Effects of alteplase for acute stroke on the distribution of functional outcomes: a pooled analysis of 9 trials

Background—Thrombolytic therapy with intravenous alteplase within 4.5 hours of ischemic stroke onset increases the overall likelihood of an excellent outcome (no, or nondisabling, symptoms). Any improvement in functional outcome distribution has value, and herein we provide an assessment of the effect of alteplase on the distribution of the functional level by treatment delay, age, and stroke severity. Methods—Prespecified pooled analysis of 6756 patients from 9 randomized trials comparing alteplase versus placebo/open control. Ordinal logistic regression models assessed treatment differences after adjustment for treatment delay, age, stroke severity, and relevant interaction term(s). Results—Treatment with alteplase was beneficial for a delay in treatment extending to 4.5 hours after stroke onset, with a greater benefit with earlier treatment. Neither age nor stroke severity significantly influenced the slope of the relationship between benefit and time to treatment initiation. For the observed case mix of patients treated within 4.5 hours of stroke onset (mean 3 hours and 20 minutes), the net absolute benefit from alteplase (ie, the difference between those who would do better if given alteplase and those who would do worse) was 55 patients per 1000 treated (95% confidence interval, 13–91; P=0.004). Conclusions—Treatment with intravenous alteplase initiated within 4.5 hours of stroke onset increases the chance of achieving an improved level of function for all patients across the age spectrum, including the over 80s and across all severities of stroke studied (top versus bottom fifth means: 22 versus 4); the earlier that treatment is initiated, the greater the benefit

Stroke treatment academic industry roundtable recommendations for individual data pooling analyses in stroke

Pooled analysis of individual patient data from stroke trials can deliver more precise estimates of treatment effect, enhance power to examine prespecified subgroups, and facilitate exploration of treatment-modifying influences. Analysis plans should be declared, and preferably published, before trial results are known. For pooling trials that used diverse analytic approaches, an ordinal analysis is favored, with justification for considering deaths and severe disability jointly. Because trial pooling is an incremental process, analyses should follow a sequential approach, with statistical adjustment for iterations. Updated analyses should be published when revised conclusions have a clinical implication. However, caution is recommended in declaring pooled findings that may prejudice ongoing trials, unless clinical implications are compelling. All contributing trial teams should contribute to leadership, data verification, and authorship of pooled analyses. Development work is needed to enable reliable inferences to be drawn about individual drug or device effects that contribute to a pooled analysis, versus a class effect, if the treatment strategy combines ≥2 such drugs or devices. Despite the practical challenges, pooled analyses are powerful and essential tools in interpreting clinical trial findings and advancing clinical care

Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

User opinions expressed in the form of ratings can influence an individual's view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance different users place on different aspects of an item. We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.Comment: Accepted for publication in IJCAI 201