Meta-analysis can be broadly defined as the quantitative review and synthesis of the results of related but independent studies into a single overall result. It is a statistical analysis that combines or integrates the results of several independent clinical trials considered by the analyst to be "combinable". In many biomedical research areas, especially clinical trials in oncology, researchers often use time to some event (or death) as the primary endpoint to assess treatment effects. As the amount of survival analyses continues to increase, there is a greater need to summarize a pool of studies into a coherent overview.
It is well established that in Cox proportional hazard models with censored survival data, estimates of treatment effects with some important covariates omitted will be biased toward zero. This is especially problematic in meta-analyses which combine estimates of parameters from studies where different covariate adjustments are made. Presently, few constructive solutions have been provided to address this issue. We propose a meta-analytic framework for combining incomparable Cox models using both aggregated patient data (APD) and individual patient data (IPD) structures. For APD, two meta-regression models (meta-ANOVA and meta-polynomial models) with indicators of different covariates in Cox models are proposed to adjust for the heterogeneity of treatment effects across studies. Both parametric and nonparametric estimators for the pooled treatment effect and the heterogeneity variance are presented and compared. For IPD, we propose a hierarchical multiple imputation method to handle the unique missing covariates problem when we combine individual data from different studies for a meta-analysis, and results are compared with estimations from the conventional multiple imputation method. We illustrate the advantages of our proposed analytic procedures over existing methodologies by simulation studies and real data analyses using multiple breast cancer clinical trials.
The public health significance of our work is to provide practical guidance of designing and implementing meta-analyses of incomparable Cox proportional hazard models for researchers in the fields of clinical trials, medical research, and other health care areas. Such guidance is important due to the emerging role of meta-analysis in assessing important public health studies.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-11272009-231440 |
| Date | 27 January 2010 |
| Creators | Yuan, Xing |
| Contributors | Garrick L. Wallstrom, Jong-Hyeon Jeong, Stewart J. Anderson, Joseph P. Costantino |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-11272009-231440/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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