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
Objectives: Three definitions of confounding are available in the epidemiologic literature, namely, the classical, collapsibility, and counterfactual. The classical and collapsibility definitions are intuitively appealing but, especially in the case of the latter, there are shortcomings. The more recent counterfactual definition overcomes these limitations but at the cost of increased abstraction. One of the aims of this article is to demonstrate that under certain conditions the three definitions of confounding have key features in common.
Conclusions: The counterfactual definition of confounding addresses the inherent shortcomings of the classical and collapsibility definitions, and forms the basis of innovative methods of data analysis.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1016/j.jclinepi.2003.07.014 | DOI Listing |
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