Often in cancer and other medical studies, the primary response variable is the time to the occurrence of some particular event of interest (e.g. relapse of cancer, death of the patient, etc.). This kind of data is called survival data. The goals of this proposed research are to develop and extend semiparametric Bayesian models and associated full Bayes and empirical Bayes methodologies for the analysis of survival data obtained from various biomedical studies. The semiparametric models and associated methods will be sophisticated enough to deal with survival data in the presence of complex censoring and irregular data- collection monitoring schemes, in the presence of missed clinic visits and with subjects at the risk of recurrent events of different types as well as failure from different causes. Semiparametric models are a popular compromise between the too restrictive parametric and too non-informative nonparametric models. A semiparametric model has a nonparametric part (an unknown function such as a baseline hazard or an intensity function) as well as a parametric part involving a few parameters, such as regression coefficients for explanatory variables. The available prior information on the nonparametric part will be summarized as a stochastic process, called a prior process. The available prior information on the parametric part will be modeled as a prior distribution. The methodology developed during this project will be useful for the analysis of recurrent events data, survival data with competing causes of failures, survival data from the studies with multiple event states par subject, longitudinal data measured via outcome-dependent irregular clinic visits and survival data from multiple quality of life events. Development of models, associated data analytic tools, related computer programs and codes for statistical packages, extensive simulation studies, and diagnostic tools for verifying the key modeling assumptions will play central roles in each specific aim. New and existing methods will be evaluated and compared using analysis of mainly cancer studies data from the published literature and from other sources such as the MUSC Hoolings Cancer Center.
| Martinez, Elvis E; Sinha, Debajyoti; Wang, Wenting et al. (2017) Tests for equivalence of two survival functions: Alternative to the tests under proportional hazards. Stat Methods Med Res 26:75-87 |
| Lipsitz, Stuart R; Fitzmaurice, Garrett M; Sinha, Debajyoti et al. (2017) Efficient Computation of Reduced Regression Models. Am Stat 71:171-176 |
| Gupta, Cherry; Cobre, Juliana; Polpo, Adriano et al. (2016) Semiparametric Bayesian estimation of quantile function for breast cancer survival data with cured fraction. Biom J 58:1164-77 |
| Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti et al. (2016) Approximate median regression for complex survey data with skewed response. Biometrics 72:1336-1347 |
| Lipsitz, Stuart R; Fitzmaurice, Garrett M; Arriaga, Alex et al. (2015) Using the jackknife for estimation in log link Bernoulli regression models. Stat Med 34:444-53 |
| Royal-Thomas, Tamika; McGee, Daniel; Sinha, Debajyoti et al. (2015) Association of maternal blood pressure in pregnancy with blood pressure of their offspring through adolescence. J Perinat Med 43:695-701 |
| Lipsitz, Stuart R; Fitzmaurice, Garrett M; Sinha, Debajyoti et al. (2015) Testing for independence in J×K contingency tables with complex sample survey data. Biometrics 71:832-40 |
| Tang, Yuanyuan; Sinha, Debajyoti; Pati, Debdeep et al. (2015) Bayesian partial linear model for skewed longitudinal data. Biostatistics 16:441-53 |
| Fitzmaurice, Garrett; Lipsitz, Stuart; Natarajan, Sundar et al. (2014) Simple methods of determining confidence intervals for functions of estimates in published results. PLoS One 9:e98498 |
| Lipsitz, Stuart R; Fitzmaurice, Garrett M; Regenbogen, Scott E et al. (2013) Bias correction for the proportional odds logistic regression model with application to a study of surgical complications. J R Stat Soc Ser C Appl Stat 62:233-250 |
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