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
Incomplete coverage by cancer registries can lead to an underreporting of cancers and a resulting bias in risk estimates. When registries are defined by geographic region, gaps in observation can arise for individuals who reside outside of or migrate from the total registry catchment area. Moreover, the exact periods of non-observation for an individual may be unknown due to intermittent reporting of residential histories. The motivating example for this work is the U.S. Radiologic Technologist (USRT) study which ascertained cancer outcomes for a national cohort through 43 state/regional registries; similar gaps in outcome ascertainment can appear in other registry or electronic health record- based cohort studies. We propose a two-step procedure for estimating relative and absolute risk in these settings. First, using a mover stayer model fitted to individuals' known residential history, we obtain individual posterior probabilities of residing outside the registry catchment area each year. Second, we incorporate these probabilities in the survival data likelihood for competing risks to account for unobserved events. We assess the performance of the proposed method in extensive simulation studies. Compared to several simple alternative approaches, the proposed method reduces bias and improves efficiency. Finally, we apply the proposed method to a study of first primary lung cancers in the USRT cohort.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1002/sim.9668 | DOI Listing |
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