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: 3122
Function: getPubMedXML
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
Statistical methods have been well-developed for comparing two binary screening tests in the presence of verification bias. However, the complexity of existing methods and the computational difficulty in implementing them have restricted their use. A simple and easily implemented statistical method is therefore needed. In this paper, we propose a weighted McNemar's test statistic for comparing two sensitivities(specificities). The proposed test statistics are intuitive and simple to compute, only involving some minor modification of a McNemar's test statistic using the estimated verification probabilities for discordant pairs. Simulations demonstrate that the proposed weighted McNemar's test statistics preserve type I error as well as or better than the existing statistics. Furthermore, unlike the existing methods, the proposed weighted McNemar's test statistics can still be applied even when none of the accordant pairs are verified.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1002/sim.9409 | DOI Listing |
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