Browsing by Author "Mickenautsch, Steffen"

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    Extension of the composite quality score (cqs) as an appraisal tool for prospective, controlled clinical therapy trials–A systematic review of meta-epidemiological evidence
    (Public Library of Science, 2022) Mickenautsch, Steffen; Rupf, Stefan; Miletić, Ivana
    To conduct a survey of current meta-epidemiological studies to identify additional trial design characteristics that may be associated with significant over- or underestimation of the treatment effect and to use such identified characteristics as a basis for the formulation of new CQS appraisal criteria. We retrieved eligible studies from two systematic reviews on this topic (latest search May 2015) and searched the databases PubMed and Embase for further studies from June 2015 –March 2022. All data were extracted by one author and verified by another. Sufficiently homogeneous estimates from single studies were pooled using random-effects meta-analysis. Trial design characteristics associated with statistically significant estimates from single datasets (which could not be pooled) and meta-analyses were used as a basis to formulate new or amend existing CQS criteria.
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    Risk of selection bias assessment in the NINDS rt‑PA stroke study
    (BMC, 2022) Garg, Ravi; Mickenautsch, Steffen
    The NINDS rt-PA Stroke Study is frequently cited in support of alteplase for acute ischemic stroke within 3 h of symptom onset. Multiple post-hoc reanalyses of this trial have been published to adjust for a baseline imbalance in stroke severity. We performed a risk of selection bias assessment and reanalyzed trial data to determine if the etiology of this baseline imbalance was more likely due to random chance or randomization errors. A risk of selection bias assessment was conducted using signaling questions from the Cochrane Risk of Bias 2 (ROB 2) tool. Four sensitivity analyses were conducted on the trial data based on the randomization process: assessment of imbalances in allocation in unique strata; adherence to a pre-specified restriction on randomization between time strata at each randomization center; assessment of differences in baseline computed tomography (CT) results in unique strata; and comparison of baseline characteristics between allocation groups within each time strata. A multivariable logistic regression model was used to compare reported treatment effects with revised treatment effects after adjustment of baseline imbalances identified in the sensitivity analyses.