"Assessing Assay Variability of Pesticide Metabolites in the Presence of Heavy Left-censoring"
Haiying Chen, PhD Associate Professor Department of Biostatistics Wake Forest School of Medicine
Abstract: Assessing assay variability for field samples in environmental research is challenging since a quantitative assay is typically constrained by a lower limit of detection. The purpose of this analysis is to compare three parametric models for assessing assay variability using duplicate data subject to heavy left-censoring. Efron information criterion (EIC) is used to aid in model selections. Distributional parameter estimates are obtained using maximum likelihood estimation for bivariate lognormal, bivariate zero-inflated lognormal, and bivariate 3-component mixture models.We illustrate a practical application using duplicate pesticide data from the Community Participatory Approach to Measuring Farmworker Pesticide Exposure (PACE3) study. Furthermore, a simulation study is conducted to empirically evaluate the performance of the three models. The results from PACE3 indicate that the bivariate zero-inflated lognormal model is fairly competitive based on EIC. Further, total variability for the lognormal component can be decomposed into between-subject and within-subject variance based on this model. Assay variability estimates such as within-subject coefficient variation, minimum detectable change and probability of -fold difference can be easily derived under the bivariate zero-inflated lognormal model. Additionally, the assay variability is rather large for the PACE3 data. Therefore, apparent longitudinal change in pesticide exposure should be examined cautiously in the context of substantial assay variability.
"A new estimation approach for combining epidemiological data from multiple sources"
Yongtao Guan, PhD Professor Department of Management Science University of Miami
Abstract: We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. In the first step, we derive all possible unbiased estimating functions based on a group of cases and a group of controls each time. In the second step, we combine these estimating functions efficiently to make full use of the information contained in data. Our approach is computationally simple and flexible. We illustrate its efficacy through simulation and apply it to investigate pancreatic cancer risks based on data obtained from the Connecticut Tumor Registry, a population-based case–control study, and the Behavioral Risk Factor Surveillance System which is a state-based system of health surveys.
Supplementary materials for this article are available online.
"Modeling & Simulation to Improve the Design of Clinical Trials"
Gary Rosner, ScD Professor and Director Division of Biostatistics and Bioinformatics Johns Hopkins University
Abstract: Clinical studies in cancer are becoming more complex, especially in early phase studies. The complexity arises because of many factors, such as the desire to be more efficient than in the past, the need to address endpoints other than tumor shrinkage with newer targeted agents, an interest in applying decision theoretic considerations to optimize the design, and the current interest in determining the best drugs for the specific molecular characteristics of each tumor. Many studies that incorporate Bayesian ideas in their design often require modeling and simulation to help determine the best design or to evaluate each study's operating characteristics. In this talk, I will discuss several examples of designs that benefitted from modeling and simulation.
"Generalizing Quntile Regression for Counting Processes with Applications to Recurrent Events"
Abstract: In survival analysis, quantile reqgression has become a useful approach to accounting for covariate effects on the distribution of a single event time. In this work, we discusss how quantile regression can be extneded to model counting processes that are naturally embedded in event history data, which leads to a broader regression framework for survival data. We specifically investigate the proposed modeling of couting processes in recurrent events settings. We show that the new counting process model facilitates the accommodation of complex observation windows of recurrecnt events as often encountered in observational studies. It also enables a unified theory and inferential framework for studying both censored quantile regression and the proposed recurrent events model. As another useful contribution of this work, we propose a sample-based covariance estimation procedure, which provides a useful inference tool that complements the prevailing boostrapping approach. We demonstrate the utility of our proposals via simulation studies and an application to a dataset from the US Cystic Fibrosis Foundation Patient Registry (CFFPR).
Limin Peng, PhD Associate Professor Division of Biostatistics and Bioinformatics Emory University
"How important is HLA Matching for Young Kidney Transplant Recipients?"
Bethany Foster, MD, MSCE
Associate Professor, Department of Pediatrics,
Montreal Children’s Hospital, McGill University
"Safe and Active Transportation: Conjoint Approaches to Promoting Health and Preventing Road Injury"
Alexander Quistberg, PhD, MPH
University of Washington, School of Medicine
"Causal modeling under complex dependency in clustered and longitudinal observations"
Jiwei He, MS PhD Candidate Division of Biostatistics Department of Biostatistics and Epidemiology
Abstract: In assessing the efficacy of a time-varying treatment MarginalStructural Nested Mean Models (SNMMs) are useful in dealing with confounding by variables affected by earlier treatments. MSMs model the joint effect of treatments on the marginal mean of the potential outcome, whereas SNMMs model the joint effect of treatments on the mean of the potential outcome conditional on the treatment and covariate history. These models often consider independent subjects with noninformative time of observation.
We extend the two classes of models to clustered observations with time-varying treatments in the presence of time-varying confounding. We formulate models with both cluster- and unit-level treatments and derive semiparametric estimators of parameters in such models. For unit-level treatments, we consider both the presence and absence of interference, namely the effect of treatment on outcomes in other units of the same cluster. For MSMs, we show that the use of unit-specific inverse probability weights and certain working correlation structures can improve the efficiency of estimators under specified conditions. The properties of the estimators are evaluated through simulations and compared with the conventional GEE regression method for clustered outcomes. To illustrate our methods, we use data from the treatment arm of a glaucoma clinical trial to compare the effectiveness of two commonly used ocular hypertension medications.
We also extend SNMMs to situations with intermittent missing observations. In observational longitudinal studies, subjects often miss prescheduled visits intermittently. Previous literature has mainly focused on dealing with monotone censoring due to early dropout. Here we focus on intermittent missingness that can depend on the subjects' covariate and treatment history. We show that under certain assumptions the standard SNMMs can be used for situations where non-outcome covariates are missing intermittently. In situations where outcomes are also missing intermittently, we use a method that does not require artificially censoring the data, but requires a strict missing at random assumption. The estimators are shown to be consistent and achieve reasonable efficiency. We illustrate the method by estimating the effect of non-steroidal anti-inflammatory drugs (NSAIDs) on genitourinary pain using data from a study of chronic pelvic pain.
Dissertation Advisor: Marshall Joffe, MD, MPH, PhD, Alisa Stephens, PhD Committee Chair: Russell Taki Shinohara, PhD Committee Members: John Kempen, MD, Linda Zhao, PhD
“Natural Language Processing of Clinical Texts”
Qing Zeng, PhD
University of Utah, Salt Lake City
The uses and misuses of meta-analyses and the nuts, bolts and infrastructure to make them happen
Kay Dickerson, MA, PhD
Johns Hopkins Bloomberg School of Public Health
“Temporal Dependencies with Time-Varying Exposures: Modelling Exposure-Lag-Response Associations”