Epidemiology Commons^™

Correcting Instrumental Variables Estimators For Systematic Measurement Error, Stijn Vansteelandt, Manoochehr Babanezhad, Els Goetghebeur

Harvard University Biostatistics Working Paper Series

No abstract provided.

Estimating The Prevalence Of Disease Using Relatives Of Case And Control Probands, Kristin N. Javaras, Nan M. Laird, James I. Hudson, Brian D. Ripley

COBRA Preprint Series

We introduce a method for estimating the prevalence of disease using data from a case-control family study performed to investigate the aggregation of disease in families. The families are sampled via case and control probands, and the resulting data consist of information on disease status and covariates for the probands and their relatives. We introduce estimators for overall prevalence and for covariate stratum-specific prevalence (e.g., sex-specific prevalence) that yield approximately unbiased estimates of their population counterparts. We also introduce corresponding confidence intervals that have good coverage properties even for small prevalences. The estimators and intervals address the over-representation of diseased …

Empirical Efficiency Maximization, Daniel B. Rubin, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

It has long been recognized that covariate adjustment can increase precision, even when it is not strictly necessary. The phenomenon is particularly emphasized in clinical trials, whether using continuous, categorical, or censored time-to-event outcomes. Adjustment is often straightforward when a discrete covariate partitions the sample into a handful of strata, but becomes more involved when modern studies collect copious amounts of baseline information on each subject.

The dilemma helped motivate locally efficient estimation for coarsened data structures, as surveyed in the books of van der Laan and Robins (2003) and Tsiatis (2006). Here one fits a relatively small working model …

Is Mri-Based Volume A Mediator Of The Association Of Cumulative Lead Dose With Cognitive Function?, Brian S. Caffo, Sining Chen, Walter Stewart, Karen Bolla, David Yousem, Christos Davatzikos, Brian S. Schwartz

Johns Hopkins University, Dept. of Biostatistics Working Papers

This work considers the pathway through which past occupational lead exposure impacts cognitive function using cross-sectional data. It is motivated by studies linking cumulative lead dose with brain volumes, volumes with cognitive function, and lead dose with cognitive function. It is hypothesized that the brain regions associated with lead mediate a portion of the association between lead dose and cognitive function. The data were derived from an ongoing study of 513 former organolead manufacturing workers. Using MRIs, a novel analysis was performed to investigate Mediation. Volumes associated with cognitive function and lead dose were derived using registered images and used …

Trends In Particulate Matter And Mortality: An Approach To The Assessment Of Unmeasured Confounding, Holly Janes, Francesca Dominici, Scott Zeger

Johns Hopkins University, Dept. of Biostatistics Working Papers

We propose a method for diagnosing confounding bias under a model which links a spatially and temporally varying exposure and health outcome. We decompose the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We show that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates.

We use this approach to estimate the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality …

Power Boosting In Genome-Wide Studies Via Methods For Multivariate Outcomes, Mary J. Emond

UW Biostatistics Working Paper Series

Whole-genome studies are becoming a mainstay of biomedical research. Examples include expression array experiments, comparative genomic hybridization analyses and large case-control studies for detecting polymorphism/disease associations. The tactic of applying a regression model to every locus to obtain test statistics is useful in such studies. However, this approach ignores potential correlation structure in the data that could be used to gain power, particularly when a Bonferroni correction is applied to adjust for multiple testing. In this article, we propose using regression techniques for misspecified multivariate outcomes to increase statistical power over independence-based modeling at each locus. Even when the outcome …

The Causal Effect Of Recent Leisure-Time Physical Activity On All-Cause Mortality Among The Elderly, Oliver Bembom, Mark J. Van Der Laan, Ira B. Tager

U.C. Berkeley Division of Biostatistics Working Paper Series

We analyze data collected as part of a prospective cohort study of elderly people living in and around Sonoma, CA, in order to estimate, for each round of interviews, the causal effect of leisure-time physical activity (LTPA) over the past year on the risk of mortality in the following two years. For each round of interviews, this effect is estimated separately for subpopulations defined based on past exercise habits, age, and whether subjects have had cardiac events in the past. This decomposition of the original longitudinal data structure into a series of point-treatment data structures corresponds to an application of …

A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano

Harvard University Biostatistics Working Paper Series

No abstract provided.

Spatio-Temporal Analysis Of Areal Data And Discovery Of Neighborhood Relationships In Conditionally Autoregressive Models, Subharup Guha, Louise Ryan

Harvard University Biostatistics Working Paper Series

No abstract provided.

Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh

Harvard University Biostatistics Working Paper Series

No abstract provided.

Statistical Analysis Of Air Pollution Panel Studies: An Illustration, Holly Janes, Lianne Sheppard, Kristen Shepherd

UW Biostatistics Working Paper Series

The panel study design is commonly used to evaluate the short-term health effects of air pollution. Standard statistical methods for analyzing longitudinal data are available, but the literature reveals that the techniques are not well understood by practitioners. We illustrate these methods using data from the 1999 to 2002 Seattle panel study. Marginal, conditional, and transitional approaches for modeling longitudinal data are reviewed and contrasted with respect to their parameter interpretation and methods for accounting for correlation and dealing with missing data. We also discuss and illustrate techniques for controlling for time-dependent and time-independent confounding, and for exploring and summarizing …

Biologic Interaction And Their Identification, Tyler J. Vanderweele, James Robins

COBRA Preprint Series

The definitions of a biologic interaction and causal interdependence are reconsidered in light of a sufficient-component cause framework. Various conditions and statistical tests are derived for the presence of biologic interactions. The conditions derived are sufficient but not necessary for the presence of a biologic interaction. Through a series of examples it is made evident that in the context of monotonic effects, but not in general, the conditions which are derived are closely related but not identical to effect modification on the risk difference scale.

A Theory Of Sufficient Cause Interactions, Tyler J. Vanderweele, James M. Robins

COBRA Preprint Series

Sufficient-component causes are discussed within the potential outcome framework so as to formalize notions of sufficient causes, synergism and sufficient cause interactions. Doing so allows for the derivation of counterfactual conditions and statistical tests for detecting the presence of sufficient cause interactions. Under the assumption of monotonic effects, more powerful statistical tests for sufficient cause interactions can be derived. The statistical tests derived for sufficient cause interactions are compared with and contrasted to interaction terms in standard statistical models.

Exploiting Gene-Environment Independence For Analysis Of Case-Control Studies: An Empirical Bayes Approach To Trade Off Between Bias And Efficiency , Bhramar Mukherjee

The University of Michigan Department of Biostatistics Working Paper Series

Standard prospective logistic regression analysis of case-control data often leads to very imprecise estimates of gene-environment interactions due to small numbers of cases or controls in cells of crossing genotype and exposure. In contrast, modern ``retrospective'' methods, including the celebrated ``case-only'' approach, can estimate the interaction parameters much more precisely, but they can be seriously biased when the underlying assumption of gene-environment independence is violated. In this article, we propose a novel approach to analyze case-control data that can relax the gene-environment independence assumption using an empirical Bayes (EB) framework. In the special case, involving a binary gene and a …

Spatial Cluster Detection For Censored Outcome Data, Andrea J. Cook, Diane Gold, Yi Li

Harvard University Biostatistics Working Paper Series

No abstract provided.

Extending Marginal Structural Models Through Local, Penalized, And Additive Learning, Daniel Rubin, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Marginal structural models (MSMs) allow one to form causal inferences from data, by specifying a relationship between a treatment and the marginal distribution of a corresponding counterfactual outcome. Following their introduction in Robins (1997), MSMs have typically been fit after assuming a semiparametric model, and then estimating a finite dimensional parameter. van der Laan and Dudoit (2003) proposed to instead view MSM fitting not as a task of semiparametric parameter estimation, but of nonparametric function approximation. They introduced a class of causal effect estimators based on mapping loss functions suitable for the unavailable counterfactual data to those suitable for the …

Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng

Harvard University Biostatistics Working Paper Series

No abstract provided.

Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin

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.

Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, 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.

Relative Risk Regression In Medical Research: Models, Contrasts, Estimators, And Algorithms, Thomas Lumley, Richard Kronmal, Shuangge Ma

UW Biostatistics Working Paper Series

The relative risk or prevalence ratio is a natural and familiar summary of association between a binary outcome and an exposure or intervention. For rare events, the relative risk can be approximately estimated by logistic regression. For common events estimation is more difficult. We review proposed estimation algorithms for relative risk regression. Some of these give inconsistent estimates or invalid standard errors. We show that the methods that give correct inference can be viewed as arising from a family of quasilikelihood estimating functions for the same generalized linear model, differing in their efficiency and in their robustness to outlying values …

Investigating Mediation When Counterfactuals Are Not Metaphysical: Does Sunlight Uvb Exposure Mediate The Effect Of Eyeglasses On Cataracts?, Brian Egleston, Daniel O. Scharfstein, Beatriz Munoz, Sheila West

Johns Hopkins University, Dept. of Biostatistics Working Papers

We investigate the degree to which a reduction in ocular sunlight ultra-violet B (UVB) exposure mediates a relationship between wearing eyeglasses and a decreased risk of cataracts. An estimand is proposed in which causal effects are estimated locally within strata based on potential UVB exposure without glasses and the degree to which glasses use reduces UVB exposure. We take advantage of the structure of the data in which the counterfactual UVB exposures if the participants in the study who wore glasses had not worn glasses are considered observable.

Causal Comparisons In Randomized Trials Of Two Active Treatments: The Effect Of Supervised Exercise To Promote Smoking Cessation, Jason Roy, Joseph W. Hogan

COBRA Preprint Series

In behavioral medicine trials, such as smoking cessation trials, two or more active treatments are often compared. Noncompliance by some subjects with their assigned treatment poses a challenge to the data analyst. Causal parameters of interest might include those defined by subpopulations based on their potential compliance status under each assignment, using the principal stratification framework (e.g., causal effect of new therapy compared to standard therapy among subjects that would comply with either intervention). Even if subjects in one arm do not have access to the other treatment(s), the causal effect of each treatment typically can only be identified from …

Hierarchical Models For Combining Ecological And Case-Control Data, Sebastien Haneuse, Jon Wakefield

UW Biostatistics Working Paper Series

The ecological study design suffers from a broad range of biases that result from the loss of information regarding the joint distribution of individual-level outcomes, exposures and confounders. The consequent non-identifiability of individual-level models cannot be overcome without additional information; we combine ecological data with a sample of individual-level case-control data. The focus of this paper is hierarchical models to account for between-group heterogeneity. Estimation and inference pose serious compu- tational challenges. We present a Bayesian implementation, based on a data augmentation scheme where the unobserved data are treated as auxiliary variables. The methods are illustrated with a dataset of …

Disease Mapping And Spatial Regression With Count Data, Jon Wakefield

UW Biostatistics Working Paper Series

In this paper we provide critical reviews of methods suggested for the analysis of aggregate count data in the context of disease mapping and spatial regression. We introduce a new method for picking prior distributions, and propose a number of refinements of previously-used models. We also consider ecological bias, mutual standardization, and choice of both spatial model and prior specification. We analyze male lip cancer incidence data collected in Scotland over the period 1975–1980, and outline a number of problems with previous analyses of these data. A number of recommendations are provided. In disease mapping studies, hierarchical models can provide …

Semiparametric Latent Variable Regression Models For Spatio-Temporal Modeling Of Mobile Source Particles In The Greater Boston Area, Alexandros Gryparis, Brent A. Coull, Joel Schwartz, Helen H. Suh

Harvard University Biostatistics Working Paper Series

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic …

Different Public Health Interventions Have Varying Effects, Paula Diehr, Anne B. Newman, Liming Cai, Ann Derleth

UW Biostatistics Working Paper Series

Objective: To compare performance of one-time health interventions to those that change the probability of transitioning from one health state to another. Study Design and Setting: We used multi-state life table methods to estimate the impact of eight types of interventions on several outcomes. Results: In a cohort beginning at age 65, curing all the sick persons at baseline would increase life expectancy by 0.23 years and increase years of healthy life by .54 years. An equal amount of improvement could be obtained with a 12% decrease in the probability of getting sick, a 16% increase in the probability of …

A Hybrid Model For Reducing Ecological Bias, Ruth Salway, Jon Wakefield

UW Biostatistics Working Paper Series

A major drawback of epidemiological ecological studies, in which the association between area-level summaries of risk and exposure are used to make inference about individual risk, is the difficulty in characterising within-area variability in exposure and confounder variables. To avoid ecological bias, samples of individual exposure/confounder data within each area are required. Unfortunately these may be difficult or expensive to obtain, particularly if large samples are required. In this paper we propose a new approach suitable for use with small samples. We combine a Bayesian non-parametric Dirichlet process prior with an estimating functions approach, and show that this model gives …

Health-Exposure Modelling And The Ecological Fallacy, Jon Wakefield, Gavin Shaddick

UW Biostatistics Working Paper Series

Recently there has been increased interest in modelling the association between aggregate disease counts and environmental exposures measured, for example via air pollution monitors, at point locations. This paper has two aims: first we develop a model for such data in order to avoid ecological bias; second we illustrate that modelling the exposure surface and estimating exposures may lead to bias in estimation of health effects. Design issues are also briefly considered, in particular the loss of information in moving from individual to ecological data, and the at-risk populations to consider in relation to the pollution monitor locations. The approach …