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Inference for Right Censored, and Right Censored Length Biased Data Through Inverse Weighting

Emeremni, Chetachi (2012) Inference for Right Censored, and Right Censored Length Biased Data Through Inverse Weighting. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

In many medical studies, the outcome of interest may be the time from a starting point to a predefined specific event. A key feature of these data is that the complete event times may not be completely known for some subjects. When this occurs, the survival times are said to be censored. When observations are right censored, all that is known for such individuals is that their event time is greater than some given value. Analysis of variance has been one of the most powerful statistical tools for comparing mean continuous response across multiple groups. Use of classical ANOVA in time-to-event data is problematic because of the right censored nature of survival times.
In this dissertation, we propose a weighted analysis of variance approach to comparing mean continuous response between groups when the outcome is subject to right censoring. The method weights each observation by the inverse of the probability of being censored. We show that classical ANOVA methods such as decomposition of sums of squares and tests of contrasts follow in the weighted ANOVA setting. Simulation results show that the weighted ANOVA could be a comparable alternative to other methods of analyzing survival data. We apply our methods to a dataset from the North Central Cancer Treatment Group and another from the Radiation Therapy Oncology Group.
Length-Biased sampling is statistical artifact that occurs in survival analysis when the probability of an observation being included in the sample is proportional to a particular characteristic of that observation. When the length-biased data is subject to right censoring, the inference is biased if these key features of the data are not accounted for in the analysis.
In this dissertation, we propose an estimating equation approach to eliminate the bias introduced by censoring and the unequal probability of inclusion using inverse weighting. Simulation results show that the estimator is a simple and effective method of analyzing these types of survival data. We apply our methods to the Channing House data.
The mean survival time is often used as a measure of effectiveness in screening and other public health programs. When the data are right censored and/or length-biased, the mean survival time is a biased estimator of the population mean. This work is of important public health significance because it provides an effective method of comparing health populations using the mean survival time as the measure of effectiveness.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Emeremni, Chetachiche7@pitt.eduCHE7
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWahed, Abdus S.wahedA@edc.pitt.eduWAHED
Committee MemberMazumdar, Satimaz1@pitt.eduMAZ1
Committee MemberJong-Hyeon, Jeongjeong@nsabp.pitt.edu JJEONG
Committee MemberMoore, Charitymoorecg@upmc.edu
Date: 24 September 2012
Date Type: Completion
Defense Date: 19 July 2012
Approval Date: 24 September 2012
Submission Date: 23 July 2012
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 104
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: Analysis of variance, Survival Analysis, Censoring, Inverse probability of censoring weighting, Length bias, Inverse probability weighting, Kaplan-Meier, Proportional hazard, Accelerated failure time
Date Deposited: 24 Sep 2012 15:55
Last Modified: 15 Nov 2016 14:03
URI: http://d-scholarship.pitt.edu/id/eprint/14074

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  • Inference for Right Censored, and Right Censored Length Biased Data Through Inverse Weighting. (deposited 24 Sep 2012 15:55) [Currently Displayed]

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