Biostatistics Commons^™

Using Longitudinal Data To Estimate The Effect Of Starting To Exercise On The Health Of Sedentary Older Adults, Paula Diehr, Calvin Hirsch

UW Biostatistics Working Paper Series

Background It is difficult to estimate the effect of exercise on future health from observational data because exercising may be both a cause and an effect of health status. Unadjusted analyses suffer from selection bias (healthier persons more likely to exercise), while adjusted analyses may adjust away some of the benefits of exercise.

Objective To obtain a "low-bias" interpretable estimate of the effect of exercise on future health.

Methods We used data from the Cardiovascular Health Study, a longitudinal study of 5,888 older adults. The number of blocks walked in the previous week, collected annually, were classified as Sedentary (less …

Why Match? Investigating Matched Case-Control Study Designs With Causal Effect Estimation, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Matched case-control study designs are commonly implemented in the field of public health. While matching is intended to eliminate confounding, the main potential benefit of matching in case-control studies is a gain in efficiency. Methods for analyzing matched case-control studies have focused on utilizing conditional logistic regression models that provide conditional and not causal estimates of the odds ratio. This article investigates the use of case-control weighted targeted maximum likelihood estimation to obtain marginal causal effects in matched case-control study designs. We compare the use of case-control weighted targeted maximum likelihood estimation in matched and unmatched designs in an effort …

A Novel And Simple Rule Of Thumb For Multiplicity Control In Equivalence Testing Using Two One-Sided Tests, Carolyn Lauzon, Brian S. Caffo

Johns Hopkins University, Dept. of Biostatistics Working Papers

Equivalence testing is growing in use in scientific research outside of its traditional role in the drug approval process. Largely due to its ease of use and recommendation from the United States Food and Drug Administration guidance, the most common statistical method for testing (bio)equivalence is the two one-sided tests procedure (TOST). Like classical point-null hypothesis testing, TOST is subject to multiplicity concerns as more comparisons are made. In this manuscript, a condition that bounds the family-wise error rate (FWER) using TOST is given. This condition then leads to a simple solution for controlling the FWER. Specifically, we demonstrate that …

Estimating The Causal Effect Of Lower Tidal Volume Ventilation On Survival In Patients With Acute Lung Injury, Weiwei Wang, Daniel Scharfstein, Roy Brower, Dale Needham

Johns Hopkins University, Dept. of Biostatistics Working Papers

Acute lung injury (ALI) is a condition characterized by acute onset of severe hypoxemia and bliateral pulmonary infiltrates. ALI patients typically require mechanical ventilation in an intensive care unit. Low tidal volume ventilation (LTVV), a time-varying dynamic treatment regime, has been recommended as an effective ventilation strategy. This recommendation was based on the results of the ARMA study, a randomized clinical trial designed to compare low vs. high tidal volume strategies (ARDSNetwork, 2000) . After publication of the trial, some critics focused on the high non-adherence rates in the LTVV arm suggesting that non-adherence occurred because treating physicians felt that …

Bayesian Inference For Smoking Cessation With A Latent Cure State, Sheng Luo, Ciprian M. Crainiceanu, Thomas A. Louis, Nilanjan Chatterjee

Johns Hopkins University, Dept. of Biostatistics Working Papers

We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-specic probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random e ects is used to account for the potential correlation among the subject-specic transition probabilities. Inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation. This framework provides various measures of subject-specic predictions, which are useful for policy making, intervention development, and evaluation. Simulations are used to validate our Bayesian methodology, and …

Semiparametric And Nonparametric Methods For Evaluating Risk Prediction Markers In Case-Control Studies, Ying Huang, Margaret Pepe

UW Biostatistics Working Paper Series

The performance of a well calibrated risk model, Risk(Y)=P(D=1|Y), can be characterized by the population distribution of Risk(Y) and displayed with the predictiveness curve. Better performance is characterized by a wider distribution of Risk(Y), since this corresponds to better risk stratification in the sense that more subjects are identified at low and high risk for the outcome D=1. Although methods have been developed to estimate predictiveness curves from cohort studies, most studies to evaluate novel risk prediction markers employ case-control designs. Here we develop semiparametric and nonparametric methods that accommodate case-control data and assume apriori knowledge of P(D=1). Large and …

Estimation And Testing For The Effect Of A Genetic Pathway On A Disease Outcome Using Logistic Kernel Machine Regression Via Logistic Mixed Models, Dawei Liu, Debashis Ghosh, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Powerful And Flexible Multilocus Association Test For Quantitative Traits, Lydia Coulter Kwee, Dawei Liu, Xihong Lin, Debashis Ghosh, Michael P. Epstein

Harvard University Biostatistics Working Paper Series

No abstract provided.

Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Supervised Distance Matrices: Theory And Applications To Genomics, Katherine S. Pollard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We propose a new approach to studying the relationship between a very high dimensional random variable and an outcome. Our method is based on a novel concept, the supervised distance matrix, which quantifies pairwise similarity between variables based on their association with the outcome. A supervised distance matrix is derived in two stages. The first stage involves a transformation based on a particular model for association. In particular, one might regress the outcome on each variable and then use the residuals or the influence curve from each regression as a data transformation. In the second stage, a choice of distance …

Confidence Intervals For The Population Mean Tailored To Small Sample Sizes, With Applications To Survey Sampling, Michael Rosenblum, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

The validity of standard confidence intervals constructed in survey sampling is based on the central limit theorem. For small sample sizes, the central limit theorem may give a poor approximation, resulting in confidence intervals that are misleading. We discuss this issue and propose methods for constructing confidence intervals for the population mean tailored to small sample sizes.

We present a simple approach for constructing confidence intervals for the population mean based on tail bounds for the sample mean that are correct for all sample sizes. Bernstein's inequality provides one such tail bound. The resulting confidence intervals have guaranteed coverage probability …

Estimation Based On Case-Control Designs With Known Incidence Probability, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Case-control sampling is an extremely common design used to generate data to estimate effects of exposures or treatments on a binary outcome of interest when the proportion of cases (i.e., binary outcome equal to 1) in the population of interest is low. Case-control sampling represents a biased sample of a target population of interest by sampling a disproportional number of cases. Case-control studies are also commonly employed to estimate the effects of genetic markers or biomarkers on phenotypes. The typical approach used in practice is to fit (conditional) logistic regression models, ignoring the case-control sampling, in order to estimate the …

A Guide To Causal Parameters In Case-Control Designs: Targeted Maximum Likelihood Estimation, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Researchers of uncommon diseases are often interested in assessing potential risk factors. Given the low incidence of disease, these studies are frequently case-control in design, as this allows for a sufficient number of cases to be obtained without extensive sampling and can increase efficiency. However, these case-control samples are then biased since the proportion of cases in the sample is not the same as the population of interest. Methods for analyzing case-control studies have focused on utilizing logistic regression models that provide conditional and not causal estimates of the odds ratio. This article will demonstrate the use of the prevalence …

Semiparametric Methods For Evaluating The Covariate-Specific Predictiveness Of Continuous Markers In Matched Case-Control Studies, Ying Huang, Margaret S. Pepe

UW Biostatistics Working Paper Series

To assess the value of a continuous marker in predicting the risk of a disease, a graphical tool called the predictiveness curve has been proposed. It characterizes the marker's predictiveness, or capacity to risk stratify the population by displaying the population distribution of risk endowed by the marker. Methods for making inference about the curve and for comparing curves in a general population have been developed. However, knowledge about a marker's performance in the general population only is not enough. Since a marker's effect on the risk model and its distribution can both differ across subpopulations, its predictiveness may vary …

Nonparametric Heteroscedastic Transformation Regression Models For Skewed Data With An Application To Health Care Costs, Xiao-Hua Zhou, Huazhen Lin, Eric Johnson

UW Biostatistics Working Paper Series

No abstract provided.

Semiparametric Inferential Procedures For Comparing Multivariate Roc Curves With Interaction Terms, Liansheng Tang, Xiao-Hua Zhou

UW Biostatistics Working Paper Series

Multivariate ROC curve models that include an interaction term be- tween biomarker type and false positive rate is important in comparative biomarker studies, because such interaction allows ROC curves of different biomarkers to cross each other. However, there has been limited work in drawing inference for comparing multivariate ROC curves, especially when the interaction terms are present. In this article we derive the asymptotic covariance of three estimators for multivariate ROC models. These covariance estimates have not been readily available in the literature, and bootstrap methods have to be used to obtain co- variance estimates. With the readily available variance …

Matrix Pooling: An Accurate And Cost Effective Testing Algorithm For Detection Of Acute Hiv Infection, Bethany L. Hedt, Marcello Pagano

Harvard University Biostatistics Working Paper Series

No abstract provided.

Semi-Parametric Maximum Likelihood Estimates For Roc Curves Of Continuous-Scale Tests, Xiao-Hua Zhou, Huazhen Lin

UW Biostatistics Working Paper Series

No abstract provided.

A Matrix Pooling Algorithm For Disease Detection, Bethany L. Hedt, Marcello Pagano

Harvard University Biostatistics Working Paper Series

No abstract provided.

Nonparametric Inference Procedure For Percentiles Of The Random Effect Distribution In Meta Analysis, Rui Wang, Lu Tian, Tianxi Cai, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Bayesian Approach To Modeling Associations Between Pulsatile Hormones, Nichole E. Carlson, Timothy D. Johnson, Morton B. Brown

The University of Michigan Department of Biostatistics Working Paper Series

Many hormones are secreted in pulses. The pulsatile relationship between hormones regulates many biological processes. To understand endocrine system regulation, time series of hormone concentrations are collected. The goal is to characterize pulsatile patterns and associations between hormones. Currently each hormone on each subject is fitted univariately. This leads to estimates of the number of pulses and estimates of the amount of hormone secreted; however, when the signal-to-noise ratio is small, pulse detection and parameter estimation remains di±cult with existing approaches. In this paper, we present a bivariate deconvolution model of pulsatile hormone data focusing on incorporating pulsatile associations. Through …

Targeted Methods For Biomarker Discovery, The Search For A Standard, Catherine Tuglus, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

More often than not biomarker studies analyze large quantities of variables with complicated and generally unknown correlation structure. There are numerous statistical methods which attempt to unravel these variables and determine the underlying mechanism through identification of causally related biomarkers. Results from these methods are generally difficult to interpret and nearly impossible to compare across studies. The FDA has currently called for a standardization of methods and protocol for biomarker detection. In response, we propose targeted variable importance (tVIM) as a standardized method for biomarker discovery. Through the use of targeted Maximum Likelihood, tVIM provides double robust estimates of variable …

Data-Adaptive Selection Of The Truncation Level For Inverse-Probability-Of-Treatment-Weighted Estimators, Oliver Bembom, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Inverse-Probability-of-Treatment-Weighted (IPTW) estimators are becoming a popular analysis tool in causal inference. It is well known that these estimators suffer from high variability if some treatment probabilities are estimated to be close to zero. While it is a common recommendation for such situations to truncate the weights in order to reduce the mean squared error of the estimator, the current literature gives little guidance on how to select an appropriate truncation level. In this article, we develop a closed-form estimate for the mean squared error of a truncated IPTW estimator that can be used to select this truncation level data-adaptively. …

Data-Adaptive Selection Of The Adjustment Set In Variable Importance Estimation, Oliver Bembom, Jeffrey W. Fessel, Robert W. Shafer, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

If estimates of the effect of a treatment variable on an outcome of interest are to be adjusted for a set of possible confounding factors, it is necessary to rely on the assumption of experimental treatment assignment (ETA) according to which each experimental unit has positive probability of being observed at any of the possible levels of the treatment variable regardless of the values the confounding factors may take on. Even if this assumption is only practically violated in the sense that certain values of the confounding factors cause some treatment levels to become not impossible, but at least highly …

Cluster Mass Inference Method Via Random Field Theory, Hui Zhang, Thomas E. Nichols, Timothy D. Johnson

The University of Michigan Department of Biostatistics Working Paper Series

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this …

Accommodating Covariates In Roc Analysis, Holly Janes, Gary M. Longton, Margaret Pepe

UW Biostatistics Working Paper Series

Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized using the receiver operating characteristic (ROC) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three different ways of using covariate informa- tion. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (ie the ROC curve), we describe methods for mod- elling the ROC curve as a function of covariates. Finally, for factors …

Estimation And Comparison Of Receiver Operating Characteristic Curves, Margaret Pepe, Gary M. Longton, Holly Janes

UW Biostatistics Working Paper Series

The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Non-parametric, semiparametric and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under the ROC curve. Alternatively pointwise comparisons between ROC curves or inverse ROC curves can be made. Options to adjust these analyses for covariates, and to perform ROC regression are described in a companion article. We use a unified framework by representing the ROC curve …

Spatio-Temporal Associations Between Goes Aerosol Optical Depth Retrievals And Ground-Level Pm2.5, Christopher J. Paciorek, Yang Liu, Hortensia Moreno-Macias, Shobha Kondragunta

Harvard University Biostatistics Working Paper Series

We assess the strength of association between aerosol optical depth (AOD) retrievals from the GOES Aerosol/Smoke Product (GASP) and ground-level fine particulate matter (PM2.5) to assess AOD as a proxy for PM2.5 in the United States. GASP AOD is retrieved from a geostationary platform and therefore provides dense temporal coverage with half-hourly observations every day, in contrast to once per day snapshots from polar-orbiting satellites. However, GASP AOD is based on a less-sophisticated instrument and retrieval algorithm. We find that correlations between GASP AOD and PM2.5 over time at fixed locations are reasonably high, except in the winter and in …