Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Determining the probability of success of a clinical trial using a prior distribution on the treatment effect can significantly enhance decision-making by the sponsor. In a group sequential design, the probability of success calculated at the design stage can be updated to incorporate the information disclosed by the Data Monitoring Committee (DMC), usually consisting in a simple statement that advises to continue or to stop the trial, either for efficacy or futility, following pre-specified rules defined in the protocol. We define the "probability of success post interim" as the probability of success conditioned on the assumption that the DMC recommends continuing the trial after an interim analysis. A good assessment of this probability helps mitigate the tendency of the study team to express excessive optimism or unwarranted pessimism regarding the trial's ultimate outcome after the DMC recommendation. We explore the relationship between this "probability of success post interim" and the initial probability of success, and we provide an in-depth investigation of how interim boundaries impact these probabilities. This analysis offers valuable insights that can guide the selection of boundaries for both efficacy and futility interim analyses, leading to more informed clinical trial designs.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1080/10543406.2024.2430308 | DOI Listing |
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