Dataset Information

A method making fewer assumptions gave the most reliable estimates of exposure-outcome associations in stratified case-cohort studies.

ABSTRACT:

Objective

A case-cohort study is an efficient epidemiological study design for estimating exposure-outcome associations. When sampling of the subcohort is stratified, several methods of analysis are possible, but it is unclear how they compare. Our objective was to compare five analysis methods using Cox regression for this type of data, ranging from a crude model that ignores the stratification to a flexible one that allows nonproportional hazards and varying covariate effects across the strata.

Study design and setting

We applied the five methods to estimate the association between physical activity and incident type 2 diabetes using data from a stratified case-cohort study and also used artificial data sets to exemplify circumstances in which they can give different results.

Results

In the diabetes study, all methods except the method that ignores the stratification gave similar results for the hazard ratio associated with physical activity. In the artificial data sets, the more flexible methods were shown to be necessary when certain assumptions of the simpler models failed. The most flexible method gave reliable results for all the artificial data sets.

Conclusion

The most flexible method is computationally straightforward, and appropriate whether or not key assumptions made by the simpler models are valid.

SUBMITTER: Jones E

PROVIDER: S-EPMC4669309 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A method making fewer assumptions gave the most reliable estimates of exposure-outcome associations in stratified case-cohort studies.

Jones Edmund E Sweeting Michael J MJ Sharp Stephen J SJ Thompson Simon G SG

Journal of clinical epidemiology 20150430 12

<h4>Objective</h4>A case-cohort study is an efficient epidemiological study design for estimating exposure-outcome associations. When sampling of the subcohort is stratified, several methods of analysis are possible, but it is unclear how they compare. Our objective was to compare five analysis methods using Cox regression for this type of data, ranging from a crude model that ignores the stratification to a flexible one that allows nonproportional hazards and varying covariate effects across th ...[more]

PMID: 26051242

Similar Datasets

Project description:Background Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis. Failing to take the true non-linear functional form of the confounder-exposure and confounder-outcome associations into account may result in an under- or overestimation of the true exposure effect. Therefore, this paper aims to demonstrate the importance of assessing the linearity assumption for confounder-exposure and confounder-outcome associations and the importance of correctly specifying these associations when the linearity assumption is violated. Methods A Monte Carlo simulation study was used to assess and compare the performance of confounder-adjustment methods when the functional form of the confounder-exposure and confounder-outcome associations were misspecified (i.e., linearity was wrongly assumed) and correctly specified (i.e., linearity was rightly assumed) under multiple sample sizes. An empirical data example was used to illustrate that the misspecification of confounder-exposure and confounder-outcome associations leads to bias. Results The simulation study illustrated that the exposure effect estimate will be biased when for propensity score (PS) methods the confounder-exposure association is misspecified. For methods in which the outcome is regressed on the confounder or the PS, the exposure effect estimate will be biased if the confounder-outcome association is misspecified. In the empirical data example, correct specification of the confounder-exposure and confounder-outcome associations resulted in smaller exposure effect estimates. Conclusion When attempting to remove bias by adjusting for confounding, misspecification of the confounder-exposure and confounder-outcome associations might actually introduce bias. It is therefore important that researchers not only assess the linearity of the exposure-outcome effect, but also of the confounder-exposure or confounder-outcome associations depending on the confounder-adjustment method used. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01817-0.

Dataset Information

A method making fewer assumptions gave the most reliable estimates of exposure-outcome associations in stratified case-cohort studies.

Objective

Study design and setting

Results

Conclusion

Publications

A method making fewer assumptions gave the most reliable estimates of exposure-outcome associations in stratified case-cohort studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets