Dataset Information

Bias in odds ratios by logistic regression modelling and sample size.

ABSTRACT:

Background

In epidemiological studies researchers use logistic regression as an analytical tool to study the association of a binary outcome to a set of possible exposures.

Methods

Using a simulation study we illustrate how the analytically derived bias of odds ratios modelling in logistic regression varies as a function of the sample size.

Results

Logistic regression overestimates odds ratios in studies with small to moderate samples size. The small sample size induced bias is a systematic one, bias away from null. Regression coefficient estimates shifts away from zero, odds ratios from one.

Conclusion

If several small studies are pooled without consideration of the bias introduced by the inherent mathematical properties of the logistic regression model, researchers may be mislead to erroneous interpretation of the results.

SUBMITTER: Nemes S

PROVIDER: S-EPMC2724427 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bias in odds ratios by logistic regression modelling and sample size.

Nemes Szilard S Jonasson Junmei Miao JM Genell Anna A Steineck Gunnar G

BMC medical research methodology 20090727

<h4>Background</h4>In epidemiological studies researchers use logistic regression as an analytical tool to study the association of a binary outcome to a set of possible exposures.<h4>Methods</h4>Using a simulation study we illustrate how the analytically derived bias of odds ratios modelling in logistic regression varies as a function of the sample size.<h4>Results</h4>Logistic regression overestimates odds ratios in studies with small to moderate samples size. The small sample size induced bia ...[more]

PMID: 19635144

Similar Datasets

Project description:BackgroundThe odds ratio (OR) is used as an important metric of comparison of two or more groups in many biomedical applications when the data measure the presence or absence of an event or represent the frequency of its occurrence. In the latter case, researchers often dichotomize the count data into binary form and apply the well-known logistic regression technique to estimate the OR. In the process of dichotomizing the data, however, information is lost about the underlying counts which can reduce the precision of inferences on the OR.MethodsWe propose analyzing the count data directly using regression models with the log odds link function. With this approach, the parameter estimates in the model have the exact same interpretation as in a logistic regression of the dichotomized data, yielding comparable estimates of the OR. We prove analytically, using the Fisher information matrix, that our approach produces more precise estimates of the OR than logistic regression of the dichotomized data. We also show the gains in precision using simulation studies and real-world datasets. We focus on three related distributions for count data: geometric, Poisson, and negative binomial.ResultsIn simulation studies, confidence intervals for the OR were 56-65% as wide (geometric model), 75-79% as wide (Poisson model), and 61-69% as wide (negative binomial model) as the corresponding interval from a logistic regression produced by dichotomizing the data. When we analyzed existing datasets using our approach, we found that confidence intervals for the OR could be up to 64% shorter (36% as wide) compared to if the data had been dichotomized and analyzed using logistic regression.ConclusionsMore precise estimates of the OR can be obtained directly from the count data by using the log odds link function. This analytic approach is easy to implement in software packages that are capable of fitting generalized linear models or of maximizing user-defined likelihood functions.

Dataset Information

Bias in odds ratios by logistic regression modelling and sample size.

Background

Methods

Results

Conclusion

Publications

Bias in odds ratios by logistic regression modelling and sample size.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets