Regularized Ordinal Regression and the ordinalNet R Package

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, for instance to accommodate unordered categorical data. We introduce an elastic net penalty class that applies to both model forms. Additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class

Regularized Ordinal Regression and the ordinalNet R Package

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, that can be used to model either ordered or unordered categorical response data. We call this the elementwise link multinomial-ordinal class, and it includes widely used models such as multinomial logistic regression (which also has an ordinal form) and ordinal logistic regression (which also has an unordered multinomial form). We introduce an elastic net penalty class that applies to either model form, and additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class

Genetic inhibition of autophagy in a transgenic mouse model of anal cancer

Background: The dynamic role of autophagy in cancer development is a topic of considerable research and debate. Previously published studies have shown that anal cancer development can be promoted or prevented with the pharmacologic inhibition or induction, respectively, of autophagy in a human papillomavirus (HPV) mouse model. However, these results are confounded by the fact that the drugs utilized are known to affect other pathways besides autophagy. It has also been shown that autophagic inhibition occurs in the setting of HPV16 oncoprotein expression (E6 and E7) and correlates with increased susceptibility to anal carcinogenesis. Materials and Methods: In this study, we employed a conditional, genetic, autophagic (Atg7) knockout mouse model to determine conclusively that autophagy has a role in anal cancer development, in the absence or presence of E6 and E7. Results: In mice lacking both HPV16 oncogenes, knockout of autophagy followed by exposure to a carcinogen resulted in a tumor incidence of 40%, compared to 0% in mice treated with a carcinogen alone with an intact autophagic pathway (P = 0.007). In mice expressing either one or both HPV16 oncoproteins, the addition of genetic knockout of autophagy to carcinogen treatment did not lead to a significant difference in tumor incidence compared to carcinogen treatment alone, consistent with the ability of HPV oncogenes to inhibit autophagy in themselves. Conclusions: These results provide the first conclusive evidence for the distinct role of autophagy in anal carcinogenesis, and suggest that autophagy is a plausible target for therapies aimed at reducing anal dysplasia and anal cancer development

Association Between Medicaid Status, Social Determinants of Health, and Bariatric Surgery Outcomes

Objective:. To compare outcomes after bariatric surgery between Medicaid and non-Medicaid patients and assess whether differences in social determinants of health were associated with postoperative weight loss. Background:. The literature remains mixed on weight loss outcomes and healthcare utilization for Medicaid patients after bariatric surgery. It is unclear if social determinants of health geocoded at the neighborhood level are associated with outcomes. Methods:. Patients who underwent laparoscopic sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) from 2008 to 2017 and had ≥1 year of follow-up within a large health system were included. Baseline characteristics, 90-day and 1-year outcomes, and weight loss were compared between Medicaid and non-Medicaid patients. Area deprivation index (ADI), urbanicity, and walkability were analyzed at the neighborhood level. Median regression with percent total body weight (TBW) loss as the outcome was used to assess predictors of weight loss after surgery. Results:. Six hundred forty-seven patients met study criteria (191 Medicaid and 456 non-Medicaid). Medicaid patients had a higher 90-day readmission rate compared to non-Medicaid patients (19.9% vs 12.3%, P < 0.016). Weight loss was similar between Medicaid and non-Medicaid patients (23.1% vs 21.9% TBW loss, respectively; P = 0.266) at a median follow-up of 3.1 years. In adjusted analyses, Medicaid status, ADI, urbanicity, and walkability were not associated with weight loss outcomes. Conclusions:. Medicaid status and social determinants of health at the neighborhood level were not associated with weight loss outcomes after bariatric surgery. These findings suggest that if Medicaid patients are appropriately selected for bariatric surgery, they can achieve equivalent outcomes as non-Medicaid patients