Medicine and Health Sciences 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 …

Methodological Issues In The Study Of The Effects Of Hemoglobin Variability, Marshall Joffe, Wei Yang, Steve Brunelli, Harold Feldman

UPenn Biostatistics Working Papers

We consider estimating the effect of hemoglobin variability on mortality in hemodialysis patients. Causal effects can be defined as comparisons of outcomes under different hypothetical interventions. Defining measures of the effect of hemoglobin variability and clinical outcomes is complicated by the fact that hypothetical interventions on variability used to define its effect inevitably involve manipulation of related variables. We propose a model-based definition of the effect of the hemoglobin variability as a parameter for variability in a causal model for the effect of an overall intervention on hemoglobin levels over time. We consider this problem using history-adjusted marginal structural models, …

Adjusting For Covariates In Studies Of Diagnostic, Screening, Or Prognostic Markers: An Old Concept In A New Setting, Holly Janes, Margaret Pepe

UW Biostatistics Working Paper Series

The concept of covariate adjustment is well established in therapeutic and etiologic studies. However, it has received little attention in the growing area of medical research devoted to the development of markers for disease diagnosis, screening, or prognosis, where classification accuracy, rather than association, is of primary interest. In this paper, we demonstrate the need for covariate adjustment in studies of classification accuracy, discuss methods for adjusting for covariates, and distinguish covariate adjustment from several other related but fundamentally different uses for covariates. We draw analogies and contrasts throughout with studies of association.

Weighted Likelihood Method For Grouped Survival Data In Case-Cohort Studies With Application To Hiv Vaccine Trials, Zhiguo Li, Peter B. Gilbert, Bin Nan

The University of Michigan Department of Biostatistics Working Paper Series

Grouped failure time data arise often in HIV studies. In a recent preventive HIV vaccine efficacy trial, immune responses generated by the vaccine were measured from a case-cohort sample of vaccine recipients, who were subsequently evaluated for the study endpoint of HIV infection at pre-specified follow-up visits. Gilbert et al. (2005) and Forthal et al. (2007) analyzed the association between the immune responses and HIV incidence with a Cox proportional hazards model, treating the HIV infection diagnosis time as a right censored random variable. The data, however, are of the form of grouped failure time data with case-cohort covariate sampling, …

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 …

Assessing The Unreliability Of The Medical Literature: A Response To "Why Most Published Research Findings Are False", Steven Goodman, Sander Greenland

Johns Hopkins University, Dept. of Biostatistics Working Papers

A recent article in this journal (Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2: e124) argued that more than half of published research findings in the medical literature are false. In this commentary, we examine the structure of that argument, and show that it has three basic components:

1)An assumption that the prior probability of most hypotheses explored in medical research is below 50%.

2)Dichotomization of P-values at the 0.05 level and introduction of a “bias” factor (produced by significance-seeking), the combination of which severely weakens the evidence provided by every design.

3)Use of Bayes …

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 …

Estimating The Empirical Lorenz Curve And Gini Coefficient In The Presence Of Error, Chaya S. Moskowitz, E. S. Venkatraman, Elyn Riedel, Colin B. Begg

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

The Lorenz curve is a graphical tool that is widely used to characterize the concentration of a measure in a population, such as wealth. It is frequently the case that the measure of interest used to rank experimental units when estimating the empirical Lorenz curve, and the corresponding Gini coefficient, is subject to random error. This error can result in an incorrect ranking of experimental units which inevitably leads to a curve that exaggerates the degree of concentration (variation) in the population. We explore this bias and discuss several widely available statistical methods that have the potential to reduce or …