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Browsing by Author "Sherer, Eric A."
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Item The accuracy and completeness for receipt of colorectal cancer care using Veterans Health Administration administrative data.(BMC, 2016) Sherer, Eric A.; Fisher, Deborah A.; Barnd, Jeffrey; Jackson, George L.; Provenzale, Dawn; Haggstrom, David A.; Department of Medicine, IU School of MedicineThe National Comprehensive Cancer Network and the American Society of Clinical Oncology have established guidelines for the treatment and surveillance of colorectal cancer (CRC), respectively. Considering these guidelines, an accurate and efficient method is needed to measure receipt of care.Item Clinical Trial Simulation to Evaluate Population Pharmacokinetics and Food Effect: Capturing Abiraterone and Nilotinib Exposures(John Wiley & Sons, Inc., 2015-05) Li, Claire H.; Sherer, Eric A.; Lewis, Lionel D.; Bies, Robert R.; Department of Medicine, IU School of MedicineThe objectives of this study were to determine (1) the accuracy with which individual patient level exposure can be determined and (2) whether a known food effect can be identified in a trial simulation of a typical population pharmacokinetic trial. Clinical trial simulations were undertaken using NONMEM VII to assess a typical oncology pharmacokinetic trial design. Nine virtual trials for each compound were performed for combinations of different level of between-occasion variability, number of patients in the trial and magnitude of a food covariate on oral clearance. Less than 5% and 20% bias and precision were obtained in individual clearance estimated for both abiraterone and nilotinib using this design. This design resulted biased and imprecise population clearance estimates for abiraterone. The between-occasion variability in most trials was captured with less than 30% of percent bias and precision. The food effect was detectable as a statistically significant covariate on oral clearance for abiraterone and nilotinib with percent bias and precision of the food covariate less than 20%. These results demonstrate that clinical trial simulation can be used to explore the ability of specific trial designs to evaluate the power to identify individual and population level exposures,covariate and variability effects.Item A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection(Wiley, 2015-01) Sale, Mark; Sherer, Eric A.; Department of Medicine, IU School of MedicineThe current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection.Item Tailoring Surveillance Colonoscopy in Patients with Advanced Adenomas(Elsevier, 2021) Kahi, Charles J.; Myers, Laura J.; Stump, Timothy E.; Imler, Timothy D.; Sherer, Eric A.; Larson, Jason; Imperiale, Thomas F.; Medicine, School of MedicineBackground and Aims Patients with advanced colorectal adenomas (AA) are directed to undergo intensive surveillance. However, the benefit derived from surveillance may be outweighed by the risk of death from non-colorectal cancer (CRC) causes, leading to uncertainty on how best to individualize follow-up. The aim of this study was to derive a risk prediction model and risk index that estimates and stratifies the risk for non-colorectal cancer mortality (NCM) subsequent to diagnosis and removal of AA. Methods We conducted a retrospective cohort study of Veterans > 40 years who had colonoscopy for diagnostic or screening indications at 13 VAMCs between 2002 and 2009, and had one or more AAs. The primary outcome was non-CRC mortality (NCM) using a fixed follow-up time period of 5 years. Logistic regression using the lasso technique was used to identify factors independently associated with non-CRC mortality (NCM), and an index based on points from regression coefficients was constructed to estimate risk of 5-year NCM. Results We identified 2,943 Veterans with AA (mean age (SD) 63 (8.6) years, 98% male, 74% white), with an overall 5-year mortality of 16.7%, which was nearly all due to NCM (16.6%). Age, comorbidity burden, specific comorbid conditions, and hospitalization within the preceding year were independently associated with NCM. The risk prediction model had a goodness of fit (calibration) p-value of 0.41, and c-statistic (discrimination) of 0.74 (95% CI, 0.71-0.76). Based on comparable 5-year risks of NCM, the scores comprised 3 risk categories: low (score of 0-1), intermediate (score of 2-4) and high (score of ≥ 5), in which NCM occurred in 6.5%, 14.1%, and 33.2%, respectively. Conclusions We derived a risk prediction model that identifies Veterans at high risk of NCM within 5 years, and who are thus unlikely to benefit from further surveillance.