Moss Marc, Wellman D Andrew, Cotsonis George A
Department of Medicine, Division of Pulmonary and Critical Care Medicine, Emory Unversity, Atlanta, GA, USA.
Chest. 2003 Mar;123(3):923-8. doi: 10.1378/chest.123.3.923.
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.
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.
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.
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.
多变量建模技术在当今医学文献中出现的频率越来越高。这些统计模型报告不当可能会使研究结果不准确、产生误导或难以解释。我们对五本国际肺部和重症医学期刊进行了人工文献检索，以确定逻辑回归建模策略报告的准确性。
我们检查了2000年7月1日至2000年12月31日这6个月期间发表的所有手稿，以查找重要统计方法报告中的12个潜在局限性。
在81篇包含多变量逻辑回归分析的文章中，只有65%（53项分析）正确报告了最终模型中相关自变量的编码分类。最终模型中包含的自变量的比值比和置信区间分别有79%（64项分析）和74%（60项分析）进行了报告。只有12%（10篇文章）提及是否检验了交互项或效应修正，1%（1篇文章）报告了共线性检验，只有16%（13篇文章）对逻辑模型进行了拟合优度分析。69%（56篇文章）报告了统计软件包的类型。最后，约39%的文章（57篇中的22篇）可能过度拟合了逻辑回归模型，导致回归系数和比值比可能不可靠。
我们的结果表明，肺部和重症医学文献中多变量逻辑回归分析的报告往往不完整，因此读者难以准确解释手稿。我们建议实施适当的指南，以全面改善报告情况，并可能改善肺部医学和重症医学文献中的多变量分析。
