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Multinomial Logistic Regression Approach for the Evaluation of Binary Diagnostic Test in Medical Research

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  • Dwivedi Alok Kumar

    (Division of Biostatistics & Epidemiology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas, United States)

  • Mallawaarachchi Indika

    (Division of Biostatistics & Epidemiology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas, United States)

  • Figueroa-Casas Juan B.

    (Division of Pulmonary and Critical Care Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas, United States)

  • Morales Angel M.

    (Department of Surgery, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX 79905, United States)

  • Tarwater Patrick

    (Division of Biostatistics & Epidemiology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas, United States)

Abstract

Evaluating the effect of variables on diagnostic measures (sensitivity, specificity, positive, and negative predictive values) is often of interest to clinical researchers. Logistic regression (LR) models can be used to predict diagnostic measures of a screening test. A marginal model framework using generalized estimating equation (GEE) with logit/log link can be used to compare the diagnostic measures between two or more screening tests. These individual modeling approaches to each diagnostic measure ignore the dependency among these measures that might affect the association of covariates with each diagnostic measure. The diagnostic measures are computed using joint distribution of screening test result and reference test result which generates a multinomial response data. Thus, multinomial logistic regression (MLR) is a more appropriate approach to modeling these diagnostic measures. In this study, the validity of LR and GEE approaches as compared to MLR model was assessed for modeling diagnostic measures. All methods provided unbiased estimates of diagnostic measures in the absence of any covariate. LR and GEE methods produced more biased estimates as compared to MLR approach especially for small sample size studies. No bias was obtained in predicting sensitivity measure using MLR method for one screening test. Our proposed MLR method is robust for modeling diagnostic measures of a screening test as opposed to LR method. MLR method and GEE method produced similar estimates of diagnostic measures for comparing two screening tests in large sample size studies. The proposed MLR model for diagnostic measures is simple, and available in common statistical software. Our study demonstrates that MLR method should be preferred as an alternative for modeling diagnostic measures.

Suggested Citation

  • Dwivedi Alok Kumar & Mallawaarachchi Indika & Figueroa-Casas Juan B. & Morales Angel M. & Tarwater Patrick, 2015. "Multinomial Logistic Regression Approach for the Evaluation of Binary Diagnostic Test in Medical Research," Statistics in Transition New Series, Polish Statistical Association, vol. 16(2), pages 203-222, June.
    • Handle: RePEc:vrs:stintr:v:16:y:2015:i:2:p:203-222:n:6
    DOI: 10.21307/stattrans-2015-011
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    References listed on IDEAS

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