Biostatistics Commons^™

Testing For Improvement In Prediction Model Performance, Margaret S. Pepe Phd, Kathleen F. Kerr, Gary M. Longton, Zheyu Wang

UW Biostatistics Working Paper Series

New methodology has been proposed in recent years for evaluating the improvement in prediction performance gained by adding a new predictor, Y, to a risk model containing a set of baseline predictors, X, for a binary outcome D. We prove theoretically that null hypotheses concerning no improvement in performance are equivalent to the simple null hypothesis that the coefficient for Y is zero in the risk model, P(D = 1|X, Y ). Therefore, testing for improvement in prediction performance is redundant if Y has already been shown to be a risk factor. We investigate properties of tests through simulation studies, …

Hierarchical Rank Aggregation With Applications To Nanotoxicology, Trina Patel, Donatello Telesca, Robert Rallo, Saji George, Xia Tian, Nel Andre

COBRA Preprint Series

The development of high throughput screening (HTS) assays in the field of nanotoxicology provide new opportunities for the hazard assessment and ranking of engineered nanomaterials (ENM). It is often necessary to rank lists of materials based on multiple risk assessment parameters, often aggregated across several measures of toxicity and possibly spanning an array of experimental platforms. Bayesian models coupled with the optimization of loss functions have been shown to provide an effective framework for conducting inference on ranks. In this article we present various loss function based ranking approaches for comparing ENM within experiments and toxicity parameters. Additionally, we propose …

Robustness Of Measures Of Interaction To Unmeasured Confounding, Eric J. Tchetgen Tchetgen, Tyler J. Vanderweele

Harvard University Biostatistics Working Paper Series

No abstract provided.

Bootstrap-Based Inference On The Difference In The Means Of Two Correlated Functional Processes, Ciprian M. Crainiceanu, Ana-Maria Staicu, Shubankar Ray, Naresh Punjabi

Johns Hopkins University, Dept. of Biostatistics Working Papers

Nonparametric inference methods on the mean difference between two correlated Functional processes are proposed. We compare methods that: 1) incorporate different levels of smoothing of the mean and covariance; 2) preserve the sampling design; and 3) use parametric and nonparametric estimation of the mean functions. We apply our method to estimating the mean difference between average normalized δ-power of sleep electroencephalograms for 51 subjects with severe sleep apnea and 51 matched controls in the first 4 hours after sleep onset. Data are obtained from the Sleep Heart Health Study (SHHS), the largest community cohort study of sleep. While methods are …

Robustness Of Measures Of Interaction To Unmeasured Confounding, Eric J. Tchetgen Tchetgen, Tyler J. Vanderweele

COBRA Preprint Series

In this paper, we study the impact of unmeasured confounding on inference about a two-way interaction in a mean regression model with identity, log or logit link function. Necessary and sufficient conditions are established for a two-way interaction to be nonparametrically identified from the observed data, despite unmeasured confounding for the factors defining the interaction. A lung cancer data application illustrates the results.

On A Logistic Mixed Model Formulation Of A Quadratic Exponential Model For Correlated Binary Outcomes, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

On A Closed-Form Doubly Robust Estimator Of The Adjusted Odds Ratio For A Binary Exposure, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

Formulae For Causal Mediation Analysis In An Odds Ratio Context Without A Normality Assumption For The Continuous Mediator, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Cautionary Note On Specification Of The Correlation Structure In Inverse-Probability-Weighted Estimation For Repeated Measures, Eric J. Tchetgen Tchetgen, M. Maria Glymour, Jennifer Weuve, James Robins

Harvard University Biostatistics Working Paper Series

No abstract provided.

On Parametrization, Robustness And Sensitivity Analysis In A Marginal Structural Cox Proportional Hazards Model For Point Exposure, Eric J. Tchetgen Tchetgen, James M. Robins

Harvard University Biostatistics Working Paper Series

No abstract provided.

Multiple-Robust Estimation Of An Odds Ratio Interaction, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

Inverse Odds Ratio-Weighted Estimation For Causal Mediation Analysis, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Systematic Selection Method For The Development Of Cancer Staging Systems, Yunzhi Lin, Richard Chappell, Mithat Gonen

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

The tumor-node-metastasis (TNM) staging system has been the anchor of cancer diagnosis, treatment, and prognosis for many years. For meaningful clinical use, an orderly, progressive condensation of the T and N categories into an overall staging system needs to be defined, usually with respect to a time-to-event outcome. This can be considered as a cutpoint selection problem for a censored response partitioned with respect to two ordered categorical covariates and their interaction. The aim is to select the best grouping of the TN categories. A novel bootstrap cutpoint/model selection method is proposed for this task by maximizing bootstrap estimates of …

On Identification Of Natural Direct Effects When A Confounder Of The Mediator Is Directly Affected By Exposure, Eric J. Tchetgen Tchetgen, Tyler J. Vanderweele

Harvard University Biostatistics Working Paper Series

No abstract provided.

Analysis Of Periodontal Data Using Circular Statistics, Samopriyo Maitra, Thomas M. Braun

The University of Michigan Department of Biostatistics Working Paper Series

Periodontal disease is a common cause of tooth loss in adults. The severity of periodontal disease is usually quantified based upon the magnitudes of several tooth-level clinical parameters, the most common of which is clinical attachment level (CAL). Re- cent clinical studies have presented data on the distribution of periodontal disease in hopes of providing information for localized treatments that can reduce the prevalence of periodontal disease. However, these findings have been descriptive without consid- eration of statistical modeling for estimation and inference. To this end, we visualize the mouth as a circle and the teeth as points located on …

The Bayesian Continual Reassessment Method Using A Mixture-Of-Uniforms Prior, Thomas M. Braun

The University of Michigan Department of Biostatistics Working Paper Series

Traditionally, the Bayesian formulation of the Continual Reassessment Method (CRM) is implemented with a one-parameter model describing the association of dose with the probability of dose-limiting toxicity (DLT). Determination of the appropriate value of the prior variance is often done via simulation over a grid search of possible values until suitable operating characteristics are found. However, it is under-appreciated that the operating characteristics for a given value of the prior variance vary by the “skeleton,” which is the vector of a priori probabilities of DLT for each dose. The skeleton implicitly leads to a set of indifference intervals, with one …

Flexible Distributed Lag Models Using Random Functions With Application To Estimating Mortality Displacement From Heat-Related Deaths, Roger D. Peng

Johns Hopkins University, Dept. of Biostatistics Working Papers

No abstract provided.

Proxy Pattern-Mixture Analysis For A Binary Variable Subject To Nonresponse., Rebecca H. Andridge, Roderick J. Little

The University of Michigan Department of Biostatistics Working Paper Series

We consider assessment of the impact of nonresponse for a binary survey

variable Y subject to nonresponse, when there is a set of covariates

observed for nonrespondents and respondents. To reduce dimensionality and

for simplicity we reduce the covariates to a continuous proxy variable X

that has the highest correlation with Y, estimated from a probit

regression analysis of respondent data. We extend our previously proposed

proxy-pattern mixture analysis (PPMA) for continuous outcomes to the binary

outcome using a latent variable approach. The method does not assume data

are missing at random, and creates a framework for sensitivity analyses.

Maximum …

Estimation Of A Non-Parametric Variable Importance Measure Of A Continuous Exposure, Chambaz Antoine, Pierre Neuvial, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We define a new measure of variable importance of an exposure on a continuous outcome, accounting for potential confounders. The exposure features a reference level x0 with positive mass and a continuum of other levels. For the purpose of estimating it, we fully develop the semi-parametric estimation methodology called targeted minimum loss estimation methodology (TMLE) [van der Laan & Rubin, 2006; van der Laan & Rose, 2011]. We cover the whole spectrum of its theoretical study (convergence of the iterative procedure which is at the core of the TMLE methodology; consistency and asymptotic normality of the estimator), practical implementation, simulation …

Bland-Altman Plots For Evaluating Agreement Between Solid Tumor Measurements, Chaya S. Moskowitz, Mithat Gonen

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

Rationale and Objectives. Solid tumor measurements are regularly used in clinical trials of anticancer therapeutic agents and in clinical practice managing patients' care. Consequently studies evaluating the reproducibility of solid tumor measurements are important as lack of reproducibility may directly affect patient management. The authors propose utilizing a modified Bland-Altman plot with a difference metric that lends itself naturally to this situation and facilitates interpretation. Materials and Methods. The modification to the Bland-Altman plot involves replacing the difference plotted on the vertical axis with the relative percent change (RC) between the two measurements. This quantity is the same one used …

A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error, David M. Zucker, Malka Gorfine, Yi Li, Donna Spiegelman

Harvard University Biostatistics Working Paper Series

No abstract provided.

Longitudinal Analysis Of Spatiotemporal Processes: A Case Study Of Dynamic Contrast-Enhanced Magnetic Resonance Imaging In Multiple Sclerosis, Russell T. Shinohara, Ciprian M. Crainiceanu, Brian S. Caffo, Daniel S. Reich

Johns Hopkins University, Dept. of Biostatistics Working Papers

Multiple sclerosis (MS) is an immune-mediated disease in which inflammatory lesions form in the brain. In many active MS lesions, the blood-brain barrier (BBB) is disrupted and blood flows into white matter; this disruption may be related to morbidity and disability. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows quantitative study of blood flow and permeability dynamics throughout the brain. This technique involves a subject being imaged sequentially during a study visit as an intravenously administered contrast agent flows into the brain. In regions where flow is abnormal, such as white matter lesions, this allows the quantification of the BBB damage. …

Movelets: A Dictionary Of Movement, Jiawei Bai, Jeff Goldsmith, Brian Caffo, Thomas A. Glass, Ciprian M. Crainiceanu

Johns Hopkins University, Dept. of Biostatistics Working Papers

Recent technological advances provide researchers a way of gathering real-time information on an individual’s movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called “movelets”, and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage …

Some Observations On The Wilcoxon Rank Sum Test, Scott S. Emerson

UW Biostatistics Working Paper Series

This manuscript presents some general comments about the Wilcoxon rank sum test. Even the most casual reader will gather that I am not too impressed with the scientific usefulness of the Wilcoxon test. However, the actual motivation is more to illustrate differences between parametric, semiparametric, and nonparametric (distribution-free) inference, and to use this example to illustrate how many misconceptions have been propagated through a focus on (semi)parametric probability models as the basis for evaluating commonly used statistical analysis models. The document itself arose as a teaching tool for courses aimed at graduate students in biostatistics and statistics, with parts of …

The Importance Of Statistical Theory In Outlier Detection, Sarah C. Emerson, Scott S. Emerson

UW Biostatistics Working Paper Series

We explore the performance of the outlier-sum statistic (Tibshirani and Hastie, Biostatistics 2007 8:2--8), a proposed method for identifying genes for which only a subset of a group of samples or patients exhibits differential expression levels. Our discussion focuses on this method as an example of how inattention to standard statistical theory can lead to approaches that exhibit some serious drawbacks. In contrast to the results presented by those authors, when comparing this method to several variations of the $t$-test, we find that the proposed method offers little benefit even in the most idealized scenarios, and suffers from a number …

Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei

Harvard University Biostatistics Working Paper Series

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. …

Targeted Minimum Loss Based Estimation Of An Intervention Specific Mean Outcome, Mark J. Van Der Laan, Susan Gruber

U.C. Berkeley Division of Biostatistics Working Paper Series

Targeted minimum loss based estimation (TMLE) provides a template for the construction of semiparametric locally efficient double robust substitution estimators of the target parameter of the data generating distribution in a semiparametric censored data or causal inference model based on a sample of independent and identically distributed copies from this data generating distribution (van der Laan and Rubin (2006), van der Laan (2008), van der Laan and Rose (2011)). TMLE requires 1) writing the target parameter as a particular mapping from a typically infinite dimensional parameter of the probability distribution of the unit data structure into the parameter space, 2) …

Population Intervention Causal Effects Based On Stochastic Interventions, Ivan Diaz Munoz, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Estimating the causal effect of an intervention on a population typically involves defining parameters in a nonparametric structural equation model (Pearl, 2000, NPSEM) in which the treatment or exposure is deter- ministically assigned in a static or dynamic way. We define a new causal parameter that takes into account the fact that intervention policies can result in stochastically assigned exposures. The statistical parameter that identifies the causal parameter of interest is established. Inverse probability of treatment weighting (IPTW), augmented IPTW (A-IPTW), and targeted maximum likelihood estimators (TMLE) are developed. A simulation study is performed to demonstrate the properties of these …

Targeted Maximum Likelihood Estimation Of Natural Direct Effect, Wenjing Zheng, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In many causal inference problems, one is interested in the direct causal effect of an exposure on an outcome of interest that is not mediated by certain intermediate variables. Robins and Greenland (1992) and Pearl (2000) formalized the definition of two types of direct effects (natural and controlled) under the counterfactual framework. Since then, identifiability conditions for these effects have been studied extensively. By contrast, considerably fewer efforts have been invested in the estimation problem of the natural direct effect. In this article, we propose a semiparametric efficient, multiply robust estimator for the natural direct effect of a binary treatment …

On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.