, Jongjeong@pitt.edu
Committee MemberBuchanich, Jeaninejeanine@pitt.edu
Committee MemberDing, Yingyingding@pitt.edu
Committee MemberCheng, Yuyucheng@pitt.edu
Date: 29 June 2017
Date Type: Publication
Defense Date: 5 April 2017
Approval Date: 29 June 2017
Submission Date: 31 March 2017
Access Restriction: 3 year -- Restrict access to University of Pittsburgh for a period of 3 years.
Number of Pages: 81
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: lost lifespan; residual life; survival analysis; time-to-event; right censoring
Date Deposited: 29 Jun 2017 23:37
Last Modified: 01 May 2020 05:15
URI: http://d-scholarship.pitt.edu/id/eprint/31114

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