Additional outcomes and subgroup analyses of NXY-059 for acute ischemic stroke in the SAINT I trial

<p><b>Background and Purpose:</b> NXY-059 is a free radical-trapping neuroprotectant demonstrated to reduce disability from ischemic stroke. We conducted analyses on additional end points and sensitivity analyses to confirm our findings.</p> <p><b>Methods:</b> We randomized 1722 patients with acute ischemic stroke to a 72-hour infusion of placebo or intravenous NXY-059 within 6 hours of stroke onset. The primary outcome was disability at 90 days, as measured by the modified Rankin Scale (mRS), a 6-point scale ranging from 0 (no residual symptoms) to 5 (bed-bound, requiring constant care). Additional and exploratory analyses included mRS at 7 and 30 days; subgroup interactions with final mRS; assessments of activities of daily living by Barthel index; and National Institutes of Health Stroke Scale (NIHSS) neurological scores at 7 and 90 days.</p> <p><b>Results:</b> NXY-059 significantly improved the distribution of the mRS disability score compared with placebo at 7, 30, and 90 days (Cochran-Mantel-Haenszel test P=0.002, 0.004, 0.038, respectively; 90-day common odds ratio 1.20; 95% CI, 1.01 to 1.42). The benefit was not attributable to any specific baseline characteristic, stratification variable or subgroup interaction. Neurological scores were improved at 7 days (odds ratio [OR], 1.46; 95% CI, 1.13, 1.89; P=0.003) and the Barthel index was improved at 7 and 30 days (OR, 1.55; 95% CI, 1.22, 1.98; P<0.0001; OR, 1.27; 95% CI, 1.01, 1.59; P=0.02).</p> <p><b>Conclusions:</b> NXY-059 within 6 hours of acute ischemic stroke significantly reduced disability. Benefit on neurological scores and activities of daily living was detectable early but not significant at 90 days; however, our trial was underpowered to measure effects on the neurological examination. The benefit on disability is not confounded by interactions and is supported by other outcome measures.</p&gt

Stochastic Approximation and Modern Model-Based Designs for Dose-Finding Clinical Trials

In 1951 Robbins and Monro published the seminal article on stochastic approximation and made a specific reference to its application to the "estimation of a quantal using response, nonresponse data." Since the 1990s, statistical methodology for dose-finding studies has grown into an active area of research. The dose-finding problem is at its core a percentile estimation problem and is in line with what the Robbins--Monro method sets out to solve. In this light, it is quite surprising that the dose-finding literature has developed rather independently of the older stochastic approximation literature. The fact that stochastic approximation has seldom been used in actual clinical studies stands in stark contrast with its constant application in engineering and finance. In this article, I explore similarities and differences between the dose-finding and the stochastic approximation literatures. This review also sheds light on the present and future relevance of stochastic approximation to dose-finding clinical trials. Such connections will in turn steer dose-finding methodology on a rigorous course and extend its ability to handle increasingly complex clinical situations.Comment: Published in at http://dx.doi.org/10.1214/10-STS334 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Risk Stratification in Post-MI Patients Based on Left Ventricular Ejection Fraction and Heart-Rate Turbulence

Objectives: Development of risk stratification criteria for predicting mortality in post-infarction patients taking into account LVEF and heart-rate turbulence (HRT). Methods: Based on previous results the two parameters LVEF (continuously) and turbulence slope (TS) as an indicator of the HRT were combined for risk stratification. The method has been applied within two independent data sets (the MPIP-trial and the EMIAT-study). Results: The criteria were defined in order to match the outcome of applying LVEF ( 30 % in sensitivity. In the MPIP trial the optimal criteria selected are TS normal and LVEF ( 21 % or TS abnormal and LVEF ( 40 %. Within the placebo group of the EMIAT-study the corresponding criteria are: TS normal and LVEF ( 23 % or TS abnormal and LVEF ( 40 %. Combining both studies the following criteria could be obtained: TS normal and LVEF ( 20 % or TS abnormal and LVEF ( 40 %. In the MPIP study 83 out of the 581 patients (= 14.3 %) are fulfilling these criteria. Within this group 30 patients have died during the follow-up. In the EMIAT-trial 218 out of the 591 patients (= 37.9 %) are classified as high risk patients with 53 deaths. Combining both studies the high risk group contains 301 patients with 83 deaths (ppv = 27.7 %). Using the MADIT-criterion as classification rule (LVEF ( 30 %) a sample of 375 patients with 85 deaths (ppv = 24 %) can be selected. Conclusions: The stratification rule based on LVEF and TS is able to select high risk patients suitable for implanting an ICD. The rule performs better than the classical one with LVEF alone. The high risk group applying the new criteria is smaller with about the same number of deaths and therefor with a higher positive predictive value. The classification criteria have been validated within a bootstrap study with 100 replications. In all samples the rule based on TS and LVEF (= NEW) was superior to LVEV alone, the high risk group has been smaller (( s: 301 ( 14.5 (NEW) vs. 375 ( 14.5 (LVEF)) and the positive predictive value was larger (( s: 27.2 ( 2.6 % (NEW) vs. 23.3 ( 2.2 % (LVEF)). The new criteria are less expensive due to a reduced number of high risk patients selected

Sitting too much: a hierarchy of socio-demographic correlates

Too much sitting (extended sedentary time) is recognized as a public health concern in Europe and beyond. Time spent sedentary is influenced and conditioned by clusters of individual-level and contextual (upstream) factors. Identifying population subgroups that sit too much could help to develop targeted interventions to reduce sedentary time. We explored the relative importance of socio-demographic correlates of sedentary time in adults across Europe. We used data from 26,617 adults who participated in the 2013 Special Eurobarometer 412 "Sport and physical activity". Participants from all 28 EU Member States were randomly selected and interviewed face-to-face. Self-reported sedentary time was dichotomized into sitting less or >7.5h/day. A Chi-squared Automatic Interaction Detection (CHAID) algorithm was used to create a tree that hierarchically partitions the data on the basis of the independent variables (i.e., socio-demographic factors) into homogeneous (sub)groups with regard to sedentary time. This allows for the tentative identification of population segments at risk for unhealthy sedentary behaviour. Overall, 18.5% of the respondents reported sitting >7.5h/day. Occupation was the primary discriminator. The subgroup most likely to engage in extensive sitting were higher educated, had white-collar jobs, reported no difficulties with paying bills, and used the internet frequently. Clear socio-demographic profiles were identified for adults across Europe who engage in extended sedentary time. Furthermore, physically active participants were consistently less likely to engage in longer daily sitting times. In general, those with more indicators of higher wealth were more likely to spend more time sitting

Active Clinical Trials for Personalized Medicine

Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this paper, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learning the optimal ITRs) from an ongoing clinical trial. Simulation studies and real-data examples show that our active clinical trial method significantly improves on competing methods. We derive risk bounds and show that they support these observed empirical advantages.Comment: 48 Page, 9 Figures. To Appear in JASA--T&