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 Title
 Tests for equivalence of two survival functions: Alternative to the tests under proportional hazards..
 Creator

Martinez, Elvis E, Sinha, Debajyoti, Wang, Wenting, Lipsitz, Stuart R, Chappell, Richard J
 Abstract/Description

For either the equivalence trial or the noninferiority trial with survivor outcomes from two treatment groups, the most popular testing procedure is the extension (e.g., Wellek, A logrank test for equivalence of two survivor functions, Biometrics, 1993; 49: 877881) of logrank based test under proportional hazards model. We show that the actual type I error rate for the popular procedure of Wellek is higher than the intended nominal rate when survival responses from two treatment arms...
Show moreFor either the equivalence trial or the noninferiority trial with survivor outcomes from two treatment groups, the most popular testing procedure is the extension (e.g., Wellek, A logrank test for equivalence of two survivor functions, Biometrics, 1993; 49: 877881) of logrank based test under proportional hazards model. We show that the actual type I error rate for the popular procedure of Wellek is higher than the intended nominal rate when survival responses from two treatment arms satisfy the proportional odds survival model. When the true model is proportional odds survival model, we show that the hypothesis of equivalence of two survival functions can be formulated as a statistical hypothesis involving only the survival odds ratio parameter. We further show that our new equivalence test, formulation, and related procedures are applicable even in the presence of additional covariates beyond treatment arms, and the associated equivalence test procedures have correct type I error rates under the proportional hazards model as well as the proportional odds survival model. These results show that use of our test will be a safer statistical practice for equivalence trials of survival responses than the commonly used logrank based tests.
Show less  Date Issued
 20170201
 Identifier
 FSU_pmch_24925887, 10.1177/0962280214539282, PMC5557049, 24925887, 24925887, 0962280214539282
 Format
 Citation
 Title
 Approximate median regression for complex survey data with skewed response.
 Creator

Fraser, Raphael André, Lipsitz, Stuart R, Sinha, Debajyoti, Fitzmaurice, Garrett M, Pan, Yi
 Abstract/Description

The ready availability of publicuse data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling, and...
Show moreThe ready availability of publicuse data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling, and weighting. In this article, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a doubletransformbothsides (DTBS)'based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same BoxCox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudolikelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey.
Show less  Date Issued
 20161201
 Identifier
 FSU_pmch_27062562, 10.1111/biom.12517, PMC5055849, 27062562, 27062562
 Format
 Citation
 Title
 Exact Bayesian pvalues for a test of independence in a 2 × 2 contingency table with missing data.
 Creator

Lin, Yan, Lipsitz, Stuart R, Sinha, Debajyoti, Fitzmaurice, Garrett, Lipshultz, Steven
 Abstract/Description

Altham (Altham PME. Exact Bayesian analysis of a 2 × 2 contingency table, and Fisher's "exact" significance test. J R Stat Soc B 1969; 31: 261269) showed that a onesided pvalue from Fisher's exact test of independence in a 2 × 2 contingency table is equal to the posterior probability of negative association in the 2 × 2 contingency table under a Bayesian analysis using an improper prior. We derive an extension of Fisher's exact test pvalue in the presence of missing data, assuming the...
Show moreAltham (Altham PME. Exact Bayesian analysis of a 2 × 2 contingency table, and Fisher's "exact" significance test. J R Stat Soc B 1969; 31: 261269) showed that a onesided pvalue from Fisher's exact test of independence in a 2 × 2 contingency table is equal to the posterior probability of negative association in the 2 × 2 contingency table under a Bayesian analysis using an improper prior. We derive an extension of Fisher's exact test pvalue in the presence of missing data, assuming the missing data mechanism is ignorable (i.e., missing at random or completely at random). Further, we propose Bayesian pvalues for a test of independence in a 2 × 2 contingency table with missing data using alternative priors; we also present results from a simulation study exploring the Type I error rate and power of the proposed exact test pvalues. An example, using data on the association between blood pressure and a cardiac enzyme, is presented to illustrate the methods.
Show less  Date Issued
 20181101
 Identifier
 FSU_pmch_28633606, 10.1177/0962280217702538, PMC5799034, 28633606, 28633606
 Format
 Citation
 Title
 OneStep Generalized Estimating Equations with Large Cluster Sizes.
 Creator

Lipsitz, Stuart, Fitzmaurice, Garrett, Sinha, Debajyoti, Hevelone, Nathanael, Hu, Jim, Nguyen, Louis L
 Abstract/Description

Medical studies increasingly involve a large sample of independent clusters, where the cluster sizes are also large. Our motivating example from the 2010 Nationwide Inpatient Sample (NIS) has 8,001,068 patients and 1049 clusters, with average cluster size of 7627. Consistent parameter estimates can be obtained naively assuming independence, which are inefficient when the intracluster correlation (ICC) is high. Efficient generalized estimating equations (GEE) incorporate the ICC and sum all...
Show moreMedical studies increasingly involve a large sample of independent clusters, where the cluster sizes are also large. Our motivating example from the 2010 Nationwide Inpatient Sample (NIS) has 8,001,068 patients and 1049 clusters, with average cluster size of 7627. Consistent parameter estimates can be obtained naively assuming independence, which are inefficient when the intracluster correlation (ICC) is high. Efficient generalized estimating equations (GEE) incorporate the ICC and sum all pairs of observations within a cluster when estimating the ICC. For the 2010 NIS, there are 92.6 billion pairs of observations, making summation of pairs computationally prohibitive. We propose a onestep GEE estimator that 1) matches the asymptotic efficiency of the fullyiterated GEE; 2) uses a simpler formula to estimate the ICC that avoids summing over all pairs; and 3) completely avoids matrix multiplications and inversions. These three features make the proposed estimator much less computationally intensive, especially with large cluster sizes. A unique contribution of this paper is that it expresses the GEE estimating equations incorporating the ICC as a simple sum of vectors and scalars.
Show less  Date Issued
 20170101
 Identifier
 FSU_pmch_29422762, 10.1080/10618600.2017.1321552, PMC5800532, 29422762, 29422762
 Format
 Citation
 Title
 Biascorrected estimates for logistic regression models for complex surveys with application to the United States' Nationwide Inpatient Sample.
 Creator

Rader, Kevin A, Lipsitz, Stuart R, Fitzmaurice, Garrett M, Harrington, David P, Parzen, Michael, Sinha, Debajyoti
 Abstract/Description

For complex surveys with a binary outcome, logistic regression is widely used to model the outcome as a function of covariates. Complex survey sampling designs are typically stratified cluster samples, but consistent and asymptotically unbiased estimates of the logistic regression parameters can be obtained using weighted estimating equations (WEEs) under the naive assumption that subjects within a cluster are independent. Despite the relatively large samples typical of many complex surveys,...
Show moreFor complex surveys with a binary outcome, logistic regression is widely used to model the outcome as a function of covariates. Complex survey sampling designs are typically stratified cluster samples, but consistent and asymptotically unbiased estimates of the logistic regression parameters can be obtained using weighted estimating equations (WEEs) under the naive assumption that subjects within a cluster are independent. Despite the relatively large samples typical of many complex surveys, with rare outcomes, many interaction terms, or analysis of subgroups, the logistic regression parameters estimates from WEE can be markedly biased, just as with independent samples. In this paper, we propose biascorrected WEEs for complex survey data. The proposed method is motivated by a study of postoperative complications in laparoscopic cystectomy, using data from the 2009 United States' Nationwide Inpatient Sample complex survey of hospitals.
Show less  Date Issued
 20171001
 Identifier
 FSU_pmch_26265769, 10.1177/0962280215596550, PMC5799008, 26265769, 26265769, 0962280215596550
 Format
 Citation
 Title
 Efficient Computation of Reduced Regression Models.
 Creator

Lipsitz, Stuart R, Fitzmaurice, Garrett M, Sinha, Debajyoti, Hevelone, Nathanael, Giovannucci, Edward, Trinh, QuocDien, Hu, Jim C
 Abstract/Description

We consider settings where it is of interest to fit and assess regression submodels that arise as various explanatory variables are excluded from a larger regression model. The larger model is referred to as the full model; the submodels are the reduced models. We show that a computationally efficient approximation to the regression estimates under any reduced model can be obtained from a simple weighted least squares (WLS) approach based on the estimated regression parameters and covariance...
Show moreWe consider settings where it is of interest to fit and assess regression submodels that arise as various explanatory variables are excluded from a larger regression model. The larger model is referred to as the full model; the submodels are the reduced models. We show that a computationally efficient approximation to the regression estimates under any reduced model can be obtained from a simple weighted least squares (WLS) approach based on the estimated regression parameters and covariance matrix from the full model. This WLS approach can be considered an extension to unbiased estimating equations of a firstorder Taylor series approach proposed by Lawless and Singhal. Using data from the 2010 Nationwide Inpatient Sample (NIS), a 20% weighted, stratified, cluster sample of approximately 8 million hospital stays from approximately 1000 hospitals, we illustrate the WLS approach when fitting interval censored regression models to estimate the effect of type of surgery (robotic versus nonrobotic surgery) on hospital lengthofstay while adjusting for three sets of covariates: patientlevel characteristics, hospital characteristics, and zipcode level characteristics. Ordinarily, standard fitting of the reduced models to the NIS data takes approximately 10 hours; using the proposed WLS approach, the reduced models take seconds to fit.
Show less  Date Issued
 20170101
 Identifier
 FSU_pmch_29104296, 10.1080/00031305.2017.1296375, PMC5664962, 29104296, 29104296
 Format
 Citation
 Title
 Association Models for Clustered Data with Binary and Continuous Responses.
 Creator

Lin, Lanjia, Sinha, Debajyoti, Hurt, Myra, Lipsitz, Stuart R., McGee, Daniel, Department of Statistics, Florida State University
 Abstract/Description

This dissertation develops novel single random effect models as well as bivariate correlated random effects model for clustered data with bivariate mixed responses. Logit and identity link functions are used for the binary and continuous responses. For the ease of interpretation of the regression effects, random effect of the binary response has bridge distribution so that the marginal model of mean of the binary response after integrating out the random effect preserves logistic form. And...
Show moreThis dissertation develops novel single random effect models as well as bivariate correlated random effects model for clustered data with bivariate mixed responses. Logit and identity link functions are used for the binary and continuous responses. For the ease of interpretation of the regression effects, random effect of the binary response has bridge distribution so that the marginal model of mean of the binary response after integrating out the random effect preserves logistic form. And the marginal regression function of the continuous response preserves linear form. Withincluster and withinsubject associations could be measured by our proposed models. For the bivariate correlated random effects model, we illustrate how different levels of the association between two random effects induce different Kendall's tau values for association between the binary and continuous responses from the same cluster. Fully parametric and semiparametric Bayesian methods as well as maximum likelihood method are illustrated for model analysis. In the semiparametric Bayesian model, normality assumption of the regression error for the continuous response is relaxed by using a nonparametric Dirichlet Process prior. Robustness of the bivariate correlated random effects model using ML method to misspecifications of regression function as well as random effect distribution is investigated by simulation studies. The Bayesian and likelihood methods are applied to a developmental toxicity study of ethylene glycol in mice.
Show less  Date Issued
 2009
 Identifier
 FSU_migr_etd1330
 Format
 Thesis
 Title
 Bayesian Methods for Skewed Response Including Longitudinal and Heteroscedastic Data.
 Creator

Tang, Yuanyuan, Sinha, Debajyoti, Pati, Debdeep, Flynn, Heather, She, Yiyuan, Lipsitz, Stuart, Zhang, Jinfeng, Department of Statistics, Florida State University
 Abstract/Description

Skewed response data are very popular in practice, especially in biomedical area. We begin our work from the skewed longitudinal response without heteroscedasticity. We extend the skewed error density to the multivariate response. Then we study the heterocedasticity. We extend the transformbothsides model to the bayesian variable selection area to handle the univariate skewed response, where the variance of response is a function of the median. At last, we proposed a novel model to handle...
Show moreSkewed response data are very popular in practice, especially in biomedical area. We begin our work from the skewed longitudinal response without heteroscedasticity. We extend the skewed error density to the multivariate response. Then we study the heterocedasticity. We extend the transformbothsides model to the bayesian variable selection area to handle the univariate skewed response, where the variance of response is a function of the median. At last, we proposed a novel model to handle the skewed univariate response with a flexible heteroscedasticity. For longitudinal studies with heavily skewed continuous response, statistical model and methods focusing on mean response are not appropriate. In this paper, we present a partial linear model of median regression function of skewed longitudinal response. We develop a semiparametric Bayesian estimation procedure using an appropriate Dirichlet process mixture prior for the skewed error distribution. We provide justifications for using our methods including theoretical investigation of the support of the prior, asymptotic properties of the posterior and also simulation studies of finite sample properties. Ease of implementation and advantages of our model and method compared to existing methods are illustrated via analysis of a cardiotoxicity study of children of HIV infected mother. Our second aim is to develop a Bayesian simultaneous variable selection and estimation of median regression for skewed response variable. Our hierarchical Bayesian model can incorporate advantages of $l_0$ penalty for skewed and heteroscedastic error. Some preliminary simulation studies have been conducted to compare the performance of proposed model and existing frequentist median lasso regression model. Considering the estimation bias and total square error, our proposed model performs as good as, or better than competing frequentist estimators. In biomedical studies, the covariates often affect the location, scale as well as the shape of the skewed response distribution. Existing biostatistical literature mainly focuses on the mean regression with a symmetric error distribution. While such modeling assumptions and methods are often deemed as restrictive and inappropriate for skewed response, the completely nonparametric methods may lack a physical interpretation of the covariate effects. Existing nonparametric methods also miss any easily implementable computational tool. For a skewed response, we develop a novel model accommodating a nonparametric error density that depends on the covariates. The advantages of our semiparametric associated Bayes method include the ease of prior elicitation/determination, an easily implementable posterior computation, theoretically sound properties of the selection of priors and accommodation of possible outliers. The practical advantages of the method are illustrated via a simulation study and an analysis of a reallife epidemiological study on the serum response to DDT exposure during gestation period.
Show less  Date Issued
 2013
 Identifier
 FSU_migr_etd7622
 Format
 Thesis
 Title
 Analysis of Multivariate Data with Random Cluster Size.
 Creator

Li, Xiaoyun, Sinha, Debajyoti, Zhou, Yi, McGee, Dan, Lipsitz, Stuart, Department of Statistics, Florida State University
 Abstract/Description

In this dissertation, we examine binary correlated data with present/absent component or missing data that are related to binary responses of interest. Depending on the data structure, correlated binary data can be referred as emph{clustered data} if sampling unit is a cluster of subjects, or it can be referred as emph{longitudinal data} when it involves repeated measurement of same subject over time. We propose our novel models in these two data structures and illustrate the model with real...
Show moreIn this dissertation, we examine binary correlated data with present/absent component or missing data that are related to binary responses of interest. Depending on the data structure, correlated binary data can be referred as emph{clustered data} if sampling unit is a cluster of subjects, or it can be referred as emph{longitudinal data} when it involves repeated measurement of same subject over time. We propose our novel models in these two data structures and illustrate the model with real data applications. In biomedical studies involving clustered binary responses, the cluster size can vary because some components of the cluster can be absent. When both the presence of a cluster component as well as the binary disease status of a present component are treated as responses of interest, we propose a novel twostage random effects logistic regression framework. For the ease of interpretation of regression effects, both the marginal probability of presence/absence of a component as well as the conditional probability of disease status of a present component, preserve the approximate logistic regression forms. We present a maximum likelihood method of estimation implementable using standard statistical software. We compare our models and the physical interpretation of regression effects with competing methods from literature. We also present a simulation study to assess the robustness of our procedure to wrong specification of the random effects distribution and to compare finite sample performances of estimates with existing methods. The methodology is illustrated via analyzing a study of the periodontal health status in a diabetic Gullah population. We extend this model in longitudinal studies with binary longitudinal response and informative missing data. In longitudinal studies, when treating each subject as a cluster, cluster size is the total number of observations for each subject. When data is informatively missing, cluster size of each subject can vary and is related to the binary response of interest and we are also interested in the missing mechanism. This is a modified situation of the cluster binary data with present components. We modify and adopt our proposed twostage random effects logistic regression model so that both the marginal probability of binary response and missing indicator as well as the conditional probability of binary response and missing indicator preserve logistic regression forms. We present a Bayesian framework of this model and illustrate our proposed model on an AIDS data example.
Show less  Date Issued
 2011
 Identifier
 FSU_migr_etd1425
 Format
 Thesis
 Title
 Median Regression for Complex Survey Data.
 Creator

Fraser, Raphael André, Sinha, Debajyoti, Lipsitz, Stuart, Carlson, Elwood, Slate, Elizabeth H., Huffer, Fred W. (Fred William), Florida State University, College of Arts and...
Show moreFraser, Raphael André, Sinha, Debajyoti, Lipsitz, Stuart, Carlson, Elwood, Slate, Elizabeth H., Huffer, Fred W. (Fred William), Florida State University, College of Arts and Sciences, Department of Statistics
Show less  Abstract/Description

The ready availability of publicuse data from various large national complex surveys has immense potential for the assessment of population characteristicsmeans, proportions, totals, etcetera. Using a modelbased approach, complex surveys can be used to evaluate the effectiveness of treatments and to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data...
Show moreThe ready availability of publicuse data from various large national complex surveys has immense potential for the assessment of population characteristicsmeans, proportions, totals, etcetera. Using a modelbased approach, complex surveys can be used to evaluate the effectiveness of treatments and to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to design features such as stratification, multistage sampling and unequal selection probabilities. In this paper, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a doubletransformbothsides based estimating equations approach to estimate the median regression parameters of the highly skewed response; the doubletransformbothsides method applies the same transformation twice to both the response and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudolikelihood based on minimizing absolute deviations. Furthermore, the doubletransformbothsides estimator is relatively robust to the true underlying distribution, and has much smaller mean square error than the least absolute deviations estimator. The method is motivated by an analysis of laboratory data on urinary iodine concentration from the National Health and Nutrition Examination Survey.
Show less  Date Issued
 2015
 Identifier
 FSU_2015fall_Fraser_fsu_0071E_12825
 Format
 Thesis
 Title
 Influence Measures for Bayesian Data Analysis.
 Creator

De Oliveira, Melaine C. (Melaine Cristina), Sinha, Debajyoti, Panton, Lynn B., Bradley, Jonathan R., Linero, Antonio Ricardo, Lipsitz, Stuart, Florida State University, College...
Show moreDe Oliveira, Melaine C. (Melaine Cristina), Sinha, Debajyoti, Panton, Lynn B., Bradley, Jonathan R., Linero, Antonio Ricardo, Lipsitz, Stuart, Florida State University, College of Arts and Sciences, Department of Statistics
Show less  Abstract/Description

Identifying influential observations in the data is desired to ensure proper inference and statistical analysis. Modern methods to identify influence cases uses crossvalidation diagnostics based on the effect of deletion of ith observation on inference. A popular method to identify influential observations is to use KullbackLiebler divergence measure between the posterior distribution of the parameter of interest given full data and the posterior distribution given the crossvalidated data...
Show moreIdentifying influential observations in the data is desired to ensure proper inference and statistical analysis. Modern methods to identify influence cases uses crossvalidation diagnostics based on the effect of deletion of ith observation on inference. A popular method to identify influential observations is to use KullbackLiebler divergence measure between the posterior distribution of the parameter of interest given full data and the posterior distribution given the crossvalidated data, where the crossvalidated data has the ith observation removed. Although, in Bayesian inference, the posterior distribution contains all the relevant information about a parameter of interest, when the goal is prediction, perhaps the predictive distribution should be used to identifying influential observations. So, we extended our method to the comparison of the posterior predictive distributions given full data and crossvalidated data. We generalize and extend existing popular Bayesian crossvalidated influence diagnostics using Bregman divergence based measure (BD). We derive useful properties of these BD based on the influence of each observation on the posterior distribution and we show that it can be extended to the predictive distribution. We show that these BD based measures allow interpretable calibration and that they can be computed via Monte Carlo Markov Chain (MCMC) samples from a single posterior based on full data. We illustrate how our new measure of influence of observations have more useful practical roles for data analysis than popular Bayesian residual analysis tools (CPO) in an example of metaanalysis with binary response and in other cases of intervalcensored data.
Show less  Date Issued
 2018
 Identifier
 2018_Su_DeOliveira_fsu_0071E_14712
 Format
 Thesis
 Title
 Semiparametric Bayesian Regression Models for Skewed Responses.
 Creator

Bhingare, Apurva Chandrashekhar, Sinha, Debajyoti, Shanbhag, Sachin, Linero, Antonio Ricardo, Bradley, Jonathan R., Pati, Debdeep, Lipsitz, Stuart, Florida State University,...
Show moreBhingare, Apurva Chandrashekhar, Sinha, Debajyoti, Shanbhag, Sachin, Linero, Antonio Ricardo, Bradley, Jonathan R., Pati, Debdeep, Lipsitz, Stuart, Florida State University, College of Arts and Sciences, Department of Statistics
Show less  Abstract/Description

It is common to encounter skewed response data in medicine, epidemiology and health care studies. Methodology needs to be devised to overcome the natural difficulties that occur in analyzing such data particularly when it is multivariate. Existing Bayesian statistical methods to deal with skewed data are mostly fully parametric. We propose novel semiparametric Bayesian methods to model an analyze such data. These methods make minimal assumptions about the true form of the distribution and...
Show moreIt is common to encounter skewed response data in medicine, epidemiology and health care studies. Methodology needs to be devised to overcome the natural difficulties that occur in analyzing such data particularly when it is multivariate. Existing Bayesian statistical methods to deal with skewed data are mostly fully parametric. We propose novel semiparametric Bayesian methods to model an analyze such data. These methods make minimal assumptions about the true form of the distribution and structure of the observed data. Through examples from real life studies, we demonstrate practical advantages of our semiparametric Bayesian methods over the existing methods. For many reallife studies with skewed multivariate responses, the level of skewness and association structure assumptions are essential for evaluating the covariate effects on the response and its predictive distribution. First, we present a novel semiparametric multivariate model class leading to a theoretically justifiable semiparametric Bayesian analysis of multivariate skewed responses. Like the multivariate Gaussian densities, this multivariate model is closed under marginalization, allows a wide class of multivariate associations, and has meaningful physical interpretations of skewness levels and covariate effects on the marginal density. Compared to existing models, our model enjoys several desirable practical properties, including Bayesian computing via available software, and assurance of consistent Bayesian estimates of parameters and the nonparametric error density under a set of plausible prior assumptions. We introduce a particular parametric version of the model as an alternative to various parametric skewsymmetric models available in the literature. We illustrate the practical advantages of our methods over existing parametric alternatives via application to a clinical study to assess periodontal disease and through a simulation study. Unlike most of the models existing in literature, this class of models advocates a latent variable approach making implementation under the Bayesian paradigm via standard software for MCMC computation like WinBUGS/JAGS straightforward. Although, JAGS and WinBUGS are flexible MCMC engines, for complex model structures they tend to be rather slow. We offer an alternative tool to implement the aforementioned parametric version of the models using PROC MCMC in SAS. Our goal is to facilitate and encourage more extensive implementation of these models. To achieve this goal, we illustrate the implementation using PROC MCMC in SAS via examples from real life and provide a full annotated SAS code. In large scale national surveys, we often come across skewed data as well as semicontinuous data, that is, data characterized by point mass at zero (degenerate) and right skewed continuous distribution on positive support. For example, in the Medical Expenditure Panel Survey (MEPS), the variable total health care expenditure (i.e., the response) for nonusers of the health care services is zero, whereas for the users it is has continuous distribution typically skewed towards the right. We provide an overview of the existing models and methods to analyze such data.
Show less  Date Issued
 2018
 Identifier
 2018_Sp_Bhingare_fsu_0071E_14468
 Format
 Thesis
 Title
 Semiparametric Survival Analysis Using Models with LogLinear Median.
 Creator

Lin, Jianchang, Sinha, Debajyoti, Zhou, Yi, Lipsitz, Stuart, McGee, Dan, Niu, XuFeng, She, Yiyuan, Department of Statistics, Florida State University
 Abstract/Description

First, we present two novel semiparametric survival models with loglinear median regression functions for right censored survival data. These models are useful alternatives to the popular Cox (1972) model and linear transformation models (Cheng et al., 1995). Compared to existing semiparametric models, our models have many important practical advantages, including interpretation of the regression parameters via the median and the ability to address heteroscedasticity. We demonstrate that our...
Show moreFirst, we present two novel semiparametric survival models with loglinear median regression functions for right censored survival data. These models are useful alternatives to the popular Cox (1972) model and linear transformation models (Cheng et al., 1995). Compared to existing semiparametric models, our models have many important practical advantages, including interpretation of the regression parameters via the median and the ability to address heteroscedasticity. We demonstrate that our modeling techniques facilitate the ease of prior elicitation and computation for both parametric and semiparametric Bayesian analysis of survival data. We illustrate the advantages of our modeling, as well as model diagnostics, via reanalysis of a smallcell lung cancer study. Results of our simulation study provide further guidance regarding appropriate modelling in practice. Our second goal is to develop the methods of analysis and associated theoretical properties for interval censored and current status survival data. These new regression models use loglinear regression function for the median. We present frequentist and Bayesian procedures for estimation of the regression parameters. Our model is a useful and practical alternative to the popular semiparametric models which focus on modeling the hazard function. We illustrate the advantages and properties of our proposed methods via reanalyzing a breast cancer study. Our other aim is to develop a model which is able to account for the heteroscedasticity of response, together with robust parameter estimation and outlier detection using sparsity penalization. Some preliminary simulation studies have been conducted to compare the performance of proposed model and existing median lasso regression model. Considering the estimation bias, mean squared error and other identication benchmark measures, our proposed model performs better than the competing frequentist estimator.
Show less  Date Issued
 2012
 Identifier
 FSU_migr_etd4992
 Format
 Thesis
 Title
 Practical Methods for Equivalence and NonInferiority Studies with Survival Response.
 Creator

Martinez, Elvis Englebert, Sinha, Debajyoti, Levenson, Cathy W., Chicken, Eric, Lipsitz, Stuart, McGee, Daniel, Florida State University, College of Arts and Sciences,...
Show moreMartinez, Elvis Englebert, Sinha, Debajyoti, Levenson, Cathy W., Chicken, Eric, Lipsitz, Stuart, McGee, Daniel, Florida State University, College of Arts and Sciences, Department of Statistics
Show less  Abstract/Description

Determining the equivalence or noninferiority of a new drug (test drug) with a existing treatment (reference drug) is an important topic of statistical interest. Wellek (1993) pioneered the way for logrank based equivalence and noninferiority testing by formulating a testing procedure using proportional hazards model (PHM) of Cox (1972). In many equivalence and noninferiority trials, two hazards functions may converge to one rather than being proportional for all timepoints. In this case...
Show moreDetermining the equivalence or noninferiority of a new drug (test drug) with a existing treatment (reference drug) is an important topic of statistical interest. Wellek (1993) pioneered the way for logrank based equivalence and noninferiority testing by formulating a testing procedure using proportional hazards model (PHM) of Cox (1972). In many equivalence and noninferiority trials, two hazards functions may converge to one rather than being proportional for all timepoints. In this case, the proportional odds survival model (POSM) of Bennett (1983) will be more sufficient than a Cox's PHM assumption. We show in both cases, when the wrong modeling assumption is made and Cox's PH assumption is violated, the popular procedure of Wellek (1993) has an inflated type I error. On the contrary, our proposed POS model based equivalence and noninferiority tests maintains the practitioners desired 5% level of significance regardless of the underlying modeling assumption (e.g. Cox,1972; Wellek, 1993). Furthermore for noninferiority trials, we introduce a method to determine the optimal sample size required when a desired power and type I error is specified and the data follows the POSM of Bennett (1983). For both of the above trials, we present simulation studies showing the finite approximation of powers and type I error rates, when the underlying modeling assumption are correctly specified and when the assumptions are misspecified.
Show less  Date Issued
 2014
 Identifier
 FSU_migr_etd9214
 Format
 Thesis