Biostatistics Commons^™

Dose Expansion Cohorts In Phase I Trials, Alexia Iasonos, John O'Quigley

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

A rapidly increasing number of Phase I dose-finding studies, and in particular those based on the standard 3+3 design, frequently prolong the study and include dose expansion cohorts (DEC) with the goal to better characterize the toxicity profiles of experimental agents and to study disease specific cohorts. These trials consist of two phases: the usual dose escalation phase that aims to establish the maximum tolerated dose (MTD) and the dose expansion phase that accrues additional patients, often with different eligibility criteria, and where additional information is being collected. Current protocols typically do not specify whether the MTD will be updated …

Variable Selection For Zero-Inflated And Overdispersed Data With Application To Health Care Demand In Germany, Zhu Wang, Shuangge Ma, Ching-Yun Wang

COBRA Preprint Series

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized …

Targeted Covariate-Adjusted Response-Adaptive Lasso-Based Randomized Controlled Trials, Antoine Chambaz, Wenjing Zheng, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We present a new covariate-adjusted response-adaptive randomized controlled trial design and inferential procedure built on top of it. The procedure is targeted in the sense that (i) the sequence of randomization schemes is group-sequentially determined by targeting a user-specified optimal randomization design based on accruing data and, (ii) our estimator of the user-specified parameter of interest, seen as the value of a functional evaluated at the true, unknown distribution of the data, is targeted toward it by following the paradigm of targeted minimum loss estimation. We focus for clarity on the case that the parameter of interest is the marginal …

Variable-Domain Functional Regression For Modeling Icu Data, Jonathan E. Gellar, Elizabeth Colantuoni, Dale M. Needham, Ciprian M. Crainiceanu

Johns Hopkins University, Dept. of Biostatistics Working Papers

We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registration. Methods were motivated by and applied to a study of association between daily measures of the Intensive Care Unit (ICU) Sequential Organ Failure Assessment (SOFA) score and two outcomes: in-hospital mortality, and physical impairment …

Sieve Plateau Variance Estimators: A New Approach To Confidence Interval Estimation For Dependent Data, Molly M. Davies, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Suppose we have a data set of n-observations where the extent of dependence between them is poorly understood. We assume we have an estimator that is squareroot-consistent for a particular estimand, and the dependence structure is weak enough so that the standardized estimator is asymptotically normally distributed. Our goal is to estimate the asymptotic variance of the standardized estimator so that we can construct a Wald-type confidence interval for the estimate. In this paper we present an approach that allows us to learn this asymptotic variance from a sequence of influence function based candidate variance estimators. We focus on time …

Pre-Maceration, Saignée And Temperature Affect Daily Evolution Of Pigment Extraction During Vinification, Ottorino L. Pantani, Federico M. Stefanini, Irene Lozzi, Luca Calamai, Alessandra Biondi Bartolini, Stefano Di Blasi

COBRA Preprint Series

Consumer demand for intensely coloured wines necessitates the systematic testing of pigment extraction in Sangiovese, a cultivar poor in easily extractable anthocyanins. Pre-fermentation (absent, cold soak pre-fermentation at 5 °C, cryomaceration by liquid N₂ addition), temperature (20 or 30 °C), and saignée were compared during vinification (800 kg). Concentrations of anthocyanins, non-anthocyanic flavonoids and SO₂-resistant pigments were recorded daily. A semiparametric Bayesian model permitted the kinetic description and the comparison of sigmoidal- and exponential-like curves. In total anthocyanins, saignée at 30 °C yielded a significant gain, later lost at drawing off; cryomaceration had little effect and cold …

Bounds To Evaluate The Pure/Natural Direct Effect Without Cross-World Counterfactual Independence, Eric Tchetgen Tchetgen, Kelesitse Phiri

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Global Partial Likelihood Estimator Of The Time-Varying Effects For Time-Dependent Treatment, Huazhen Lin, Zhe Fei, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

The timing of time-dependent treatment - e.g., when to perform kidney transplantation - is an important factor for evaluating treatment efficacy. A naive comparison between the treatment and nontreatment groups, while ignoring the timing of treatment, typically yields results that might biasedly favor the treatment group, as only patients who survive long enough will get treated. On the other hand, studying the effect of time-dependent treatment is often complex, as it involves modeling treatment history and accounting for the possible time-varying nature of the treatment effect. We propose a varying-coefficient Cox model that investigates the efficacy of time-dependent treatment by …

A Unification Of Mediation And Interaction: A Four-Way Decomposition, Tyler J. Vanderweele

Harvard University Biostatistics Working Paper Series

It is shown that the overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into four components: (i) the effect of the exposure in the absence of the mediator, (ii) the interactive effect when the mediator is left to what it would be in the absence of exposure, (iii) a mediated interaction, and (iv) a pure mediated effect. These four components, respectively, correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both mediation …

Efficiently Identifying Failures Using Quantitative Tests, Matrix-Pooling And The Em-Algorithm, Brett Hanscom, Susanne May, Jim Hughes

UW Biostatistics Working Paper Series

Pooled-testing methods can greatly reduce the number of tests needed to identify failures in a collection of samples. Existing methodology has focused primarily on binary tests, but there is a clear need for improved efficiency when using expensive quantitative tests, such as tests for HIV viral load in resource-limited settings. We propose a matrix-pooling method which, based on pooled-test results, uses the EM algorithm to identify individual samples most likely to be failures. Two hundred datasets for each of a wide range of failure prevalence were simulated to test the method. When the measurement of interest was normally distributed, at …

A Predictive Enrichment Procedure To Identify Potential Responders To A New Therapy For Randomized, Comparative, Controlled Clinical Studies, Junlong Li, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, Andrea Callegaro, Benjamin Dizier, Bart Spiessens, Fernando Ulloa-Montoya, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Clustering Survival Outcomes Using Dirichlet Process Mixture, Lili Zhao, Jingchunzi Shi, Tempie H. Shearon, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Motivated by the national evaluation of mortality rates at kidney transplant centers in the United States, we sought to assess transplant center long- term survival outcomes by applying a methodology developed in Bayesian non-parametrics literature. We described a Dirichlet process model and a Dirichlet process mixture model with a Half-Cauchy for the estimation of the risk- adjusted effects of the transplant centers. To improve the model performance and interpretability, we centered the Dirichlet process. We also proposed strategies to increase model's classification ability. Finally we derived statistical measures and created graphical tools to rate transplant centers and identify outlying centers …

Mediation Analysis With Time-Varying Exposures And Mediators, Tyler J. Vanderweele, Eric Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

In this paper we consider mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation, and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are no time-varying confounders affected by prior exposure and mediator values, identification of direct and indirect effects is achieved by a longitudinal version of Pearl's mediation formula. When there are time-varying confounders affected …

Regression Modeling Of Longitudinal Binary Outcomes With Outcome-Dependent Observation Times, Kay See Tan, Andrea B. Troxel, Stephen E. Kimmel, Kevin G. Volpp, Benjamin French

UPenn Biostatistics Working Papers

Conventional longitudinal data analysis methods assume that outcomes are independent of the data-collection schedule. However, the independence assumption may be violated, for example, when adverse events trigger additional physician visits in between prescheduled follow-ups. Observation times may therefore be associated with outcome values, which may introduce bias when estimating the eect of covariates on outcomes using standard longitudinal regression methods. Existing semi-parametric methods that accommodate outcome-dependent observation times are limited to the analysis of continuous outcomes. We develop new methods for the analysis of binary outcomes, while retaining the exibility of semi-parametric models. Our methods are based on counting process …

Bayesian Model Averaging:- An Application In Cancer Clinical Trial, Atanu Bhattacharjee

COBRA Preprint Series

Data driven conclusion is mostly accepted approach in any medical research problem. In case of limited knowledge of deep idea about supportive data on the problem, automatic digging of the variable plays important role for insight view of the study. Bayesian model averaging can be considered for automatics variable selection. It can be used as an alternative of stepwise regression method. The aim of this paper is to show the application of Bayesian modeling averaging in medical research particularly in cancer trial. Method is illustrated on Bone marrow transplant data. It can be recommended that BMA can be used frequently …

Adaptive Pair-Matching In The Search Trial And Estimation Of The Intervention Effect, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentially increase study power. In a common design, candidate units are identified, and their baseline characteristics used to create the best n/2 matched pairs. Within the resulting pairs, the intervention is randomized, and the outcomes measured at the end of follow-up. We consider this design to be adaptive, because the construction of the matched pairs depends on the baseline covariates of all candidate units. As consequence, the observed data cannot be considered as n/2 independent, identically distributed (i.i.d.) pairs of units, as current practice assumes. …

A Joint Model For Multistate Disease Processes And Random Informative Observation Times, With Applications To Electronic Medical Records Data, Jane M. Lange, Rebecca A. Hubbard, Lurdes Y. T. Inoue, Vladimir Minin

UW Biostatistics Working Paper Series

Multistate models are used to characterize individuals' natural histories through diseases with discrete states. Observational data resources based on electronic medical records pose new opportunities for studying such diseases. However, these data consist of observations of the process at discrete sampling times, which may either be pre-scheduled and non-informative, or symptom-driven and informative about an individual's underlying disease status. We have developed a novel joint observation and disease transition model for this setting. The disease process is modeled according to a latent continuous time Markov chain; and the observation process, according to a Markov-modulated Poisson process with observation rates that …

Change Point Testing In Logistic Regression Models With Interaction Term, Youyi Fong, Chongzhi Di, Sallie Permar

UW Biostatistics Working Paper Series

The threshold effect takes place in situations where the relationship between an outcome variable and a predictor variable changes as the predictor value crosses a certain threshold/change point. Threshold effects are often plausible in a complex biological system, especially in defining immune responses that are protective against infections such as HIV-1, which motivates the current work. We study two hypothesis testing problems in change point models. We first compare three different approaches to obtaining a p-value for the maximum of scores test in a logistic regression model with change point variable as a main effect. Next, we study the testing …

Extending Mendelian Risk Prediction Models To Handle Misreported Family History, Danielle Braun, Malka Gorfine, Hormuzd A. Katki, Argyrios Ziogas, Hoda Anton-Culver, Giovanni Parmigiani

Harvard University Biostatistics Working Paper Series

Mendelian risk prediction models calculate the probability of a proband being a mutation carrier based on family history and known mutation prevalence and penetrance. Family history in this setting, is self-reported and is often reported with error. Various studies in the literature have evaluated misreporting of family history. Using a validation data set which includes both error-prone self-reported family history and error-free validated family history, we propose a method to adjust for misreporting of family history. We estimate the measurement error process in a validation data set (from University of California at Irvine (UCI)) using nonparametric smoothed Kaplan-Meier estimators, and …

Nonparametric Adjustment For Measurement Error In Time To Event Data, Danielle Braun, Malka Gorfine, Hormuzd A. Katki, Argyrios Ziogas, Giovanni Parmigiani

Harvard University Biostatistics Working Paper Series

Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using a validation data set, we propose a method to adjust for this type of measurement error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian risk prediction models and multivariate survival prediction models, as …

Estimating Population Treatment Effects From A Survey Sub-Sample, Kara E. Rudolph, Ivan Diaz, Michael Rosenblum, Elizabeth A. Stuart

Johns Hopkins University, Dept. of Biostatistics Working Papers

We consider the problem of estimating an average treatment effect for a target population from a survey sub-sample. Our motivating example is generalizing a treatment effect estimated in a sub-sample of the National Comorbidity Survey Replication Adolescent Supplement to the population of U.S. adolescents. To address this problem, we evaluate easy-to-implement methods that account for both non-random treatment assignment and a non-random two-stage selection mechanism. We compare the performance of a Horvitz-Thompson estimator using inverse probability weighting (IPW) and two double robust estimators in a variety of scenarios. We demonstrate that the two double robust estimators generally outperform IPW in …

Set-Based Tests For Genetic Association In Longitudinal Studies, Zihuai He, Min Zhang, Seunggeun Lee, Jennifer A. Smith, Xiuqing Guo, Walter Palmas, Sharon L.R. Kardia, Ana V. Diez Roux, Bhramar Mukherjee

The University of Michigan Department of Biostatistics Working Paper Series

Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set …

Issues Related To Combining Multiple Speciated Pm2.5 Data Sources In Spatio-Temporal Exposure Models For Epidemiology: The Npact Case Study, Sun-Young Kim, Lianne Sheppard, Timothy V. Larson, Joel Kaufman, Sverre Vedal

UW Biostatistics Working Paper Series

Background: Regulatory monitoring data have been the most common exposure data resource in studies of the association between long-term PM_2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological study.

Objectives: We aimed to explore three important features of the PM_2.5 component monitoring data obtained from multiple sources to combine all available data for developing spatio-temporal prediction models in the National Particle Component and Toxicity (NPACT) study.

Methods: The NPACT monitoring data were collected in an extensive monitoring campaign targeting cohort participants. The regulatory monitoring data were obtained from the Chemical Speciation …

Prediction Of Fine Particulate Matter Chemical Components For The Multi-Ethnic Study Of Atherosclerosis Cohort: A Comparison Of Two Modeling Approaches, Sun-Young Kim, Lianne Sheppard, Silas Bergen, Adam A. Szpiro, Paul D. Sampson, Joel Kaufman, Sverre Vedal

UW Biostatistics Working Paper Series

Recent epidemiological cohort studies of the health effects of PM_2.5 have developed exposure estimates from advanced exposure prediction models. Such models represent spatial variability across participant residential locations. However, few cohort studies have developed exposure predictions for PM_2.5 components. We used two exposure modeling approaches to obtain long-term average predicted concentrations for four PM_2.5 components: sulfur, silicon, and elemental and organic carbon (EC and OC). The models were specifically developed for the Multi-Ethnic Study of Atherosclerosis (MESA) cohort as a part of the National Particle Component and Toxicity (NPACT) study. The spatio-temporal model used 2-week average measurements …

Characterizing Expected Benefits Of Biomarkers In Treatment Selection, Ying Huang, Eric Laber, Holly Janes

UW Biostatistics Working Paper Series

Biomarkers associated with the treatment response heterogeneity hold potential for treatment selection. In practice, the decision regarding whether to adopt a treatment selection marker depends on the effect of treatment selection on the rate of targeted disease as well as additional cost associated with the treatment. We propose an expected benefit measure that incorporates both aspects to quantify a biomarker's treatment selection capacity. This measure extends an existing decision-theoretic framework, to account for the fact that optimal treatment absent marker information varies with the cost of treatment. In addition, we establish upper and lower bounds for the performance of a …

A General Instrumental Variable Framework For Regression Analysis With Outcome Missing Not At Random, Eric J. Tchetgen Tchetgen, Kathleen Wirth

Harvard University Biostatistics Working Paper Series

No abstract provided.

Alternative Identification And Inference For The Effect Of Treatment On The Treated With An Instrumental Variable, Eric J. Tchetgen Tchetgen, Stijn Vansteelandt

Harvard University Biostatistics Working Paper Series

No abstract provided.

Identification And Estimation Of Survivor Average Causal Effects, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

On The Causal Interpretation Of Race In Regressions Adjusting For Confounding And Mediating Variables, Tyler J. Vanderweele, Whitney Robinson

Harvard University Biostatistics Working Paper Series

We consider different possible interpretations of the “effect of race” when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person's life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial disparity would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall disparity can be decomposed into the portion that …

A Unification Of Mediation And Interaction, Tyler J. Vanderweele

Harvard University Biostatistics Working Paper Series

We show that the overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into four components: (i) the effect of the exposure in the absence of the mediator, (ii) the interactive effect when the mediator is left to what is would be in the absence of exposure, (iii) a mediated interaction and (iv) a pure mediated effect. These four components respectively correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both mediation and …