Publications | Sebastian Sauer Academic

Most healthcare interventions tested in Cochrane Reviews are not effective according to high quality evidence: a systematic review and meta-analysis

Mon, 01 Aug 2022 00:00:00 +0000

Abstract

Objective: To estimate the proportion of healthcare interventions tested within Cochrane Reviews that are effective according to high-quality evidence.

Methods: We selected a random sample of 2,428 (35%) of all Cochrane Reviews published between 1 January 2008 and 5 March 2021. We extracted data about interventions within these reviews that were compared with placebo, or no treatment, and whose outcome quality was rated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We calculated the proportion of interventions whose benefits were based on high-quality evidence (defined as having high quality GRADE rating for at least one primary outcome, statistically significant positive results, and being judged by review authors as effective. We also calculated the proportion of interventions that suggested harm.

Results: Of 1,567 eligible interventions, 87 (5.6%) had high-quality evidence supporting their benefits. Harms were measured for 577 (36.8%) interventions. There was statistically significant evidence for harm in 127 (8.1%) of these. Our dependence on the reliability of Cochrane author assessments (including their GRADE assessments) was the main potential limitation of our study.

Conclusion: More than 9 in 10 healthcare interventions studied within recent Cochrane Reviews are not supported by high-quality evidence, and harms are under-reported.

Keywords: Epidemiology; Evidence; Harm; Quality; Safety; Systematic review.

The above figure does NOT appear in the final article.

Most healthcare interventions tested in Cochrane Reviews are not effective according to high quality evidence: a systematic review and meta-analysis

Mon, 17 Jun 2019 00:00:00 +0000

Abstract The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Real-time Prediction of User Performance based on Pupillary Assessment via Eye Tracking

Sat, 31 Mar 2018 00:00:00 +0000

Abstract

We propose a method to predict user performance based on eye-tracking. The method uses eye-tracking-based pupillometry to capture pupil diameter data and calculates—based on a Random Forest algorithm—user performance expectations. We conducted a large-scale experimental evaluation (125 participants aged from 21 to 61 years) and found promising results that pave the way for a dynamic real-time adaption of IT to a user’s mental effort and expected user performance. We have already achieved a good classification accuracy of user performance after only 40 seconds (5% of the mean total trial time that our participants took to complete our experiment). The non-invasive contact-free method can be applied cost-efficiently both in research and practical environments.

Keywords: NeuroIS, user performance, mental effort, pupillometry, eye-tracking, Random Forest.

This paper was awarded in 2018 with an Best Paper Award by the Journal Transactions of Human-Computer Interaction