Proportional Odds Logistic Regression for Ordered Category Outcomes

Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordinal logistic regression models are appropriate in many of these situations. This article presents a review of the proportional odds model, partial proportional odds model, continuation ratio model, and stereotype model. The fit, statistical inference, and comparisons between models are illustrated with data from a study on quality of life in 273 patients with schizophrenia. All tested models showed good fit, but the proportional odds or partial proportional odds models proved to be the best choice due to the nature of the data and ease of interpretation of the results. Ordinal logistic models perform differently depending on categorization of outcome, adequacy in relation to assumptions, goodness-of-fit, and parsimony.

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Mutual Fund Rating Prediction using Proportional Odds Logistic Regression with Imbalanced Class

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e1002.0285s20 ◽

2020 ◽

Vol 8 (6S) ◽

pp. 6-10

Keyword(s):

Logistic Regression ◽

Mutual Funds ◽

Mutual Fund ◽

Proportional Odds ◽

Professional Advice ◽

Rating Agencies ◽

New Class ◽

Rating Prediction ◽

Imbalanced Class ◽

First Time

Mutual funds ratings given by rating agencies, are very popular and helps new/first time investors to select and invest in funds based on the ratings a fund takes without going through the detailed portfolio. However sometimes these ratings could be biased or incorrect or in favor of specific fund and it could affect an investor decision. New investors face a lot of problems while investingand choosing mutual funds due to poor professional advice and lack of right tools and resources to assess a funds true performance. To overcome the problem of incorrect rating and to help an investor to choose the funds wisely using machine learning, we have attempted to predict the rating and classify mutual funds using proportional odds logistic regression which classifies funds intorating classes from 1 to 5 with 5 being the high rated fund and 1 being the low rated fund. While some prior studies have suggested methods of using clustering to classify based on performances using Supervised/Unsupervised learning, this paper deals with supervised learning forpredicting the ratings using the mutual fund financial ratios and also handles imbalanced classes.To handle imbalance class problem in a multi-class setting, we propose a new class balancing hybrid methodology of using EM and Gauss-Smote sampling that significantly improves the rating prediction

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Specialty pharmacist integration into an outpatient neurology clinic improves pimavanserin access

Mental Health Clinician ◽

10.9740/mhc.2021.05.187 ◽

2021 ◽

Vol 11 (3) ◽

pp. 187-193

Author(s):

Sabrina Livezey ◽

Nisha B. Shah ◽

Robert McCormick ◽

Josh DeClercq ◽

Leena Choi ◽

...

Keyword(s):

Logistic Regression ◽

Median Time ◽

Care Coordination ◽

Clinical Care ◽

Monitoring Program ◽

Patient Characteristics ◽

Primary Objective ◽

Proportional Odds ◽

Neurology Clinic ◽

Insurance Approval

Abstract Introduction Access to pimavanserin, the only Parkinson disease–related psychosis treatment approved by the FDA, is restricted by insurance requirements, a limited distribution network, and high costs. Following initiation, patients require monitoring for safety and effectiveness. The primary objective of this study was to evaluate impact of specialty pharmacist (SP) integration on time to insurance approval. Additionally, we describe a pharmacist-led monitoring program. Methods This was a single-center, retrospective study of adults prescribed pimavanserin by the neurology clinic from June 2016 to June 2018. Patients receiving pimavanserin externally or through clinical trials were excluded. Pre- (June 2016 to December 2016) and post-SP integration (January 2017 to June 2018) periods were assessed. Proportional odds logistic regression was performed to test association of approval time with patient characteristics (age, gender, insurance type) postintegration. Interventions were categorized as clinical care, care coordination, management of adverse event, or adherence. Results We included 94 patients (32 preintegration, 62 postintegration), 80% male (n = 75) and 96% white (n = 90) with a mean age of 73 years. Median time to approval was 22 days preintegration and 3 days postintegration. Higher rates of approval (81% vs 95%) and initiation (78% vs 94%) were observed postintegration. Proportional odds logistic regression suggested patients with commercial insurance were likely to have longer time to approval compared with patients with Medicare/Medicaid (odds ratio 7.1; 95% confidence interval: 1.9, 26.7; P = .004). Most interventions were clinical (51%, n = 47) or care coordination (42%, n = 39). Conclusion Median time to approval decreased postintegration. The SP performed valuable monitoring and interventions.

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