A nomogram was developed to enhance the use of multinomial logistic regression modeling in diagnostic research

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• 作者：Loes C.M. Bertens ; ^{L.C.M.Bertens-2@umcutrecht.nl" class="auth_mail" title="E-mail the corresponding author} ; ; Karel G.M. Moons ; Frans H. Rutten ; Yvonne van Mourik ; Arno W. Hoes ; Johannes B. Reitsma
• 关键词：Multinomial ; Regression analyses ; Nomogram ; Heart failure ; Chronic obstructive Pulmonary disease ; Diagnostic research
• 刊名：Journal of Clinical Epidemiology
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：71
• 期：Complete
• 页码：51-57
• 全文大小：387 K

文摘

We developed a nomogram to facilitate the interpretation and presentation of results from multinomial logistic regression models.

absSec_2">Study Design and Setting

abspara0015">We analyzed data from 376 frail elderly with complaints of dyspnea. Potential underlying disease categories were heart failure (HF), chronic obstructive pulmonary disease (COPD), the combination of both (HF and COPD), and any other outcome (other). A nomogram for multinomial model was developed to depict the relative importance of each predictor and to calculate the probability for each disease category for a given patient. Additionally, model performance of the multinomial regression model was assessed.

absSec_3">Results

abspara0020">Prevalence of HF and COPD was 14% (n = 54), HF 24% (n = 90), COPD 20% (n = 75), and Other 42% (n = 157). The relative importance of the individual predictors varied across these disease categories or was even reversed. The pairwise C statistics ranged from 0.75 (between HF and Other) to 0.96 (between HF and COPD and Other). The nomogram can be used to rank the disease categories from most to least likely within each patient or to calculate the predicted probabilities.

absSec_4">Conclusions

abspara0025">Our new nomogram is a useful tool to present and understand the results of a multinomial regression model and could enhance the applicability of such models in daily practice.