Disease Modeling Commons^™

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 …

Model Checking For Roc Regression Analysis, Tianxi Cai, Yingye Zheng

Harvard University Biostatistics Working Paper Series

The Receiver Operating Characteristic (ROC) curve is a prominent tool for characterizing the accuracy of continuous diagnostic test. To account for factors that might invluence the test accuracy, various ROC regression methods have been proposed. However, as in any regression analysis, when the assumed models do not fit the data well, these methods may render invalid and misleading results. To date practical model checking techniques suitable for validating existing ROC regression models are not yet available. In this paper, we develop cumulative residual based procedures to graphically and numerically assess the goodness-of-fit for some commonly used ROC regression models, and …

Is The Number Of Sick Persons In A Cohort Constant Over Time?, Paula Diehr, Ann Derleth, Anne Newman, Liming Cai

UW Biostatistics Working Paper Series

Objectives: To estimate the number of persons in a cohort who are sick, over time.

Methods: We calculated the number of sick persons in the Cardiovascular Health Study (CHS), a cohort study of older adults followed up to 14 years, using eight definitions of “healthy” and “sick”. We projected the number in each health state over time for a birth cohort.

Results: The number of sick persons in CHS was approximately constant for 14 years, for all definitions of “sick”. The estimated number of sick persons in the birth cohort was approximately constant from ages 55-75, after which it decreased. …

Prognosis Of Stage Ii Colon Cancer By Non-Neoplastic Mucosa Gene Expresssion Profiling, Alain Barrier, Sandrine Dudoit, Et Al.

U.C. Berkeley Division of Biostatistics Working Paper Series

Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa microarray gene expression measures, in stage II colon cancer patients. Materials and Methods. Non-neoplastic colonic mucosa mRNA samples from 24 patients (10 with a metachronous metastasis, 14 with no recurrence) were profiled using the Affymetrix HGU133A GeneChip. The k-nearest neighbor method was used for prognosis prediction using microarray gene expression measures. Leave-one-out cross-validation was used to select the number of neighbors and number of informative genes to include in the predictor. Based on this information, a prognosis predictor was proposed and its accuracy estimated by …

Colon Cancer Prognosis Prediction By Gene Expression Profiling, Alain Barrier, Sandrine Dudoit, Et Al.

U.C. Berkeley Division of Biostatistics Working Paper Series

Aims. This study assessed the possibility to build a prognosis predictor, based on microarray gene expression measures, in stage II and III colon cancer patients. Materials and Methods. Tumour (T) and non-neoplastic mucosa (NM) mRNA samples from 18 patients (9 with a recurrence, 9 with no recurrence) were profiled using the Affymetrix HGU133A GeneChip. The k-nearest neighbour method was used for prognosis prediction using T and NM gene expression measures. Six-fold cross-validation was applied to select the number of neighbours and the number of informative genes to include in the predictors. Based on this information, one T-based and one NM-based …

The Sensitivity And Specificity Of Markers For Event Times, Tianxi Cai, Margaret S. Pepe, Thomas Lumley, Yingye Zheng, Nancy Swords Jenny

Harvard University Biostatistics Working Paper Series

No abstract provided.

Estimation Of Subject Specific Icp Dynamic Models Using Prospective Clinical Data, Wayne W. Wakeland, Joe Fusion, B. Goldstein

Complex Systems Faculty Publications and Presentations

We used a prospective clinical trial to generate physiologic data to create subject-specific in silico (computer simulation) models of intracranial pressure dynamics in children with severe traumatic brain injury. The trial included a physiologic challenge protocol with changes in head-of-bed elevation and minute ventilation, applied over multiple iterations to three subjects. Physiologic signals (electrocardiogram, respiration, arterial blood pressure, intracranial pressure [ICP], oxygen saturation) were recorded continuously, along with clinical annotations indicating the precise timing of physiologic challenges. Several parameters within the model of ICP dynamics were estimated for each subject based on the ICP response to the challenges. Estimation was …