Human recombinant activated factor VII for upper gastrointestinal bleeding in patients with liver diseases.

BACKGROUND: Mortality from upper gastrointestinal bleeding in patients with liver disease is high. Recombinant human activated factor VII (rHuFVIIa) has been suggested for patients with liver disease and upper gastrointestinal bleeding. OBJECTIVES: To assess the beneficial and harmful effects of rHuFVIIa in patients with liver disease and upper gastrointestinal bleeding. SEARCH METHODS: We searched the Cochrane Hepato-Biliary Group Controlled Trials Register (December 2011), the Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library (Issue 4, 2011), MEDLINE (1948 to December 2011), EMBASE (1980 to December 2011), Science Citation Index Expanded (1900 to December 2011), and LILACS (December 2011). We sought additional randomised trials from the reference lists of the trials and reviews identified through the electronic searches. SELECTION CRITERIA: Randomised clinical trials. DATA COLLECTION AND ANALYSIS: Outcome data from randomised clinical trials were extracted and were presented using random-effects model meta-analyses. Data on the risk of bias in the included trials were also extracted. MAIN RESULTS: We included two trials with 493 randomised participants with various Child-Pugh scores. The trials had a low risk of bias. The rHuFVIIa administration did not reduce the risk of mortality within five days (21/288 (7.3%) versus 15/205 (7.3%); risk ratio (RR) 0.88, 95% confidence interval (CI) 0.48 to 1.64, I(2) = 49%) and within 42 days (5/286 (1.7%) versus 36/205 (17.6%); RR 1.01, 95% CI 0.55 to 1.87, I(2) = 55%) when compared with placebo. Trial sequential analysis demonstrated that there is sufficient evidence to exclude that rHuFVIIa decreases mortality by 80%, but there is insufficient evidence to exclude smaller effects. The rHuFVIIa did not increase the risk of adverse events by number of patients (218/297 (74%) and 164/210 (78%); RR 0.94, 95% CI 0.84 to 1.04, I(2) = 1%), serious adverse events by adverse events reported (164/590 (28%) versus 123/443 (28%); RR 0.91, 95% CI 0.75 to 1.11, I(2) = 0%), and thromboembolic adverse events (16/297 (5.4%) versus 14/210 (6.7%); RR 0.80, 95% CI 0.40 to 1.60, I(2) = 0%) when compared with placebo. AUTHORS' CONCLUSIONS: We found no evidence to support or reject the administration of rHuFVIIa for patients with liver disease and upper gastrointestinal bleeding. Further adequately powered randomised clinical trials are needed in order to evaluate the proper role of rHuFVIIa for treating upper gastrointestinal bleeding in patients with liver disease. Although the results are based on trials with low risk of bias, the heterogeneity and the small sample size result in rather large confidence intervals that cannot exclude the possibility that the intervention has some beneficial or harmful effect. Further trials with alow risk of bias are required to make more confident conclusions about the effects of the intervention.

Critical interpretation of Cochran's Q test depends on power and prior assumptions about heterogeneity.

We describe how an appropriate interpretation of the Q-test depends on its power to detect a given typical amount of between-study variance (τ(2)) as well as prior beliefs on heterogeneity. We illustrate these concepts in an evaluation of 1011 meta-analyses of clinical trials with ⩾4 studies and binary outcomes. These concepts can be seen as an application of the Bayes theorem. Across the 1011 meta-analyses, power to detect typical heterogeneity was low in most situations. Thus, usually a non-significant Q test did not change perceptibly prior convictions on heterogeneity. Conversely, significant results for the Q test typically augmented considerably the probability of heterogeneity. The posterior probability of heterogeneity depends on what τ(2) we want to detect. With the same approach, one may also estimate the posterior probability for the presence of heterogeneity that is large enough to annul statistically significant summary effects; that is half the average within-study variance of the combined studies; and that is able to change the summary effect estimate of the meta-analysis by 20%. The discussed analyses are exploratory, and may depend heavily on prior assumptions when power for the Q-test is low. Statistical heterogeneity in meta-analyses should be cautiously interpreted considering the power to detect a specific τ(2) and prior assumptions about the presence of heterogeneity. Copyright © 2010 John Wiley & Sons, Ltd.

Discovery properties of genome-wide association signals from cumulatively combined data sets.

Genetic effects for common variants affecting complex disease risk are subtle. Single genome-wide association (GWA) studies are typically underpowered to detect these effects, and combination of several GWA data sets is needed to enhance discovery. The authors investigated the properties of the discovery process in simulated cumulative meta-analyses of GWA study-derived signals allowing for potential genetic model misspecification and between-study heterogeneity. Variants with null effects on average (but also between-data set heterogeneity) could yield false-positive associations with seemingly homogeneous effects. Random effects had higher than appropriate false-positive rates when there were few data sets. The log-additive model had the lowest false-positive rate. Under heterogeneity, random-effects meta-analyses of 2-10 data sets averaging 1,000 cases/1,000 controls each did not increase power, or the meta-analysis was even less powerful than a single study (power desert). Upward bias in effect estimates and underestimation of between-study heterogeneity were common. Fixed-effects calculations avoided power deserts and maximized discovery of association signals at the expense of much higher false-positive rates. Therefore, random- and fixed-effects models are preferable for different purposes (fixed effects for initial screenings, random effects for generalizability applications). These results may have broader implications for the design and interpretation of large-scale multiteam collaborative studies discovering common gene variants.