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
The volume and diversity of data collected to support each clinical study has increased dramatically in response to the rising scope and complexity of global drug development programs. The Tufts Center for the Study of Drug Development conducted an online survey of 257 unique global companies-77% drug development sponsors and 23% contract service providers-to assess clinical data management practices and experiences. Study results indicate that companies are using an average of 6 different applications to support each clinical study and that companies are collecting a range of data types including that from case report forms, lab procedures, pharmacokinetics, biomarker, outcomes assessment, mobile health, and social media. Companies report that the primary electronic data capture (EDC) is capturing traditional data types but not many of the newer ones. Respondents report spending an average of 68.3 days to build and release a study database, 8.1 days between the patient visit and when that patient's data are entered into the EDC system, and 36.3 days on average to lock the database following the last patient last visit. Average cycle time durations are longer and more variable than those observed ten years ago. Subgroup differences (eg, by company size and company type) and factors contributing to data management cycle time and experience are discussed.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1177/2168479018769292 | DOI Listing |
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