Synthesis of Evidence from Epidemiological Studies with Interval-Censored Exposure Due to Grouping

Abstract

We describe a method for assessing dose-response effects from a series of case-control and cohort studies in which the exposure information is interval censored. The interval censoring of the exposure variable is dealt with through the use of retrospective models in which the exposure is treated as a multinomial response and disease status as a binary covariate. Polychotomous logistic regression models are adopted in which the dose-response relationship between exposure and disease may be modeled in a discrete or continuous fashion. Partial conditioning is possible to eliminate some of the nuisance parameters. The methods are applied to the motivating study of the relationship between chorionic villus sampling and the occurrence of terminal transverse limb reduction.

Empirical evaluation of meta‐analytic approaches for nutrient and health outcome dose‐response data

“…Cook et al . () and DuMouchel () opt to model the exposure levels with interval‐truncated distributions. However, it is likely that alternative approaches to calculating the mean exposure in each category of intake would impact all five methods in a similar way.…”

“…Hartemink et al (2006) propose fitting parametric distributions to match the exposure categories per study, at which point, the mean exposure per category can be obtained with integration. Cook et al (2001) andDuMouchel (1995) opt to model the exposure levels with interval-truncated distributions. However, it is likely that alternative approaches to calculating the mean exposure in each category of intake would impact all five methods in a similar way.…”

“…Another group of methods combines all data in a single estimation step. For example, Berlin et al (1993) approximate the within study covariance of the correlated effect sizes with multivariate normal distributions and combine all data in a weighted linear regression, whereas Cook et al (2001) use a discrete (multinomial) likelihood.…”

Choice of exposure scores for categorical regression in meta-analysis: a case study of a common problem

“…categorization of the exposure [27]. However, this method does not address data with open-ended categories and therefore, is not applicable to metaanalysis of typical nutritional exposures.…”