Dataset Information

Point source modeling of matched case-control data with multiple disease subtypes.

ABSTRACT: In this paper, we propose nonlinear distance-odds models investigating elevated odds around point sources of exposure, under a matched case-control design where there are subtypes within cases. We consider models analogous to the polychotomous logit models and adjacent-category logit models for categorical outcomes and extend them to the nonlinear distance-odds context. We consider multiple point sources as well as covariate adjustments. We evaluate maximum likelihood, profile likelihood, iteratively reweighted least squares, and a hierarchical Bayesian approach using Markov chain Monte Carlo techniques under these distance-odds models. We compare these methods using an extensive simulation study and show that with multiple parameters and a nonlinear model, Bayesian methods have advantages in terms of estimation stability, precision, and interpretation. We illustrate the methods by analyzing Medicaid claims data corresponding to the pediatric asthma population in Detroit, Michigan, from 2004 to 2006.

SUBMITTER: Li S

PROVIDER: S-EPMC4331356 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Point source modeling of matched case-control data with multiple disease subtypes.

Li Shi S Mukherjee Bhramar B Batterman Stuart S

Statistics in medicine 20120724 28

In this paper, we propose nonlinear distance-odds models investigating elevated odds around point sources of exposure, under a matched case-control design where there are subtypes within cases. We consider models analogous to the polychotomous logit models and adjacent-category logit models for categorical outcomes and extend them to the nonlinear distance-odds context. We consider multiple point sources as well as covariate adjustments. We evaluate maximum likelihood, profile likelihood, iterat ...[more]

PMID: 22826092

Similar Datasets

Project description:BackgroundThere is no study conducted on the association between disinfection byproducts (DBPs) in chlorinated drinking water and colorectal cancer (CRC) in Ethiopia. Therefore, this study aimed to determine the relation between chlorine based DBPs in drinking water and CRC in Addis Ababa, Ethiopia.MethodsA facility based matched case control study was conducted involving 224 cases and 448 population controls from June 2020 to May 2021. Cases were defined as histologically confirmed CRC cases. Cases were matched with controls by residence, age, and sex using frequency and individual matching. Geocoding of cases, health facility, and georeferencing of controls were carried out. Data was collected using a pretested structured questionnaire. Pearson Chi square and Fisher's exact tests were employed to assess associations. Stratified analysis was used to detect confounding factors and effect modification. A multivariable conditional logistic regression was used to identify risk factors of CRC.ResultsOf 214 CRC cases, 148 (69.2%) used chlorinated water whereas out of 428 controls 161 (37.6%) used chlorinated water. In the final regression model, drinking chlorinated surface water (adjusted matched odds ratio [adjusted mOR] = 2.6; 95% CI 1.7-4.0), history of swimming (adjusted mOR = 2.4; 95% CI 1.4-4.1), years at the place of current residence (adjusted mOR = 1.5; 95% CI 1.1-2.2), hot tap water use for showering (adjusted mOR; 3.8 = 95% CI 2.5-5.9) were significantly associated with CRC. The stratified analysis confirmed that smoking and meat ingestion were not effect modifiers and confounders.ConclusionDrinking chlorinated water for extended years is a significant risk factor for CRC in Addis Ababa, Ethiopia. In addition, hot tap water use for showering, and swimming history are risk factors for CRC. This information is essential to design integrated interventions that consider chlorination by-products and exposure routes toward the prevention and control of CRC in Ethiopia. Initiating alternative methods to chlorine disinfection of drinking water is also essential.

Dataset Information

Point source modeling of matched case-control data with multiple disease subtypes.

Publications

Point source modeling of matched case-control data with multiple disease subtypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets