Biostatistics Commons^™

Analysis Of Adverse Events In Drug Safety: A Multivariate Approach Using Stratified Quasi-Least Squares, Hanjoo Kim, Justine Shults, Scott Patterson, Robert Goldberg-Alberts

UPenn Biostatistics Working Papers

Safety assessment in drug development involves numerous statistical challenges, and yet statistical methodologies and their applications to safety data have not been fully developed, despite a recent increase of interest in this area. In practice, a conventional univariate approach for analysis of safety data involves application of the Fisher's exact test to compare the proportion of subjects who experience adverse events (AEs) between treatment groups; This approach ignores several common features of safety data, including the presence of multiple endpoints, longitudinal follow-up, and a possible relationship between the AEs within body systems. In this article, we propose various regression modeling …

Synthesis Analysis Of Regression Models With A Continuous Outcome, Andrew Zhou, Nan Hu, Guizhou Hu, Martin Root

UW Biostatistics Working Paper Series

Synthesis Analysis of Regression Models with a Continuous Outcome Xiao-Hua Zhou 1,2, Nan Hu 2, Guizhou Hu3, and Martin Root3 1 HSR&D Center of Excellence, VA Puget Sound Health Care System, Seattle, WA 98101. 2 Department of Biostatistics, University of Washington, Seattle, WA 98195. 3 BioSignia, Inc., 1822 East NC Highway 54, Suite 350, Durham, NC 27713 To estimate the multivariate regression model from multiple individual studies, it would be challenging to obtain results if the input from individual studies only provide univariate or incomplete multivariate regression information. Samsa et al [1] proposed a simple method to combine coefficients from …

A Small Sample Correction For Estimating Attributable Risk In Case-Control Studies, Daniel B. Rubin

U.C. Berkeley Division of Biostatistics Working Paper Series

The attributable risk, often called the population attributable risk, is in many epidemiological contexts a more relevant measure of exposure-disease association than the excess risk, relative risk, or odds ratio. When estimating attributable risk with case-control data and a rare disease, we present a simple correction to the standard approach making it essentially unbiased, and also less noisy. As with analogous corrections given in Jewell (1986) for other measures of association, the adjustment often won't make a substantial difference unless the sample size is very small or point estimates are desired within fine strata, but we discuss the possible utility …

Tolerance Intervals In Random-Effects Models, Kakotan Sanogo

Theses and Dissertations

In the pharmaceutical setting, it is often necessary to establish the shelf life of a drug product and sometimes suitable to assess the risk of product failure at the desired expiry period. The current statistical methodology use confidence intervals for the predicted mean to establish the expiry period and prediction intervals for a predicted new assay value or a tolerance interval for a proportion of the population for use in a risk assessment. A major concern is that most methodology treat a homogeneous subpopulation, say batch, either as a fixed effect and therefore uses a fixed-effects regression model (Graybill, 1976) …

Optimal Cutpoint Estimation With Censored Data, Mithat Gonen, Camelia Sima

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

We consider the problem of selecting an optimal cutpoint for a continuous marker when the outcome of interest is subject to right censoring. Maximal chi square methods and receiver operating characteristic (ROC) curves-based methods are commonly-used when the outcome is binary. In this article we show that selecting the cutpoint that maximizes the concordance, a metric similar to the area under an ROC curve, is equivalent to maximizing the Youden index, a popular criterion when the ROC curve is used to choose a threshold. We use this as a basis for proposing maximal concordance as a metric to use with …

A New Class Of Rank Tests For Interval-Censored Data, Guadalupe Gomez, Ramon Oller Pique

Harvard University Biostatistics Working Paper Series

No abstract provided.

The Highest Confidence Density Region And Its Usage For Inferences About The Survival Function With Censored Data, Lu Tian, Rui Wang, Tianxi Cai, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Focus On Rna Isolation: Obtaining Rna For Microrna (Mirna) Expression Profiling Analyses Of Neural Tissue, Wang-Xia Wang, Bernard R. Wilfred, Donald A. Baldwin, R. Benjamin Isett, Na Ren, Arnold J. Stromberg, Peter T. Nelson

Sanders-Brown Center on Aging Faculty Publications

MicroRNAs (miRNAs) are present in all known plant and animal tissues and appear to be somewhat concentrated in the mammalian nervous system. Many different miRNA expression profiling platforms have been described. However, relatively little research has been published to establish the importance of 'upstream' variables in RNA isolation for neural miRNA expression profiling. We tested whether apparent changes in miRNA expression profiles may be associated with tissue processing, RNA isolation techniques, or different cell types in the sample. RNA isolation was performed on a single brain sample using eight different RNA isolation methods, and results were correlated using a conventional …

Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Multilevel Latent Class Models With Dirichlet Mixing Distribution, Chongzhi Di, Karen Bandeen-Roche

Johns Hopkins University, Dept. of Biostatistics Working Papers

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when …

Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Applications Of The Bivariate Gamma Distribution In Nutritional Epidemiology And Medical Physics, Jolene Barker

Theses and Dissertations

In this thesis the utility of a bivariate gamma distribution is explored. In the field of nutritional epidemiology a nutrition density transformation is used to reduce collinearity. This phenomenon will be shown to result due to the independent variables following a bivariate gamma model. In the field of radiation oncology paired comparison of variances is often performed. The bivariate gamma model is also appropriate for fitting correlated variances. A method for simulating bivariate gamma random variables is presented. This method is used to generate data from several bivariate gamma models and the asymptotic properties of a test statistic, suggested for …

Variable Selection In Competing Risks Using The L1-Penalized Cox Model, Xiangrong Kong

Theses and Dissertations

One situation in survival analysis is that the failure of an individual can happen because of one of multiple distinct causes. Survival data generated in this scenario are commonly referred to as competing risks data. One of the major tasks, when examining survival data, is to assess the dependence of survival time on explanatory variables. In competing risks, as with ordinary univariate survival data, there may be explanatory variables associated with the risks raised from the different causes being studied. The same variable might have different degrees of influence on the risks due to different causes. Given a set of …

A Note On Risk Prediction For Case-Control Studies, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We introduce a new method for prediction in case-control study designs, which is a simple extension of the work by van der Laan (2008). Case-control samples are biased since the proportion of cases in the sample is not the same as the population of interest. The case-control weighting for prediction proposed in this paper relies on knowledge of the true incidence probability P(Y=1) to eliminate the bias of the sampling design. In many practical settings, case-control weighting will outperform an existing method for prediction, intercept adjustment.

Confidence Intervals For Negative Binomial Random Variables Of High Dispersion, David Shilane, Alan E. Hubbard, S N. Evans

U.C. Berkeley Division of Biostatistics Working Paper Series

This paper considers the problem of constructing confidence intervals for the mean of a Negative Binomial random variable based upon sampled data. When the sample size is large, we traditionally rely upon a Normal distribution approximation to construct these intervals. However, we demonstrate that the sample mean of highly dispersed Negative Binomials exhibits a slow convergence to the Normal in distribution as a function of the sample size. As a result, standard techniques (such as the Normal approximation and bootstrap) that construct confidence intervals for the mean will typically be too narrow and significantly undercover in the case of high …

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 …

Perbandingan Analisis Regresi Logistik Dengan Analisis Propensity Score Matching Pada Studi Kasus Imunisasi Bayi, Waras Budi Utomo

Kesmas

Analisis multivariat konvensioanal tidak selalu merupakan metode ideal untuk memprediksi efek pajanan pada studi-studi observasional. Ketika distribusi kovariat antara kelompok pajanan berbeda besar, penyesuaan dengan teknik multivariat konvensioanl tidak cukup menyeimbangkan kelompok tersebut. Bias yang tersisa dapat menghambat penarikan kesimpulan yang valid. Tujuan penelitian ini adalah membandingkan hasil analisis multivariat konvensional dengan analisis metoda propensity score matching pada studi kasus data sekunder imunisasi bayi ASUH KAP2 2003. Penelitian ini menemukan nilai OR metoda regresi logistik (0,99) berbeda dengan metoda propensity score matching (0,96). Metoda propensity score matching berhasil menjodohkan 574 subjek (68,27%). Untuk evaluasi pengaruh faktor risiko disarankan menggunakan model …

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 …

Knowledge, Perceptions, Beliefs And Behaviors Related To The Prevention Of Hypertension Among Black Seventh-Day Adventists Living In London, Maxine A. Newell

Loma Linda University Electronic Theses, Dissertations & Projects

This study was a cross-sectional survey of the hypertension (HTN) knowledge and risk behaviors of Black Seventh-day Adventists (SDA) in London. Recruitment took take place in 17 predominantly Black SDA churches in London. A questionnaire assessed knowledge and lay-beliefs about HTN and perceptions towards HTN using the health belief model (HBM) constructs of susceptibility, severity, benefits, barriers, and self-efficacy. Cohen’s Perceived Stress Scale was incorporated into the questionnaire. Blood pressure, height, weight and waist circumference were and current lifestyles practices were evaluated for the presence of HTN risk factors.

Of the 312 volunteers, ages 25 to 79, 55% were born …