Personalized treatment selection using observational data.

Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners have been advocating the use of new methods in determining the best treatment. Treatments that are geared toward the 'best' outcome for a patient based on his/her genetic markers and characteristics are of high importance. In this article, we develop an approach to predict the optimal personalized treatment based on observational data. We have used inverse probability of treatment weighted machine learning methods to obtain score functions to predict the optimal treatment. Extensive simulation studies showed that our proposed method has desirable performance in selecting the optimal treatment. We provided a case study to examine the Statin use on cognitive function to illustrate the use of our proposed method.

Serrated polyp detection rate in colonoscopies performed by gastrointestinal fellows.

Background: Clinically significant serrated polyp detection rate (CSSDR) and proximal serrated polyp detection rate (PSDR) have been suggested as the potential quality benchmarks for colonoscopy (CSSDR = 7% and PSDR = 11%) in comparison to the established benchmark adenoma detection rate (ADR). Another emerging milestone is the detection rate of lateral spreading lesions (LSLs). Objectives: This study aimed to evaluate CSSDR, PSDR, ADR, and LSL detection rates among gastrointestinal (GI) fellows performing a colonoscopy. A secondary aim was to evaluate patient factors associated with the detection rates of these lesions. Design and Methods: A retrospective review of 799 colonoscopy reports was performed. GI fellow details, demographic data, and pathology found on colonoscopy were collected. Multiple logistic regression analysis was performed to identify the factors associated with CSSDR, PSDR, ADR, and LSL detection rates. A p value < 0.05 was considered statistically significant. Results: For our patient population, the median age was 58 years; 396 (49.8%) were male and 386 (48.6%) were African American. The 15 GI fellows ranged from first (F1), second (F2), or third (F3) year of training. We found an overall CSSDR of 4.4%, PSDR of 10.5%, ADR of 42.1%, and LSL detection rate of 3.2%. Female gender was associated with CSSDR, while only age was associated with PSDR. GI fellow level of training was associated with LSL detection rate, with the odds of detecting them expected to be four times higher in F2/F3s than F1s. Conclusion: Although GI fellows demonstrated an above-recommended ADR and nearly reached target PSDR, they failed to achieve target CSSDR. Future studies investigating a benchmark for LSL detection rate are needed to quantify if GI fellows are detecting these lesions at adequate rates.