Abstracts ENAR 2013 Spring Meeting March 10–13 With IMS and Sections of ASA Orlando World Center Marriott Resort | Orlando, Florida S N O I T A T N E S E R P 1c. SEMIPARAMETRIC PROPORTIONAL RATE REGRESSION FOR THE COMPOSITE ENDPOINT R OF RECURRENT AND TERMINAL EVENTS E Lu Mao*, University of North Carolina, Chapel Hill T Danyu Lin, University of North Carolina, Chapel Hill ENAR 2013 S O Analysis of recurrent event data has received tremendous P attention in recent years. A major complication arises Spring Meeting when recurrent events are terminated by death. To assess | S the overall covariate effects, we consider the composite T endpoint of recurrent and terminal events and propose March 10 – 13 C a proportional rate model which specifies that (possibly A time-dependent) covariates have multiplicative affects TR on the marginal rate function of the composite event pro- BS cess. We derive appropriate estimators for the regression A parameters and the baseline mean function by modifying the familiar inverse probability weighting technique. We show that the estimators are consistent and asymptoti- cally normal with variances that can be consistently estimated. Simulation studies demonstrate that the proposed methods perform well in realistic situations. An application to the Community Programs for Clinical 1. POSTERS: CLINICAL TRIALS AND 1b. INTERACTIVE Q-LEARNING FOR DYNAMIC Research on AIDS (CPCRA) study is provided. STUDY DESIGN TREATMENT REGIMES Kristin A. Linn*, North Carolina State University email: [email protected] Eric B. Laber, North Carolina State University 1a. OPTIMAL BAYESIAN ADAPTIVE TRIAL Leonard A. Stefanski, North Carolina State University OF PERSONALIZED MEDICINE IN CANCER Yifan Zhang*, Harvard University 1d. DETECTION OF OUTLIERS AND INFLUENTIAL Forming evidence-based rules for optimal treatment Lorenzo Trippa, Harvard University, Dana-Farber POINTS IN MULTIVARIATE LONGITUDINAL allocation over time is a priority in personalized medicine Cancer Institute MODELS research. Such rules must be estimated from data col- Giovanni Parmigiani, Harvard University, Dana-Farber Yun Ling*, University of Pittsburgh School of Medicine lected in observational or randomized studies. Popular Cancer Institute Stewart J. Anderson, University of Pittsburgh methods for estimating optimal sequential decision rules School of Medicine from data, such as Q-learning, are approximate dynamic Clinical biomarkers play an important role in personalized Richard A. Bilonick, University of Pittsburgh programming algorithms that require modeling non- medicine in cancer clinical trials. An adaptive trial design School of Medicine smooth transformations of the data. Postulating a simple, enables researchers to use treatment results observed Gadi Wollstein, University of Pittsburgh well-fitting model for the transformed data can be diffi- from early patients to aid in treatment decisions of later School of Medicine cult, and under many simple generative models the most patients. We describe a biomarker-incorporated Bayesian commonly employed working models—namely linear adaptive trial design. This trial design is the optimal In clinical trials, multiple characteristics of individuals models—are known to be misspecified. We propose an strategy that maximizes the total patient responses. We are repeatedly measured. Multivariate longitudinal data alternative strategy for estimating optimal sequential study the effects of the biomarker and marker group allow one to analyze the joint evolution of multiple char- decision rules wherein all modeling takes place before proportions on the total utility and present comparisons acteristics over time. Detection of outlier and influential applying non-smooth transformations of the data. between the optimal trial design with other adaptive points for multivariate longitudinal data is important to This simple change of ordering between modeling and trial designs. understand potentially critical multivariate observations transforming the data leads to high quality estimated which can unduly influence the results of analyses. In this sequential decision rules. Additionally, the proposed email: [email protected] presentation, we propose a new approach that extends estimators involve only conditional mean and variance Cook’s distance to multivariate mixed effect models, modeling of smooth functionals of the data. Consequent- conditional on different characteristics and subjects. Our ly, standard statistical procedures for exploratory analysis, approach allows different types of outliers and influential model building, and validation can be used. Furthermore, points: it could be one or more measurements on an under minimal assumptions, the proposed estimators individual at a single time point, or all measurements enjoy simple normal limit theory. on that individual over time. Our approach also takes email: [email protected] Abstracts 2 OSTER PRES | P EN S TA CT T A IO R N T S S Holm (1979). The Hommel (1988) procedure is even B 2. POSTERS: BAYESIAN METHODS / A more powerful than the Hochberg procedure but it is CAUSAL INFERENCE less widely used because it is not as simple and is less intuitive. In this paper we derive a new procedure which into account (1) different residual variances for different 2a. BAYESIAN MODELS FOR CENSORED BINOMIAL improves upon the Hommel procedure by gaining power characteristics; (2) the correlation among residuals for DATA: RESULTS FROM AN MCMC SAMPLER as well as having a simple step-up structure similar to the the same characteristic measured at different time points Jessica Pruszynski*, Medical College of Wisconsin Hochberg procedure. Thus it offers the best choice among (within-characteristic correlation); (3) the correlation John W. Seaman, Jr., Baylor University among residuals for different characteristics measured at all p-value based stepwise multiple test procedures. The key to this improvement is employing a consonant pro- one time point (inter-characteristic correlation); and (4) Censored binomial data may lead to irregular likelihood cedure whereas the Hommel procedure is not consonant unequally spaced assessments where not all responses functions and problems with statistical inference. In and can be improved (Romano, Shaikh, and Wolf, 2011). of different individuals are measured at the same time previous studies, we have compared Bayesian and Exact critical constants of this new procedure can be points. We apply the approach to the analysis of retina frequentist models and shown that Bayesian models numerically calculated and tabled. But the 0th order ap- data for glaucoma eyes from UPMC. outperform their frequentist counterparts. In this study, proximations to the exact critical constants, albeit slightly we compare the performance of a Bayesian model under conservative, are simple to use and need no tabling, and email: [email protected] varying sample sizes and prior distributions. We include hence are recommended in practice. Adjusted p-values of results from a simulation study in which we compare this proposed procedure are derived. The proposed proce- properties such as point estimation, interval coverage, dure is shown to control the familywise error rate (FWER) 1e. TESTS FOR EQUIVALENCE OF TWO SURVIVAL and interval width. FUNCTIONS IN PROPORTIONAL ODDS MODEL both under independence (analytically) and dependence (via simulation) among the p-values, and also shown Elvis Martinez*, Florida State University email: [email protected] Debajyoti Sinha, Florida State University to be more powerful (via simulation) than competing Wenting Wang, Florida State University procedures. Illustrative examples are given. Stuart R Lipsitz, Harvard University 2b. AN APPROXIMATE UNIFORM SHRINKAGE email: [email protected] PRIOR FOR A MULTIVARIATE GENERALIZED When survival responses from two treatment arms LINEAR MIXED MODEL satisfy the proportional odds survival models (POSM), Hsiang-chun Chen*, Texas A&M University 1g. CHARACTERIZATION OF TWO-STAGE CONTINUAL we present a proper statistical formulation of the clinical Thomas E. Wehrly, Texas A&M University hypothesis of therapeutic equivalence. We show that REASSESSMENT METHOD FOR DOSE FINDING CLINICAL TRIALS difference between two survival functions being within The multivariate generalized linear mixed models Xiaoyu Jia, Columbia University maximum allowable difference, implies the survival (MGLMM) are used for jointly modeling the clustered odds parameter to be within a specific interval and vice mixed outcomes obtained when there is more than one The continual reassessment method (CRM) is an increas- versa. Our equivalence test, formulation, and related response repeatedly measured on each individual in ingly popular model-based method for dose finding procedure are applicable even in presence of additional scientific studies. Bayesian methods are one of the most clinical trials among clinicians. A common practice is to covariates beyond treatment arms. Our theoretical and widely used techniques for analyzing MGLMM. The need use the CRM in a two-stage design, whereby the model- simulation studies show that actual type I error rate for of noninformative priors arises when there is insufficient based CRM is activated only after an initial sequence of popular equivalence testing procedure (Wellek, 1993) information on the model parameters. The main purpose patients are tested. While there are practical appeals of under proportional hazards model (PHM) is higher than of this study is to propose an approximate uniform shrink-