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A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analysis can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. Existing methods for meta-analysis yield a weighted average from the results of the individual studies, and what differs is the manner in which these weights are allocated and also the manner in which the uncertainty is computed around the point estimate thus generated. In addition to providing an estimate of the unknown common truth, meta-analysis has the capacity to contrast results from different studies and identify patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light in the context of multiple studies.
Meta analysis of RCT outcome indicator structure analysis and computable input method and system
This invention discloses a method and system for analyzing and computable input of outcome indicators in meta-analysis RCTs, relating to the fields of medical statistics and literature data processing. The method first organizes a set of statistical elements with outcome indicators, detection time points, and comparison relationships as their scopes; then, it abstracts the meta-analysis formula into statistical element slot templates and performs slot matching within the outcome scope; when a target slot is missing, it is filled in based on preset substitution rules; after computability verification, a standardized meta-analysis input structure is generated. This invention solves the problems of existing technologies where meta-analysis data preparation is highly dependent on manual processes, computability verification is difficult to automate, and missing elements cannot be automatically filled in. It transforms manual judgment and calculation into a reusable structured process, significantly improving the efficiency, consistency, and data utilization of large-scale RCT outcome data processing. The output standardized data can be directly used by existing meta-analysis software.
Owner:JIEHELIX (SHANGHAI) MEDICAL TECH CO LTD +1