Yang, Shuting/ SY
(2012)
The Effects of Overstration on the Stratified Log Rank Test for Survival Analysis.
Master's Thesis, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Survival analysis concerns the characterization or comparison of one or more distributions of the time to a well defined event. The log-rank test is the most common method used to compare the survival distributions of two samples. When data within the two groups are stratified according to some risk factors, then a stratified log-rank test is employed.
Stratified analysis is a procedure used to compare outcomes in different groups while at the same time correcting for the effects of confounders. It is one way to ensure that important prognostic factors are equally distributed among different treatments.
The ordinary log-rank test is known to be conservative when treatments have been assigned by a stratified design. The stratified log-rank test is valid even when the sizes of strata differ. Schoenfeld and Tsiatis modified the log-rank test with a variance adjustment reflecting the dependence of survival on strata size. Their method is shown to be more efficient than the ordinary stratified log rank test when the number of strata is large, and it remains valid when the censoring distributions differ across treatment groups.
In this thesis, we investigate these three log-rank tests for survival analysis. The effect of the stratum sizes on each type of analysis is evaluated using simulated data.
Our results show that the modified log rank test is beneficial for stratified survival analysis in most cases especially when there are large numbers of strata and the strata sizes get small. The statistical power of the modified log-rank test is relatively stable even with very small strata sizes and high strata effects.
The public health relevance of this thesis is that the modified log-rank test we investigated and implemented using the R programming language provides an alternative and more efficient way to accommodate higher amounts of stratification in analyzing survival data. More efficient statistical methods indirectly have public health impact as such methods lead to analyses which better identify treatments, interventions or factors that influence health outcomes. Such analyses are commonly used in clinical trials and other studies which influence public health
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
29 June 2012 |
| Date Type: |
Completion |
| Defense Date: |
2 December 2011 |
| Approval Date: |
29 June 2012 |
| Submission Date: |
3 April 2012 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
52 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
survival analysis; modified log-rank test; stratified log-rank test; R |
| Date Deposited: |
29 Jun 2012 18:01 |
| Last Modified: |
15 Nov 2016 13:57 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/11951 |
Available Versions of this Item
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The Effects of Overstration on the Stratified Log Rank Test for Survival Analysis. (deposited 29 Jun 2012 18:01)
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