Abstract
In medical clinical trials, overall trial results are highlighted in the abstracts of papers/reports. These results are summaries of underlying statistical analysis where most of the time normal distributions are assumed in the analysis. It is common for clinicians to focus on the information in the abstracts in order to review or integrate several clinical trial results that address the same or similar medical question(s). Therefore, developing techniques to merge results from clinical trials based on information in the abstracts is useful and important. In reality information in an abstract can either provide sufficient details about a normal distribution or just partial information about a distribution. In this paper, we first propose approaches to constructing normal distributions from both complete and incomplete statistical information in the abstracts. We then provide methods to merge these normal distributions (or sampling distributions). Following this, we investigate the conditions under which two normal distributions can be merged. Finally, we design an algorithm to sequence the merging of trials results to ensure that the most reliable trials are considered first.
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Standard probability Table: http://onlinepubs.trb.org/onlinepubs/nchrp/cd-22/v2appendixc_files/image002.gif
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Ma, J., Liu, W., Hunter, A. (2007). Incomplete Statistical Information Fusion and Its Application to Clinical Trials Data. In: Prade, H., Subrahmanian, V.S. (eds) Scalable Uncertainty Management. SUM 2007. Lecture Notes in Computer Science(), vol 4772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75410-7_7
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DOI: https://doi.org/10.1007/978-3-540-75410-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75407-7
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