A COMPARISON OF LOGISTIC REGRESSION TO RANDOM FORESTS FOR EXPLORING DIFFERENCES IN RISK FACTORS ASSOCIATED WITH STAGE ATDIAGNOSIS BETWEEN BLACK AND WHITE COLON CANCER PATIENTS

Introduction: Colon cancer is one of the most common malignancies in America. According to the American Cancer Society, blacks have lower survival rate than whites. Many previous studies suggested that it is because blacks were more likely to be diagnosed at a late stage. Hence, it is crucial to determine factors that are associated with colon cancer stage at diagnosis. Objectives: The objectives of this study are twofold: 1)To compare logistic regression modeling to Random Forests classification with respect to variables selected and classification accuracy; and 2) To evaluate the factors related to colon cancer stage at diagnosis in a population based study. Many studies have comparedClassification and Regression Trees (CART) to logistic regression and found that they have very similar power with respect to the proportion correctly classified and the variables selected. This study extends previous methodological research by comparing the Random Forests classification techniques to logistic regression modeling using a relatively small and incomplete dataset. Methods and Materials: The data used in this research were from National Cancer Institute Black/White Cancer Survival Study which had 960 cases of invasive colon cancer. Stage at diagnosis was used as the dependent variable for fitting logistic regression models and Random Forests Classification to multiple potential explanatory variables, which included some missing data. Results: Odds ratio (blacks vs. whites) decreased from 1.628 (95%CI: 1.068-2.481) to 1.515 (95% CI: 0.920-2.493) after adjustment was made for patient delay in diagnosis, occupation, histology and grade of tumor. Race became no longer important after these variables were entered in the Random Forests. These four variables were identified as the most important variables associated with racial disparity in colon cancer stage at diagnosis in both logistic regression and Random Forests. The correctclassification rate was 47.9% using logistic regression and was 33.9% using Random Forests. Conclusion: 1). Logistic regression and Random Forests had very similar power in variable selection. 2). Logistic regression had higher classification accuracy than Random Forests with respect to overall correct classification rate

New Organizational Challenges in a Digital World: Securing Cloud Computing Usage and Reacting to Asset-Sharing Platform Disruptions

Information technology (IT) and IT-enabled business models are transforming the business ecosystem and posing new challenges for existing companies. This two-essay dissertation examines two such challenges: cloud security and the disruption of asset-sharing business models.The first essay examines how an organizations usage of cloud storage affects its likelihood of accidental breaches. The quasi-experiment in the U.S. healthcare sector reveals that organizations with higher levels of digitalization (i.e., Electronic Health Records levels) or those with more IT applications running on their internal data center are less likely to experience accidental breaches after using public cloud storage. We argue that digitalization and operational control over IT applications increase organizations awareness and capabilities of establishing a company-wide security culture, thereby reducing negligence related to physical devices and unintended disclosure after adopting cloud storage. The usage of cloud storage is more likely to cause accidental breaches for organizations contracting to more reputable or domain expert vendors. We explain this result as the consequence of less attention being focused on securing personally accessible data and physical devices given high reliance on reputed and knowledgeable cloud providers. This research is among the first to empirically examine the actual security impacts of organizations cloud storage usage and offers practical insights for cloud security management.The second essay examines how Asset-Sharing Business Model Prevalence (ASBMP) affects the performance implications of industry incumbent firms competitive actions when faced with entrants with asset-sharing business models, like Airbnb. ASBMP represents the amount of third-party products and services that originally were unavailable inside the traditional business model but now are orchestrated by asset-sharing companies in an industry. We use texting mining and econometrics approaches to analyze a longitudinal dataset in the accommodation industry. Our results demonstrate that incumbents competitive action repertoires (i.e., action volume, complexity, and heterogeneity) increase their performance when the ASBMP is high but decrease incumbents performance when the ASBMP is low. Practically, incumbents who are facing greater threat from asset-sharing firms can implement more aggressive competitive action repertoires and strategically focus on new product and M&A strategies. This research contributes to the literature of both competitive dynamics and asset-sharing business models

A comparison of logistic regression to decision tree induction in the diagnosis of Carpal Tunnel Syndrome

SIGLEAvailable from British Library Document Supply Centre-DSC:8716.785(1998/01) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A comparison of logistic regression to decision tree induction in the diagnosis of Carpal Tunnel Syndrome

SIGLEAvailable from British Library Document Supply Centre-DSC:8716.785(1998/01) / BLDSC - British Library Document Supply CentreGBUnited Kingdo