The Kaplan Meier estimator, the Iogrank test, and the proportional hazards model are reported in an extremely high percentage of oncology articles in the premier medical journals. There is good reason for this orthodoxy. Their theoretical properties are well understood, they are robust, and they are available in standard statistical software. Unfortunately, the methods are based on strong simplifying assumptions and difficulties may arise when the assumptions are violated. The proposed research will investigate three such topics arising in cancer and other chronic disease studies. The unifying theme is the novel application of semiparametric frailty models to address the limitations of the existing methods. In practice, it is often the case that a terminating event (death or drop out) censors a non terminating event (morbidity), but not vice versa. The Kaplan Meier estimator for the terminating event may not be valid if censoring by the terminating event is informative.
In Aim 1, I will use a frailty model to estimate the joint distribution of the events. The approach is valuable in evaluating the strength and marginal distribution of surrogate endpoints. In medical studies, predictive models based on the proportional hazards assumption are invariably misspecified. The may include prognostic factors which are dichotomized for ease of interpretation and may omit covariates which are scientifically relevant.
In Aim 2, I will consider the robustness of inferences for a general class of univariate proportional hazards frailty regression models which are more flexible than the standard model but may still be misspecified. In population based studies, residual familial correlations after controlling for environmental risk factors may be indicative of a genetic etiology. Multivariate models which incorporate covariates via proportional hazards assumptions are popular. However, the approach may not be appropriate when the regression model is incorrect, as occurs when important covariates are omitted.
In Aim 3, I will propose a general class of multivariate non-proportional hazards frailty regression models which are robust to such misspecification. In all of the Aims, existing methods for frailty models are unsuitable for the proposed applications and original methodology or theoretical justification is needed. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA094893-02
Application #
6770147
Study Section
Social Sciences, Nursing, Epidemiology and Methods 4 (SNEM)
Program Officer
Tiwari, Ram C
Project Start
2003-07-01
Project End
2006-06-30
Budget Start
2004-07-01
Budget End
2005-06-30
Support Year
2
Fiscal Year
2004
Total Cost
$136,208
Indirect Cost
Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Li, Jialiang; Jiang, Binyan; Fine, Jason P (2013) Multicategory reclassification statistics for assessing improvements in diagnostic accuracy. Biostatistics 14:382-94
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Cheng, Yu; Fine, Jason P (2012) Cumulative Incidence Association Models for Bivariate Competing Risks Data. J R Stat Soc Series B Stat Methodol 74:183-202
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Zhang, Xu; Zhang, Mei-Jie; Fine, Jason (2011) A proportional hazards regression model for the subdistribution with right-censored and left-truncated competing risks data. Stat Med 30:1933-51
Li, Jialiang; Fine, Jason P (2011) Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis. Biostatistics 12:710-22
Zhou, Bingqing; Latouche, Aurelien; Rocha, Vanderson et al. (2011) Competing risks regression for stratified data. Biometrics 67:661-70
Todem, D; Fine, J; Peng, L (2010) A global sensitivity test for evaluating statistical hypotheses with nonidentifiable models. Biometrics 66:558-66
Todem, David; Kim, Kyungmann; Fine, Jason et al. (2010) Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts. Stat Neerl 64:133-156
Yan, Jun; Cheng, Yu; Fine, Jason P et al. (2010) Uncovering symptom progression history from disease registry data with application to young cystic fibrosis patients. Biometrics 66:594-602

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