Estimation of Attributable Risk from Clustered Binary Data: The Case of Cross-Sectional and Cohort Studies

Mohamed Shoukri,Futwan Al-Mohanna,Allan Donner

doi:10.4236/ojs.2017.72019

Abstract

Effect sizes are estimated from several study designs when the subjects are individually sampled. When the samples are the aggregate cluster of individuals, the within cluster correlation must be accounted for to construct correct confidence intervals, and to conduct valid statistical inference. The purpose of this article is to propose and evaluate statistical procedures for the estimation of the variance of the estimated attributable risk in parallel groups of clusters, and in a design dividing each of k clusters into two segments creating multiple sub-clusters. The estimated variance is the first order approximation and is obtained by the delta method. We apply the methodology and propose a Wald type confidence interval on the difference between two correlated attributable risks. We also construct a test on the hypothesis of equality of two correlated attributable risks. We evaluate the power of the proposed test via Monte-Carlo simulations.

Highlights

In the epidemiological research, it is important that the collected data are translated into interpretable results which can be communicated to clinicians
When exposure and disease risk are measured on a binary scale, several measures of effect size are in current use [1]
The attributable risk (AR) estimate is interpreted as follow: if among infants born with congenital heart defects (CHD), given that Patent Ductus Arteriosus (PDA) among infants with CHD is a preventable event, prohibiting first degree relatives’ marriages will reduce the chance of having PDA by 6%

Summary

Introduction

It is important that the collected data are translated into interpretable results which can be communicated to clinicians. When evaluating these studies, the examination of “Effect Size” or (ES) can be a useful measure of the comparative efficacy of the treatment under investigation. When exposure and disease risk are measured on a binary scale, several measures of effect size are in current use [1]. The odds ratio (OR), the relative risk (RR), and the population attributable risk (AR) are the most commonly used measures of effect size in clinical as well as analytic epidemiology

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