Statistical Analysis Plan for the Optimal Post Rtpa-Iv Monitoring in Ischemic Stroke Trial (OPTIMIST)
crossref(2024)
The George Institute for Global Health
Abstract
The OPTIMIST trial aims to determine whether low-intensity monitoring is at least as effective ("non-inferior") to standard monitoring, on the functional recovery of patients who have received recombinant tissue plasminogen activator or equivalent lytic reperfusion treatment for acute ischaemic stroke. It is designed as an international, multicenter, stepped-wedge (4 periods/3 steps) cluster randomized trial. This statistical analysis plan pre-specifies the method of analysis for every outcome and key variable collected in the trial. The primary outcome is an "unfavourable outcome" at Day 90, defined as a score of 2 to 6 on the modified Rankin scale. The primary analysis will consist in a log-binomial regression adjusted for the effect of time and for clustering by site using a random effect. The non-inferiority margin was pre-specified as a relative risk of 1.15 for a bad outcome; thus, non-inferiority will be declared if the upper bound of the 95% confidence interval around the relative risk is lower than 1.15. The primary analysis will adjust for calendar time (6-month intervals) and will be based on imputed data. The analysis plan also includes planned sensitivity analyses including covariate adjustments and subgroup analyses.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined