Biostatistics Commons^™

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 …

Statistical Issues In Proteomic Research, Jeffrey S. Morris

Jeffrey S. Morris

No abstract provided.

Efficacy Of Cognitive Therapy And Pharmacotherapy In Depression: A Meta-Analysis, Daniel B. Michel

Loma Linda University Electronic Theses, Dissertations & Projects

A meta-analysis comparing the long-term effectiveness of cognitive behavioral therapy (CBT) and pharmacotherapy in preventing relapse following treatment discontinuation was performed using published studies of depressed participants. Twenty-three articles met inclusionary criteria and were included in the analyses. Weighted effect sizes and moderators, treatment type, were calculated using statistical analysis software. Initially, fixed effects models were applied to the data, but due to significant between-group heterogeneity that could not be fully explained by treatment type, mixed effect models were used to account for the residual heterogeneity. Results indicated that overall, depressed individuals treated to remission with CBT evidence decreased relapse …

Exact One-Sided Confidence Limits For The Difference Between Two Correlated Proportions, Chris Lloyd, Max V. Moldovan

Chris J. Lloyd

We construct exact and optimal one-sided upper and lower confidence bounds for the difference between two probabilities based on matched binary pairs using well-established optimality theory of Buehler (1957). Starting with five different approximate loer and upper limits, we adjust them to have coverage probability exactly equal to the desired nominal level and then compare the resulting exact limits by their mean size. Exact limits based on the signed root likelihood ratio statistic are preferred and recommended for practical use.

Longitudinal Data With Follow-Up Truncated By Death: Finding A Match Between Analysis Method And Research Aims, Brenda Kurland, Laura Lee Johnson, Paula Diehr

UW Biostatistics Working Paper Series

Diverse analysis approaches have been proposed to distinguish data missing due to death from nonresponse, and to summarize trajectories of longitudinal data truncated by death. We demonstrate how these analysis approaches arise from factorizations of the distribution of longitudinal data and survival information. Models are illustrated using hypothetical data examples (cognitive functioning in older adults, and quality of life under hospice care) and up to 10 annual assessments of longitudinal cognitive functioning data for 3814 participants in an observational study. For unconditional models, deaths do not occur, deaths are independent of the longitudinal response, or the unconditional longitudinal response averages …

Correction: Using Participatory Design To Develop (Public) Health Decision Support Systems Through Gis, S. Michelle Driedger, Anita Kothari, Jason Morrison, Michael Sawada, Eric J. Crighton, Ian D. Graham

Anita Kothari

Background: Organizations that collect substantial data for decision-making purposes are often characterized as being 'data rich' but 'information poor'. Maps and mapping tools can be very useful for research transfer in converting locally collected data into information. Challenges involved in incorporating GIS applications into the decision-making process within the non-profit (public) health sector include a lack of financial resources for software acquisition and training for nonspecialists to use such tools. This on-going project has two primary phases. This paper critically reflects on Phase 1: the participatory design (PD) process of developing a collaborative web-based GIS tool.

Methods: A case study …

Weight, Mortality, Years Of Healthy Life, And Active Life Expectancy In Older Adults, Paula Diehr

Paula Diehr

OBJECTIVES: To determine whether weight categories predict subsequent mortality and morbidity in older adults. DESIGN: Multistate life tables, using data from the Cardiovascular Health Study, a longitudinal population-based cohort of older adults. SETTING: Data were provided by community-dwelling seniors in four U.S. counties: Forsyth County, North Carolina; Sacramento County, California; Washington County, Maryland; and Allegheny County, Pennsylvania. PARTICIPANTS: Five thousand eight hundred eighty-eight adults aged 65 and older at baseline. MEASUREMENTS: The age- and sex-specific probabilities of transition from one health state to another and from one weight category to another were estimated. From these probabilities, future life expectancy, years …

A Parametric Roc Model Based Approach For Evaluating The Predictiveness Of Continuous Markers In Case-Control Studies, Ying Huang, Margaret Pepe

UW Biostatistics Working Paper Series

The predictiveness curve shows the population distribution of risk endowed by a marker or risk prediction model. It provides a means for assessing the model's capacity for risk stratification. Methods for making inference about the predictiveness curve have been developed using cross-sectional or cohort data. Here we consider inference based on case-control studies and prior knowledge about prevalence or incidence of the outcome. We exploit the relationship between the ROC curve and the predictiveness curve given disease prevalence. Methods are developed for deriving the predictiveness curve from a parametric ROC model. Estimation of the whole range and of a portion …

Estimation Of Dose-Response Functions For Longitudinal Data, Erica E M Moodie, David A. Stephens

COBRA Preprint Series

In a longitudinal study of dose-response, the presence of confounding or non-compliance compromises the estimation of the true effect of a treatment. Standard regression methods cannot remove the bias introduced by patient-selected treatment level, that is, they do not permit the estimation of the causal effect of dose. Using an approach based on the Generalized Propensity Score (GPS), a generalization of the classical, binary treatment propensity score, it is possible to construct a balancing score that provides a more meaningful estimation procedure for the true (unconfounded) effect of dose. Previously, the GPS has been applied only in a single interval …

Loss-Based Estimation With Evolutionary Algorithms And Cross-Validation, David Shilane, Richard H. Liang, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

Many statistical inference methods rely upon selection procedures to estimate a parameter of the joint distribution of explanatory and outcome data, such as the regression function. Within the general framework for loss-based estimation of Dudoit and van der Laan, this project proposes an evolutionary algorithm (EA) as a procedure for risk optimization. We also analyze the size of the parameter space for polynomial regression under an interaction constraints along with constraints on either the polynomial or variable degree.

The Number Of Sick Persons In A Cohort, Paula Diehr

Paula Diehr

To see if the number of sick persons in a cohort was approximately constant over time, we calculated the number of sick persons in a “research” cohort of older adults followed for up to 14 years, and also in a synthetic birth cohort. Methods: In the research cohort, we calculated the actual number of persons in each health state over time, using eight different definitions of “sick”. For the birth cohort, we estimated the number of sick persons each year after birth. Results: The number of sick persons in the research cohort was approximately constant for 14 years, for all …

Statistical Methods For Analyzing Sequentially Randomized Trials, Oliver Bembom, Mark J. Van Der Laan

Oliver Bembom

In this issue of JNCI, Thall et al. present the results of a clinical trial that makes use of sequential randomization, a novel trial design that allows the investigator to study adaptive treatment strategies. Our aim is to complement this groundbreaking work by reviewing the current state of the art of statistical methods available for such analyses. Using the data collected by Thall et al. as an example, we focus on two different approaches for estimating the success rates of different adaptive treatment strategies of interest. By emphasizing the intuitive appeal and straightforward implementation of these methods and illustrating the …

Identifiability And Estimation Of Causal Effects In Randomized Trials With Noncompliance And Completely Non-Ignorable Missing-Data, Hua Chen, Zhi Geng, Xiao-Hua Zhou

UW Biostatistics Working Paper Series

In this paper we first studied parameter identifiability in randomized clinical trials with noncompliance and missing outcomes. We showed that under certain conditions the parameters of interest were identifiable even under different types of completely non-ignorable missing data, that is, the missing mechanism depends on the outcome.We then derived their maximum likelihood (ML) and moment estimators and evaluated their finite-sample properties in simulation studies in terms of bias, efficiency and robustness. Our sensitive analysis showed the assumed non-ignorable missing- data model had an important impact on the estimated complier average causal effect (CACE) parameter. Our new method provides some new …

Inference On Overlapping Coefficients In Two Exponential Populations, Mohammad F. Al-Saleh, Hani M. Samawi

Biostatistics Faculty Publications

Three measures of overlap, namely Matusita’s measureρ , Morisita’s measure λ and Weitzman’s measure Δ are investigated in this article for two exponential populations with different means. It is well that the estimators of those measures of overlap are biased. The bias is of these estimators depends on the unknown overlap parameters. There are no closed-form, exact formulas, for those estimators variances or their exact sampling distributions. Monte Carlo evaluations are used to study the bias and precision of the proposed overlap measures. Bootstrap method and Taylor series approximation are used to construct confidence intervals for the overlap measures.

Nonparametric And Semiparametric Group Sequential Methods For Comparing Accuracy Of Diagnostic Tests, Liansheng Tang, Scott S. Emerson, Xiao-Hua Zhou

UW Biostatistics Working Paper Series

Comparison of the accuracy of two diagnostic tests using the receiver operating characteristic (ROC) curves from two diagnostic tests has been typically conducted using fixed sample designs. On the other hand, the human experimentation inherent in a comparison of diagnostic modalities argues for periodic monitoring of the accruing data to address many issues related to the ethics and efficiency of the medical study. To date, very little research has been done in the use of sequential sampling plans for comparative ROC studies, even when these studies may use expensive and unsafe diagnostic procedures. In this paper, we propose a nonparametric …

A Bayesian Image Analysis Of The Change In Tumor/Brain Contrast Uptake Induced By Radiation Via Reversible Jump Markov Chain Monte Carlo, Xiaoxi Zhang, Tim Johnson, Roderick J.A. Little

The University of Michigan Department of Biostatistics Working Paper Series

This work is motivated by a pilot study on the change in tumor/brain contrast uptake induced by radiation via quantitative Magnetic Resonance Imaging. The results inform the optimal timing of administering chemotherapy in the context of radiotherapy. A noticeable feature of the data is spatial heterogeneity. The tumor is physiologically and pathologically distinct from surrounding healthy tissue. Also, the tumor itself is usually highly heterogeneous. We employ a Gaussian Hidden Markov Random Field model that respects the above features. The model introduces a latent layer of discrete labels from an Markov Random Field (MRF) governed by a spatial regularization parameter. …

A Smoothing Approach To Data Masking, Yijie Zhous, Francesca Dominici, Thomas A. Louis

Johns Hopkins University, Dept. of Biostatistics Working Papers

Individual-level data are often not publicly available due to confidentiality. Instead, masked data are released for public use. However, analyses performed using masked data may produce invalid statistical results such as biased parameter estimates or incorrect standard errors. In this paper, we propose a data masking method using spatial smoothing, and we investigate the bias of parameter estimates resulting from analyses using the masked data for Generalized Linear Models (GLM). The method allows for varying both the form and the degree of masking by utilizing a smoothing weight function and a smoothness parameter. We show that data masking by using …

Age-Specific Prevalence And Years Of Healthy Life In A System With 3 Health States, Paula Diehr

Paula Diehr

Consider a 3-state system with one absorbing state, such as Healthy, Sick, and Dead. Over time, the prevalence of the Healthy state will approach an 'equilibrium' value that is independent of the initial conditions. We derived this equilibrium prevalence (Prev:Equil) as a function of the local transition probabilities. We then used Prev:Equil to estimate the expected number of years spent in the healthy state over time. This estimate is similar to the one calculated by multi-state life table methods, and has the advantage of having an associated standard error. In longitudinal data for older adults, the standard error was accurate …

Roc Surfaces In The Presence Of Verification Bias, Yueh-Yun Chi, Xiao-Hua (Andrew) Zhou

UW Biostatistics Working Paper Series

In diagnostic medicine, the Receiver Operating Characteristic (ROC) surface is one of the established tools for assessing the accuracy of a diagnostic test in discriminating three disease states, and the volume under the ROC surface has served as a summary index for diagnostic accuracy. In practice, the selection for definitive disease examination may be based on initial test measurements, and induces verification bias in the assessment. We propose here a nonparametric likelihood-based approach to construct the empirical ROC surface in the presence of differential verification, and to estimate the volume under the ROC surface. Estimators of the standard deviation are …

Functional Principal Component Regression And Functional Partial Least Squares, Philip T. Reiss, R. Todd Ogden

Philip T. Reiss

Regression of a scalar response on signal predictors, such as near-infrared (NIR) spectra of chemical samples, presents a major challenge when, as is typically the case, the dimension of the signals far exceeds their number. Most solutions to this problem reduce the dimension of the predictors either by regressing on components--e.g. principal component regression (PCR) and partial least squares (PLS)--or by smoothing methods which restrict the coefficient function to the span of a spline basis. This paper introduces functional versions of PCR and PLS, which combine both of the above dimension reduction approaches. Two versions of functional PCR are developed, …

Comparing Trends In Cancer Rates Across Overlapping Regions, Yi Li, Ram C. Tiwari

Harvard University Biostatistics Working Paper Series

No abstract provided.

Effective Communication Of Standard Errors And Confidence Intervals, Thomas A. Louis, Scott L. Zeger

Johns Hopkins University, Dept. of Biostatistics Working Papers

We recommend a format for communicating an estimate with its standard error or confidence interval. The format reinforces that the associated variability is an inseparable component of the estimate and it substantially improves clarity in tabular displays.

Inference For Survival Curves With Informatively Coarsened Discrete Event-Time Data: Application To Alive, Michelle Shardell, Daniel O. Scharfstein, David Vlahov, Noya Galai

Johns Hopkins University, Dept. of Biostatistics Working Papers

In many prospective studies, including AIDS Link to the Intravenous Experience (ALIVE), researchers are interested in comparing event-time distributions (e.g.,for human immunodeficiency virus seroconversion) between a small number of groups (e.g., risk behavior categories). However, these comparisons are complicated by participants missing visits or attending visits off schedule and seroconverting during this absence. Such data are interval-censored, or more generally,coarsened. Most analysis procedures rely on the assumption of non-informative censoring, a special case of coarsening at random that may produce biased results if not valid. Our goal is to perform inference for estimated survival functions across a small number of …

Effectively Combining Independent 2 X 2 Tables For Valid Inferences In Meta Analysis With All Available Data But No Artificial Continuity Corrections For Studies With Zero Events And Its Application To The Analysis Of Rosiglitazone's Cardiovascular Disease Related Event Data, Lu Tian, Tianxi Cai, Nikita Piankov, Pierre-Yves Cremieux, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Biomarker Discovery Using Targeted Maximum Likelihood Estimation: Application To The Treatment Of Antiretroviral Resistant Hiv Infection, Oliver Bembom, Maya L. Petersen , Soo-Yon Rhee , W. Jeffrey Fessel , Sandra E. Sinisi, Robert W. Shafer, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Researchers in clinical science and bioinformatics frequently aim to learn which of a set of candidate biomarkers is important in determining a given outcome, and to rank the contributions of the candidates accordingly. This article introduces a new approach to research questions of this type, based on targeted maximum likelihood estimation of variable importance measures.

The methodology is illustrated using an example drawn from the treatment of HIV infection. Specifically, given a list of candidate mutations in the protease enzyme of HIV, we aim to discover mutations that reduce clinical virologic response to antiretroviral regimens containing the protease inhibitor lopinavir. …

Biomarker Discovery Using Targeted Maximum Likelihood Estimation: Application To The Treatment Of Antiretroviral Resistant Hiv Infection, Oliver Bembom, Maya L. Petersen, Soo-Yon Rhee, W. Jeffrey Fessel, Sandra E. Sinisi, Robert W. Shafer, Mark J. Van Der Laan

Oliver Bembom

Researchers in clinical science and bioinformatics frequently aim to learn which of a set of candidate biomarkers is important in determining a given outcome, and to rank the contributions of the candidates accordingly. This article introduces a new approach to research questions of this type, based on targeted maximum likelihood estimation of variable importance measures. The methodology is illustrated using an example drawn from the treatment of HIV infection. Specifically, given a list of candidate mutations in the protease enzyme of HIV, we aim to discover mutations that reduce clinical virologic response to antiretroviral regimens containing the protease inhibitor lopinavir. …

An Example Of How To Write The Statistical Section Of A Bioequivalence Study Protocol For Fda Review, William F. Mccarthy

COBRA Preprint Series

This paper provides a detailed example of how one should write the statistical section of a bioequivalence study protocol for FDA review. Three forms of bioequivalence are covered: average bioequivalence (ABE), population bioequivalence (PBE) and individual bioequivalence (IBE). The method of analysis is based on Jones and Kenward (2003) and a modification of their SAS Macro is provided.

Variable Selection For Nonparametric Varying-Coefficient Models For Analysis Of Repeated Measurements, Lifeng Wang, Hongzhe Li

UPenn Biostatistics Working Papers

Nonparametric varying-coefficient models are commonly used for analysis of data measured repeatedly over time, including longitudinal and functional responses data. While many procedures have been developed for estimating the varying-coefficients, the problem of variable selection for such models has not been addressed. In this article, we present a regularized estimation procedure for variable selection for such nonparametric varying-coefficient models using basis function approximations and a group smoothly clipped absolute deviation penalty (gSCAD). This gSCAD procedure simultaneously selects significant variables with time-varying effects and estimates unknown smooth functions using basis function approximations. With appropriate selection of the tuning parameters, we have …

Adjustment To The Mcnemar’S Test For The Analysis Of Clustered Matched-Pair Data, William F. Mccarthy

COBRA Preprint Series

This paper presents how one can adjust the McNemar’s test for the analysis of clustered matched-pair data. A McNemar’s-like table for K clusters of matched-pair data is used.

Assessment Of Sample Size And Power For The Analysis Of Clustered Matched-Pair Data, William F. Mccarthy

COBRA Preprint Series

This paper outlines how one can determined the sample size or power of a study design that is based on clustered matched-pair data. Detailed examples are provided.