Statistics and Probability Commons^™

Predicting The Future Subject's Outcome Via An Optimal Stratification Procedure With Baseline Information, Florence H. Yong, Lu Tian, Sheng Yu, Tianxi Cai, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Interadapt -- An Interactive Tool For Designing And Evaluating Randomized Trials With Adaptive Enrollment Criteria, Aaron Joel Fisher, Harris Jaffee, Michael Rosenblum

Johns Hopkins University, Dept. of Biostatistics Working Papers

The interAdapt R package is designed to be used by statisticians and clinical investigators to plan randomized trials. It can be used to determine if certain adaptive designs offer tangible benefits compared to standard designs, in the context of investigators’ specific trial goals and constraints. Specifically, interAdapt compares the performance of trial designs with adaptive enrollment criteria versus standard (non-adaptive) group sequential trial designs. Performance is compared in terms of power, expected trial duration, and expected sample size. Users can either work directly in the R console, or with a user-friendly shiny application that requires no programming experience. Several added …

Methods For Exploring Treatment Effect Heterogeneity In Subgroup Analysis: An Application To Global Clinical Trials, I. Manjula Schou, Ian C. Marschner

COBRA Preprint Series

Multi-country randomised clinical trials (MRCTs) are common in the medical literature and their interpretation has been the subject of extensive recent discussion. In many MRCTs, an evaluation of treatment effect homogeneity across countries or regions is conducted. Subgroup analysis principles require a significant test of interaction in order to claim heterogeneity of treatment effect across subgroups, such as countries in a MRCT. As clinical trials are typically underpowered for tests of interaction, overly optimistic expectations of treatment effect homogeneity can lead researchers, regulators and other stakeholders to over-interpret apparent differences between subgroups even when heterogeneity tests are insignificant. In this …

Pgs: A Tool For Association Study Of High-Dimensional Microrna Expression Data With Repeated Measures, Yinan Zheng, Zhe Fei, Wei Zhang, Justin Starren, Lei Liu, Andrea Baccarelli, Yi Li, Lifang Hou

The University of Michigan Department of Biostatistics Working Paper Series

Motivation: MicroRNAs (miRNAs) are short single-stranded non-coding molecules that usually function as negative regulators to silence or suppress gene expression. Due to interested in the dynamic nature of the miRNA and reduced microarray and sequencing costs, a growing number of researchers are now measuring high-dimensional miRNAs expression data using repeated or multiple measures in which each individual has more than one sample collected and measured over time. However, the commonly used site-by-site multiple testing may impair the value of repeated or multiple measures data by ignoring the inherent dependent structure, which lead to problems including underpowered results after multiple comparison …

Targeted Maximum Likelihood Estimation Using Exponential Families, Iván Díaz, Michael Rosenblum

Johns Hopkins University, Dept. of Biostatistics Working Papers

Targeted maximum likelihood estimation (TMLE) is a general method for estimating parameters in semiparametric and nonparametric models. Each iteration of TMLE involves fitting a parametric submodel that targets the parameter of interest. We investigate the use of exponential families to define the parametric submodel. This implementation of TMLE gives a general approach for estimating any smooth parameter in the nonparametric model. A computational advantage of this approach is that each iteration of TMLE involves estimation of a parameter in an exponential family, which is a convex optimization problem for which software implementing reliable and computationally efficient methods exists. We illustrate …

Partially-Latent Class Models (Plcm) For Case-Control Studies Of Childhood Pneumonia Etiology, Zhenke Wu, Maria Deloria-Knoll, Laura L. Hammitt, Scott L. Zeger

Johns Hopkins University, Dept. of Biostatistics Working Papers

In population studies on the etiology of disease, one goal is the estimation of the fraction of cases attributable to each of several causes. For example, pneumonia is a clinical diagnosis of lung infection that may be caused by viral, bacterial, fungal, or other pathogens. The study of pneumonia etiology is challenging because directly sampling from the lung to identify the etiologic pathogen is not standard clinical practice in most settings. Instead, measurements from multiple peripheral specimens are made. This paper considers the problem of estimating the population etiology distribution and the individual etiology probabilities. We formulate the scientific …

Deductive Derivation And Computerization Of Compatible Semiparametric Efficient Estimation, Constantine E. Frangakis, Tianchen Qian, Zhenke Wu, Ivan Diaz

U.C. Berkeley Division of Biostatistics Working Paper Series

Researchers often seek robust inference for a parameter through semiparametric estimation. Efficient semiparametric estimation currently requires theoretical derivation of the efficient influence function (EIF), which can be a challenging and time-consuming task. If this task can be computerized, it can save dramatic human effort, which can be transferred, for example, to the design of new studies. Although the EIF is, in principle, a derivative, simple numerical differentiation to calculate the EIF by a computer masks the EIF's functional dependence on the parameter of interest. For this reason, the standard approach to obtaining the EIF has been the theoretical construction of …

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 …

Nonparametric Identifiability Of Finite Mixture Models With Covariates For Estimating Error Rate Without A Gold Standard, Zheyu Wang, Xiao-Hua Zhou

UW Biostatistics Working Paper Series

Finite mixture models provide a flexible framework to study unobserved entities and have arisen in many statistical applications. The flexibility of these models in adapting various complicated structures makes it crucial to establish model identifiability when applying them in practice to ensure study validity and interpretation. However, researches to establish the identifiability of finite mixture model are limited and are usually restricted to a few specific model configurations. Conditions for model identifiability in the general case have not been established. In this paper, we provide conditions for both local identifiability and global identifiability of a finite mixture model. The former …

A Scalable Supervised Subsemble Prediction Algorithm, Stephanie Sapp, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Subsemble is a flexible ensemble method that partitions a full data set into subsets of observations, fits the same algorithm on each subset, and uses a tailored form of V-fold cross-validation to construct a prediction function that combines the subset-specific fits with a second metalearner algorithm. Previous work studied the performance of Subsemble with subsets created randomly, and showed that these types of Subsembles often result in better prediction performance than the underlying algorithm fit just once on the full dataset. Since the final Subsemble estimator varies depending on the data used to create the subset-specific fits, different strategies for …

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 …

Computational Model For Survey And Trend Analysis Of Patients With Endometriosis : A Decision Aid Tool For Ebm, Salvo Reina, Vito Reina, Franco Ameglio, Mauro Costa, Alessandro Fasciani

COBRA Preprint Series

Endometriosis is increasingly collecting worldwide attention due to its medical complexity and social impact. The European community has identified this as a “social disease”. A large amount of information comes from scientists, yet several aspects of this pathology and staging criteria need to be clearly defined on a suitable number of individuals. In fact, available studies on endometriosis are not easily comparable due to a lack of standardized criteria to collect patients’ informations and scarce definitions of symptoms. Currently, only retrospective surgical stadiation is used to measure pathology intensity, while the Evidence Based Medicine (EBM) requires shareable methods and correct …

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. …

Adaptive Randomized Trial Designs That Cannot Be Dominated By Any Standard Design At The Same Total Sample Size, Michael Rosenblum

Johns Hopkins University, Dept. of Biostatistics Working Papers

Prior work has shown that certain types of adaptive designs can always be dominated by a suitably chosen, standard, group sequential design. This applies to adaptive designs with rules for modifying the total sample size. A natural question is whether analogous results hold for other types of adaptive designs. We focus on adaptive enrichment designs, which involve preplanned rules for modifying enrollment criteria based on accrued data in a randomized trial. Such designs often involve multiple hypotheses, e.g., one for the total population and one for a predefined subpopulation, such as those with high disease severity at baseline. We fix …

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 …