Predictor Importance in Logistic Regression: Examination of Inferential Approaches

2007 ◽  
Author(s):  
Razia Azen ◽  
Nicole M. Traxel
Author(s):  
Praveen V. Mummaneni ◽  
Mohamad Bydon ◽  
John J. Knightly ◽  
Mohammed Ali Alvi ◽  
Yagiz U. Yolcu ◽  
...  

OBJECTIVE Optimizing patient discharge after surgery has been shown to impact patient recovery and hospital/physician workflow and to reduce healthcare costs. In the current study, the authors sought to identify risk factors for nonroutine discharge after surgery for cervical myelopathy by using a national spine registry. METHODS The Quality Outcomes Database cervical module was queried for patients who had undergone surgery for cervical myelopathy between 2016 and 2018. Nonroutine discharge was defined as discharge to postacute care (rehabilitation), nonacute care, or another acute care hospital. A multivariable logistic regression predictive model was created using an array of demographic, clinical, operative, and patient-reported outcome characteristics. RESULTS Of the 1114 patients identified, 11.2% (n = 125) had a nonroutine discharge. On univariate analysis, patients with a nonroutine discharge were more likely to be older (age ≥ 65 years, 70.4% vs 35.8%, p < 0.001), African American (24.8% vs 13.9%, p = 0.007), and on Medicare (75.2% vs 35.1%, p < 0.001). Among the patients younger than 65 years of age, those who had a nonroutine discharge were more likely to be unemployed (70.3% vs 36.9%, p < 0.001). Overall, patients with a nonroutine discharge were more likely to present with a motor deficit (73.6% vs 58.7%, p = 0.001) and more likely to have nonindependent ambulation (50.4% vs 14.0%, p < 0.001) at presentation. On multivariable logistic regression, factors associated with higher odds of a nonroutine discharge included African American race (vs White, OR 2.76, 95% CI 1.38–5.51, p = 0.004), Medicare coverage (vs private insurance, OR 2.14, 95% CI 1.00–4.65, p = 0.04), nonindependent ambulation at presentation (OR 2.17, 95% CI 1.17–4.02, p = 0.01), baseline modified Japanese Orthopaedic Association severe myelopathy score (0–11 vs moderate 12–14, OR 2, 95% CI 1.07–3.73, p = 0.01), and posterior surgical approach (OR 11.6, 95% CI 2.12–48, p = 0.004). Factors associated with lower odds of a nonroutine discharge included fewer operated levels (1 vs 2–3 levels, OR 0.3, 95% CI 0.1–0.96, p = 0.009) and a higher quality of life at baseline (EQ-5D score, OR 0.43, 95% CI 0.25–0.73, p = 0.001). On predictor importance analysis, baseline quality of life (EQ-5D score) was identified as the most important predictor (Wald χ2 = 9.8, p = 0.001) of a nonroutine discharge; however, after grouping variables into distinct categories, socioeconomic and demographic characteristics (age, race, gender, insurance status, employment status) were identified as the most significant drivers of nonroutine discharge (28.4% of total predictor importance). CONCLUSIONS The study results indicate that socioeconomic and demographic characteristics including age, race, gender, insurance, and employment may be the most significant drivers of a nonroutine discharge after surgery for cervical myelopathy.


2009 ◽  
Vol 34 (3) ◽  
pp. 319-347 ◽  
Author(s):  
Razia Azen ◽  
Nicole Traxel

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R2 analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A simulation study, using both simple random sampling from a known population and bootstrap sampling from a single (parent) random sample, was performed to evaluate the bias, sampling distribution, and confidence intervals of quantitative dominance measures as well as the reproducibility of qualitative dominance measures. Results indicated that the bootstrap procedure is feasible and can be used in applied research to generalize logistic regression dominance analysis results to the population of interest. The procedures for determining and interpreting the general dominance of predictors in a logistic regression context are illustrated with an empirical example.


Author(s):  
Michael Brusco

Logistic regression is one of the most fundamental tools in predictive analytics. Graduate business analytics students are often familiarized with implementation of logistic regression using Python, R, SPSS, or other software packages. However, an understanding of the underlying maximum likelihood model and the mechanics of estimation are often lacking. This paper describes two Excel workbooks that can be used to enhance conceptual understanding of logistic regression in several respects: (i) by providing a clear formulation and solution of the maximum likelihood estimation problem; (ii) by showing the process for testing the significance of logistic regression coefficients; (iii) by demonstrating different methods for model selection to avoid overfitting, specifically, all possible subsets ordinary least squares regression and l1-regularized logistic regression (lasso); and (iv) by illustrating the measurement of relative predictor importance using all possible subsets.


2007 ◽  
Vol 23 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Carmen Hagemeister

Abstract. When concentration tests are completed repeatedly, reaction time and error rate decrease considerably, but the underlying ability does not improve. In order to overcome this validity problem this study aimed to test if the practice effect between tests and within tests can be useful in determining whether persons have already completed this test. The power law of practice postulates that practice effects are greater in unpracticed than in practiced persons. Two experiments were carried out in which the participants completed the same tests at the beginning and at the end of two test sessions set about 3 days apart. In both experiments, the logistic regression could indeed classify persons according to previous practice through the practice effect between the tests at the beginning and at the end of the session, and, less well but still significantly, through the practice effect within the first test of the session. Further analyses showed that the practice effects correlated more highly with the initial performance than was to be expected for mathematical reasons; typically persons with long reaction times have larger practice effects. Thus, small practice effects alone do not allow one to conclude that a person has worked on the test before.


2012 ◽  
Vol 2 (2) ◽  
pp. 72-81
Author(s):  
Christina M. Rudin-Brown ◽  
Eve Mitsopoulos-Rubens ◽  
Michael G. Lenné

Random testing for alcohol and other drugs (AODs) in individuals who perform safety-sensitive activities as part of their aviation role was introduced in Australia in April 2009. One year later, an online survey (N = 2,226) was conducted to investigate attitudes, behaviors, and knowledge regarding random testing and to gauge perceptions regarding its effectiveness. Private, recreational, and student pilots were less likely than industry personnel to report being aware of the requirement (86.5% versus 97.1%), to have undergone testing (76.5% versus 96.1%), and to know of others who had undergone testing (39.9% versus 84.3%), and they had more positive attitudes toward random testing than industry personnel. However, logistic regression analyses indicated that random testing is more effective at deterring AOD use among industry personnel.


2001 ◽  
Vol 6 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Michaela Kiernan ◽  
Helena C. Kraemer ◽  
Marilyn A. Winkleby ◽  
Abby C. King ◽  
C. Barr Taylor

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