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Predicting enrollment performance of investigational centers in phase III multi-center clinical trials
Failure to meet subject recruitment targets in clinical trials continues to be a widespread problem with potentially serious scientific, logistical, financial and ethical consequences. On the operational level, enrollment-related issues may be mitigated by careful site selection and by allocating mo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5898520/ https://www.ncbi.nlm.nih.gov/pubmed/29696188 http://dx.doi.org/10.1016/j.conctc.2017.07.004 |
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author | van den Bor, Rutger M. Grobbee, Diederick E. Oosterman, Bas J. Vaessen, Petrus W.J. Roes, Kit C.B. |
author_facet | van den Bor, Rutger M. Grobbee, Diederick E. Oosterman, Bas J. Vaessen, Petrus W.J. Roes, Kit C.B. |
author_sort | van den Bor, Rutger M. |
collection | PubMed |
description | Failure to meet subject recruitment targets in clinical trials continues to be a widespread problem with potentially serious scientific, logistical, financial and ethical consequences. On the operational level, enrollment-related issues may be mitigated by careful site selection and by allocating monitoring or training resources proportionally to the anticipated risk of poor enrollment. Such procedures require estimates of the expected recruitment performance that are sufficiently reliable to allow centers to be sensibly categorized. In this study, we investigate whether information obtained from feasibility questionnaires can potentially be used to predict which centers will and which centers will not meet their enrollment targets by means of multivariable logistic regression analysis. From a large set of 59 candidate predictors, we determined the subset that is optimal for predictive purposes using Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Although the extent to which the results are generalizable remains to be determined, they indicate that the prediction accuracy of the optimal model is only a marginal improvement over the intercept-only model, illustrating the difficulty of prediction in this setting. |
format | Online Article Text |
id | pubmed-5898520 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-58985202018-04-25 Predicting enrollment performance of investigational centers in phase III multi-center clinical trials van den Bor, Rutger M. Grobbee, Diederick E. Oosterman, Bas J. Vaessen, Petrus W.J. Roes, Kit C.B. Contemp Clin Trials Commun Article Failure to meet subject recruitment targets in clinical trials continues to be a widespread problem with potentially serious scientific, logistical, financial and ethical consequences. On the operational level, enrollment-related issues may be mitigated by careful site selection and by allocating monitoring or training resources proportionally to the anticipated risk of poor enrollment. Such procedures require estimates of the expected recruitment performance that are sufficiently reliable to allow centers to be sensibly categorized. In this study, we investigate whether information obtained from feasibility questionnaires can potentially be used to predict which centers will and which centers will not meet their enrollment targets by means of multivariable logistic regression analysis. From a large set of 59 candidate predictors, we determined the subset that is optimal for predictive purposes using Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Although the extent to which the results are generalizable remains to be determined, they indicate that the prediction accuracy of the optimal model is only a marginal improvement over the intercept-only model, illustrating the difficulty of prediction in this setting. Elsevier 2017-07-20 /pmc/articles/PMC5898520/ /pubmed/29696188 http://dx.doi.org/10.1016/j.conctc.2017.07.004 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article van den Bor, Rutger M. Grobbee, Diederick E. Oosterman, Bas J. Vaessen, Petrus W.J. Roes, Kit C.B. Predicting enrollment performance of investigational centers in phase III multi-center clinical trials |
title | Predicting enrollment performance of investigational centers in phase III multi-center clinical trials |
title_full | Predicting enrollment performance of investigational centers in phase III multi-center clinical trials |
title_fullStr | Predicting enrollment performance of investigational centers in phase III multi-center clinical trials |
title_full_unstemmed | Predicting enrollment performance of investigational centers in phase III multi-center clinical trials |
title_short | Predicting enrollment performance of investigational centers in phase III multi-center clinical trials |
title_sort | predicting enrollment performance of investigational centers in phase iii multi-center clinical trials |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5898520/ https://www.ncbi.nlm.nih.gov/pubmed/29696188 http://dx.doi.org/10.1016/j.conctc.2017.07.004 |
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