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METHODS in RANDOMIZATION BASED ANCOVA for NOVEL CROSSOVER DESIGNS and SENSITIVITY ANALYSIS for MISSING DATA Siying Li a Disserta

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METHODS in RANDOMIZATION BASED ANCOVA for NOVEL CROSSOVER DESIGNS and SENSITIVITY ANALYSIS for MISSING DATA Siying Li a Disserta View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Carolina Digital Repository METHODS IN RANDOMIZATION BASED ANCOVA FOR NOVEL CROSSOVER DESIGNS AND SENSITIVITY ANALYSIS FOR MISSING DATA Siying Li A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics in the Gillings School of Global Public Health. Chapel Hill 2017 Approved by: Gary G. Koch Amy H. Herring Anastasia Ivanova Charles Poole John S. Preisser © 2017 Siying Li ALL RIGHTS RESERVED ii ABSTRACT Siying Li: Methods in Randomization Based ANCOVA for Novel Crossover Designs and Sensitivity Analysis for Missing Data (Under the direction of Gary G. Koch) In clinical trials, statistical inference is preferably conducted with less stringent assumptions. This dissertation proposes a non-parametric method for dichotomous and ordinal missing data, and it proposes a structure for the hypothesis testing and estimation for innovative crossover designs. When data missing not at random (MNAR) arise from randomized multi-visit, multi- center clinical trials, sensitivity analyses to address possibly informative missing are needed. We propose a closed form point and variance matrix estimation for dichotomized missing data by probabilistically redistributing missing counts, adjusting for a stratification factor and/or baseline covariables. The parameter estimates are computed via weighted least squares asymptotic regression through randomization based methods. We further extend the methods to sensitivity analyses for ordinal endpoints. A novel crossover design, the sequential parallel comparison design (SPCD), where information from placebo responders in the second period are excluded, serves as a design for studies with high placebo response. Estimators for sources of comparison in the traditional SPCD design, as well as other sources of information that are available, are constructed with methods based on the randomization distribution of the observed population using the nonparametric iii mean and variance estimates under the null hypothesis, which control Type I error well in hypothesis testing. Baseline imbalance is adjusted by randomization-based ANCOVA. Simulations are performed to study the statistical properties of the proposed methods, which are compared to those of a repeated measures model proposed by Doros et al. (2013). Point and confidence interval estimation is also addressed by assuming the study population comes from a simple random sample of an almost infinite population. A consistent covariance matrix estimator is constructed and properties of the proposed estimators are studied with simulations, particularly for coverage of confidence intervals. The nominal coverage level is achieved with a t distribution for the approximation to the asymptotic distribution when the sample size is not sufficiently large. The methodologies are extended to the two-way enrichment design (TED) introduced by Ivanova and Tamura (2011), and to a related bilateral design that applies the four sequence group design to two sides of the same subject instead of two periods. iv ACKNOWLEDGEMENTS I want to take the opportunity to express my thanks to Dr. Gary Koch, who supported me both financially and mentally throughout my graduate school years. His role as mentor of life and his advice on both research and life (and Carolina basketball and football), in the office, at breakfast in Bob Evans and Whole Foods, and over the phone whenever I needed, will always be a memorable experience. My gratitude also goes to Dr. Amy Herring, for coaching me on research projects and serving as my role model as a passionate public health female researcher. Their being my pillar of support and always backing me up has encouraged me through both my good and struggling times. I also want to thank Dr. Preisser and Dr. Ivanova for being coauthors of manuscripts and for the constructive feedbacks they have provided, and Dr. Poole for being on my committee. I would like to thank my grandparents and parents, who have always set the bar high since I was a kid and believed in my capability in accomplishing goals. I could not express my gratitude enough to my husband, who always support me unconditionally, with great patience along my way. I am also grateful for my fellow students at UNC, SPH, and the biostat department, and my friends at the RTP running club, for making this venture within and outside campus even more meaningful. v TABLE OF CONTENTS LIST OF TABLES ......................................................................................................................... ix LIST OF FIGURES ....................................................................................................................... xi CHAPTER 1 INTRODUCTION .................................................................................................... 1 Handling Random Imbalance of Baseline Covariables .................................................... 1 Handling Missing Data..................................................................................................... 4 1.2.1 Missing Data Mechanism ......................................................................................... 5 1.2.2 Approaches for Handling Dropouts by Parametric Model ....................................... 9 1.2.3 Approaches for Handling Dropouts by Imputation ................................................ 11 1.2.4 Sensitivity Analyses ................................................................................................ 14 Crossover Studies ........................................................................................................... 15 1.3.1 Traditional Crossover Designs ................................................................................ 15 1.3.2 Innovative Two-Period Crossover Designs ............................................................ 17 Summary ........................................................................................................................ 23 REFERENCES .......................................................................................................................... 25 CHAPTER 2: SENSITIVITY ANALYSIS OF FAVORABLE PROPORTION FOR MISSING DICHOTOMOUS DATA IN MULTI-VISIT RANDOMIZED CLINICAL TRIAL ....................................................................................................................... 28 Introduction .................................................................................................................... 28 Methods .......................................................................................................................... 29 2.2.1 Notation................................................................................................................... 30 2.2.2 Data Structure ......................................................................................................... 30 2.2.3 Favorable Probability Estimation ........................................................................... 31 vi 2.2.4 Sensitivity Analysis ................................................................................................ 32 2.2.5 Covariance Matrix Estimation ................................................................................ 34 2.2.6 Treatment Comparison............................................................................................ 36 Example .......................................................................................................................... 39 Discussion ...................................................................................................................... 42 REFERENCES .......................................................................................................................... 43 CHAPTER 3: SENSITIVITY ANALYSIS IN TREATMENT COMPARISON FOR MISSING ORDINAL DATA IN MULTIE-VISIT RANDOMIZED CLINICAL TRIAL ....................................................................................................................... 44 Introduction .................................................................................................................... 44 Methods .......................................................................................................................... 44 3.2.1 Notation................................................................................................................... 44 3.2.2 Data Structure ......................................................................................................... 45 3.2.3 Multinomial Probability Estimation ....................................................................... 46 3.2.4 Sensitivity Analysis ................................................................................................ 46 3.2.5 Treatment Comparison............................................................................................ 49 Example .......................................................................................................................... 54 REFERENCES .......................................................................................................................... 57 CHAPTER 4: RANDOMIZATION-BASED ANCOVA FOR HYPOTHESES TESTING IN THE SEQUENTIAL PARALLEL COMPARISON DESIGN (SPCD) ................ 58 Introduction ...................................................................................................................
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