Hi Everyone Much to my surprise and pleasure the funding for my new grant has arrived in record time. We now have money for 4 or more positions for three years of support described below and are actively starting the recruiting process. If you or anyone you know might interested please pass this on.
Hiring up to four PhD post-docs and/or MS trained biostatisticians and/or clinical epidemiologists Immediate hires to work on recently funded NIH RC4 (RFA OD-10-009) titled NEW OBSERVATIONAL DATA ANALYSIS METHODS FOR COMPARATIVE EFFECTIVENESS RESEARCH. Work will be in the Washington University School of Medicine's Department of Medicine's Biostatistical Consulting Center (Dr. Shannon, Center Director and RC4 PI) where the statistical methods and data analyses will be performed, and the affiliated BJC HealthCare's Center for Healthcare Quality and Effectiveness (Dr. Dunagan, Center Director, BJC VP of Quality Improvement, and RC4 Co-Investigator) where CER expertise and data will be obtained. Great opportunity for successful candidates to develop independent research programs in comparative effectiveness research and statistical methods development. Interested applicants: MS Candidates: a) Please visit https://jobs.wustl.edu for full job posting and application information (Job Number 20616) (should be online within 48 hours) PhD Candidates: a) Obtain and review copy of proposal from [email protected]<mailto:[email protected]> b) Submit by mail to William Shannon, PhD c/o Ms. Kathy Kronk to address below a. Cover letter describing how you see yourself contributing to the proposals goal (1 page) b. Description of professional goals (1/2 page) c. Full CV d. Copy of 2 published or in-press papers (required for PhD's, encouraged for MS candidates) e. Names, titles, phone numbers, and email of three references ABSTRACT: Comparative effectiveness research (CER) is designed to identify healthcare interventions having the best patient outcomes to direct patients to receive the best treatment and to direct our healthcare dollars to where they will be most productive. When comparing observational data to determine the best intervention, CER requires that we apply risk or case-mix adjustment methods before examining outcomes of care. For example, to compare survival in treatment or hospital for inpatient acute myocardial infarction (AMI) patients using the proportion surviving may be misleading if the severity of disease is significantly different across interventions or hospital. To make comparisons valid, risk adjustment must balance patient factors, such as disease severity and co-morbidities, which result in different likelihood of death. A standard approach to risk adjustment is to use measures of "observed-to-expected" rates, where expected outcome for patients are estimated by an existing, often unknown and proprietary, regression model previously fit to a standard or reference population of patient data said to be representative of all patients. The observed outcome is obtained from the patient's discharge data. The goal of the risk adjustment is to determine if an intervention (or provider) on average shows better, worse, or the same observed outcomes compared to expected outcomes. We propose to develop and release an open-source HealthCare Rankings (HCR) case-mix adjustment software package combining methods from observational data analysis, operations research, statistics, and mathematics that have not been applied in combination previously in CER and health services research. The HCR algorithm ranks two or more interventions or providers simultaneously based on direct comparison of patient-level data. This algorithm avoids the need to have a reference database for observed-to-expected comparisons. This proposal is a joint effort of investigators in the Washington University School of Medicine (WUSM) Dept. of Medicine's Biostatistical Consulting Center and the BJC HealthCare Center for Clinical Excellence (CCE). There are 11 hospitals in the BJC network with a comprehensive informatics system of patient level clinical and administrative data available for developing and validating the HCR algorithm. EOE M/F/D/V Thank you Bill Shannon, PhD Associate Prof. of Biostatistics in Medicine Washington University School of Medicine 660 South Euclid Ave, Box 8005 St. Louis, MO 63110 [email protected]/314-454-8356<http://[email protected]/314-454-8356> ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
