Challenges and Opportunities to Updating Prescribing Information for Longstanding Oncology Drugs.

A number of important drugs used to treat cancer-many of which serve as the backbone of modern chemotherapy regimens-have outdated prescribing information in their drug labeling. The Food and Drug Administration is undertaking a pilot project to develop a process and criteria for updating prescribing information for longstanding oncology drugs, based on the breadth of knowledge the cancer community has accumulated with the use of these drugs over time. This article highlights a number of considerations for labeling updates, including selecting priorities for updating; data sources and evidentiary criteria; as well as the risks, challenges, and opportunities for iterative review to ensure prescribing information for oncology drugs remains relevant to current clinical practice

Caffeine for asthma

Background Caffeine has a variety of pharmacological effects; it is a weak bronchodilator and it also reduces respiratory muscle fatigue. It is chemically related to the drug theophylline which is used to treat asthma. It has been suggested that caffeine may reduce asthma symptoms and interest has been expressed in its potential role as an asthma treatment. A number of studies have explored the effects of caffeine in asthma, this is the first review to systematically examine and summarise the evidence. Objectives To assess the effects of caffeine on lung function and identify whether there is a need to control for caffeine consumption prior to either lung function or exhaled nitric oxide testing. Search strategy We searched the Cochrane Airways Group trials register and the reference lists of articles (August 2009). We also contacted study authors.Selection criteriaRandomised clinical trials of oral caffeine compared to placebo or coffee compared to decaffeinated coffee in adults with asthma. Data collection and analysis Trial selection, quality assessment and data extraction were done independently by two reviewers. Main results Seven trials involving a total of 75 people with mild to moderate asthma were included. The studies were all of cross-over design.Six trials involving 55 people showed that in comparison with placebo, caffeine, even at a 'low dose' (< 5 mg/kg body weight), appears to improve lung function for up to two hours after consumption. Forced expiratory volume in one minute showed a small improvement up to two hours after caffeine ingestion (SMD 0.72; 95% CI 0.25 to 1.20), which translates into a 5% mean difference in FEV1. However in two studies the mean differences in FEV1 were 12% and 18% after caffeine. Mid-expiratory flow rates also showed a small improvement with caffeine and this was sustained up to four hours.One trial involving 20 people examined the effect of drinking coffee versus a decaffeinated variety on the exhaled nitric oxide levels in patients with asthma and concluded that there was no significant effect on this outcome. Authors' conclusions Caffeine appears to improve airways function modestly, for up to four hours, in people with asthma. People may need to avoid caffeine for at least four hours prior to lung function testing, as caffeine ingestion could cause misinterpretation of the results. Drinking caffeinated coffee before taking exhaled nitric oxide measurements does not appear to affect the results of the test, but more studies are needed to confirm this

Residual Weighted Learning for Estimating Individualized Treatment Rules

Personalized medicine has received increasing attention among statisticians, computer scientists, and clinical practitioners. A major component of personalized medicine is the estimation of individualized treatment rules (ITRs). Recently, Zhao et al. (2012) proposed outcome weighted learning (OWL) to construct ITRs that directly optimize the clinical outcome. Although OWL opens the door to introducing machine learning techniques to optimal treatment regimes, it still has some problems in performance. In this article, we propose a general framework, called Residual Weighted Learning (RWL), to improve finite sample performance. Unlike OWL which weights misclassification errors by clinical outcomes, RWL weights these errors by residuals of the outcome from a regression fit on clinical covariates excluding treatment assignment. We utilize the smoothed ramp loss function in RWL, and provide a difference of convex (d.c.) algorithm to solve the corresponding non-convex optimization problem. By estimating residuals with linear models or generalized linear models, RWL can effectively deal with different types of outcomes, such as continuous, binary and count outcomes. We also propose variable selection methods for linear and nonlinear rules, respectively, to further improve the performance. We show that the resulting estimator of the treatment rule is consistent. We further obtain a rate of convergence for the difference between the expected outcome using the estimated ITR and that of the optimal treatment rule. The performance of the proposed RWL methods is illustrated in simulation studies and in an analysis of cystic fibrosis clinical trial data.Comment: 48 pages, 3 figure