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
A dose-finding study with an adaptive design generates three computational problems: fitting the dose-response curve given the current data, identifying the dose to be given to the next patient that is optimal for learning about the dose-response curve, and pretrial simulation in order to establish operating characteristics of alternative designs. Identifying the 'optimal' dose is the rate-limiting step since conventional methods, estimating the full posterior predictive distribution of some utility function under each of the possible doses, are very slow. We explore a simpler strategy based on importance sampling, whereby the posterior mean of the utility at each candidate dose is estimated by taking its average across an empirical distribution for the model parameters from the current Markov chain Monte Carlo (MCMC) run, weighted according to the likelihood of one or more predicted observations. We identify appropriate settings for this algorithm and illustrate its application in the context of a normal dynamic linear model used in a dose-finding clinical trial of a neutrophil inhibitory factor in acute ischaemic stroke.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1080/10543400701643947 | DOI Listing |
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