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10.1177/0962280214535763 http://scihub22266oqcxt.onion/10.1177/0962280214535763 C4676739!4676739!24847900     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24847900&cmd=llinks): Failed to open stream: HTTP request failed! 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Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: development and validation |pdf=|usr=}}{{24847900}} gab.com Text Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: development and validation JO: Stat Methods Med Res http://www.kidney.de/pget.php?&tw=1&pmid=24847900 #FOAvip With the emergence of rich information on biomarkers after treatments, new types of prognostic tools are being developed: dynamic prognostic tools that can be updated at each new biomarker measurement. Such predictions are of interest in oncology where after an initial treatment patients are monitored with repeated biomarker data. However, in such setting, patients may receive second treatments to slow down the progression of the disease. This paper aims to develop and validate dynamic individual predictions that allow the possibility of a new treatment in order to help understand the benefit of initiating new treatments during the monitoring period. The prediction of the event in the next x years is done under two scenarios: (1) the patient initiates immediately a second treatment, (2) the patient does not initiate any treatment in the next x years. Predictions are derived from shared random-effect models. Applied to prostate cancer data, different specifications for the dependence between the PSA repeated measures, the initiation of a second treatment (hormonal therapy) and the risk of clinical recurrence are investigated and compared. The predictive accuracy of the dynamic predictions is evaluated with two measures (Brier score and prognostic cross-entropy) for which approximated cross-validated estimators are proposed. Twit Text FOAVip Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: development and validation ????Stat Methods Med Res http://www.kidney.de/pget.php?&tw=1&pmid=24847900 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24847900 #FOAvip English Wikipedia <ref>{{Cite pmid|24847900}}</ref> Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: development and validation #MMPMID24847900 Sène M; Taylor JMG; Dignam JJ; Jacqmin-Gadda H; Proust-Lima C Stat Methods Med Res 2016[Dec]; 25 (6): 2972-91 PMID24847900show ga With the emergence of rich information on biomarkers after treatments, new types of prognostic tools are being developed: dynamic prognostic tools that can be updated at each new biomarker measurement. Such predictions are of interest in oncology where after an initial treatment patients are monitored with repeated biomarker data. However, in such setting, patients may receive second treatments to slow down the progression of the disease. This paper aims to develop and validate dynamic individual predictions that allow the possibility of a new treatment in order to help understand the benefit of initiating new treatments during the monitoring period. The prediction of the event in the next x years is done under two scenarios: (1) the patient initiates immediately a second treatment, (2) the patient does not initiate any treatment in the next x years. Predictions are derived from shared random-effect models. Applied to prostate cancer data, different specifications for the dependence between the PSA repeated measures, the initiation of a second treatment (hormonal therapy) and the risk of clinical recurrence are investigated and compared. The predictive accuracy of the dynamic predictions is evaluated with two measures (Brier score and prognostic cross-entropy) for which approximated cross-validated estimators are proposed. äIndividualized dynamic prediction of prostate cancer recurrence with a(epmc).pdf DeepDyve Pubget Overpricing