Objective: Multivariable modeling techniques are appearing in today’s medical literature with increasing frequency. Improper reporting of these statistical models can potentially make the results of a study inaccurate, misleading, or difficult to interpret. We performed a manual literature search of five international pulmonary and critical care journals to determine the accuracy in the reporting of logistic regression modeling strategies.
Design: We examined all of the published manuscripts for 12 potential limitations in the reporting of important statistical methodologies over a 6-month period from July 1, 2000, until December 31, 2000.
Results: Of the 81 articles that included multivariable logistic regression analyses, only 65% (53 analyses) properly reported the coding classification of pertinent independent variables that were included in the final model. An odds ratio and confidence interval were reported for the independent variables included in the final model for 79% (64 analyses) and 74% (60 analyses), respectively. Only 12% (10 articles) referenced whether interaction terms or effect modifications were examined, 1% (1 article) reported testing for collinearity, and only 16% (13 articles) included a goodness-of-fit analysis of the logistic model. The type of statistical package was reported in 69% (56 articles). Finally, approximately 39% of the articles (22 of 57) may have overfit the logistic regression model, leading to potentially unreliable regression coefficients and odds ratios.
Conclusions: Our results indicate that the reporting of multivariable logistic regression analyses in the pulmonary and critical care literature is often incomplete, therefore making it difficult for the reader to accurately interpret the manuscript. We recommend the implementation of adequate guidelines that will lead to overall improvements in the reporting and possibly to the conducting of multivariable analyses in the pulmonary medicine and critical care medicine literature.