Designing a phase‐III time‐to‐event clinical trial using a modified sample size formula and Poisson‐Gamma model for subject accrual that accounts for the lag in site initiation using the PERT distribution

A priori estimation of sample size and subject accrual in multi‐site, time‐to‐event clinical trials is often challenging. Such trials are powered based on the number of events needed to detect a clinically significant difference. Sample size based on number of events relates to the expected duration...

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Published inStatistics in medicine Vol. 42; no. 30; pp. 5694 - 5707
Main Authors Shipes, Virginia B., Meinzer, Caitlyn, Wolf, Bethany J., Li, Hong, Carpenter, Mathew J., Kamel, Hooman, Martin, Renee H.
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 30.12.2023
Wiley Subscription Services, Inc
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ISSN0277-6715
1097-0258
1097-0258
DOI10.1002/sim.9935

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Abstract A priori estimation of sample size and subject accrual in multi‐site, time‐to‐event clinical trials is often challenging. Such trials are powered based on the number of events needed to detect a clinically significant difference. Sample size based on number of events relates to the expected duration of observation time for each subject. Temporal patterns in site initiation and subject enrollment ultimately affect when subjects can be accrued into the study. Lag times are common as the site start‐up process optimizes, resulting in delays that may curtail observational follow‐up and therefore undermine power. The proposed method introduces a Program Evaluation and Review Technique (PERT) model into the sample size estimation which accounts for the lag in site start‐up. Additionally, a PERT model is introduced into a Poisson‐Gamma subject accrual model to predict the quantity of study sites needed. The introduction of the PERT model provides greater flexibility in both a priori power assessment and planning the number of sites, as it specifically allows for the inclusion of anticipated delays in site start‐up time. This model results in minimal power loss even when PERT distribution inputs are misspecified compared to the traditional assumption of simultaneous start‐up for all sites. Together these updated formulations for sample size and subject accrual models offer an improved method for designing a multi‐site time‐to‐event clinical trial that accounts for a flexible site start‐up process.
AbstractList A priori estimation of sample size and subject accrual in multi-site, time-to-event clinical trials is often challenging. Such trials are powered based on the number of events needed to detect a clinically significant difference. Sample size based on number of events relates to the expected duration of observation time for each subject. Temporal patterns in site initiation and subject enrollment ultimately affect when subjects can be accrued into the study. Lag times are common as the site start-up process optimizes, resulting in delays that may curtail observational follow-up and therefore undermine power. The proposed method introduces a Program Evaluation and Review Technique (PERT) model into the sample size estimation which accounts for the lag in site start-up. Additionally, a PERT model is introduced into a Poisson-Gamma subject accrual model to predict the quantity of study sites needed. The introduction of the PERT model provides greater flexibility in both a priori power assessment and planning the number of sites, as it specifically allows for the inclusion of anticipated delays in site start-up time. This model results in minimal power loss even when PERT distribution inputs are misspecified compared to the traditional assumption of simultaneous start-up for all sites. Together these updated formulations for sample size and subject accrual models offer an improved method for designing a multi-site time-to-event clinical trial that accounts for a flexible site start-up process.A priori estimation of sample size and subject accrual in multi-site, time-to-event clinical trials is often challenging. Such trials are powered based on the number of events needed to detect a clinically significant difference. Sample size based on number of events relates to the expected duration of observation time for each subject. Temporal patterns in site initiation and subject enrollment ultimately affect when subjects can be accrued into the study. Lag times are common as the site start-up process optimizes, resulting in delays that may curtail observational follow-up and therefore undermine power. The proposed method introduces a Program Evaluation and Review Technique (PERT) model into the sample size estimation which accounts for the lag in site start-up. Additionally, a PERT model is introduced into a Poisson-Gamma subject accrual model to predict the quantity of study sites needed. The introduction of the PERT model provides greater flexibility in both a priori power assessment and planning the number of sites, as it specifically allows for the inclusion of anticipated delays in site start-up time. This model results in minimal power loss even when PERT distribution inputs are misspecified compared to the traditional assumption of simultaneous start-up for all sites. Together these updated formulations for sample size and subject accrual models offer an improved method for designing a multi-site time-to-event clinical trial that accounts for a flexible site start-up process.
A priori estimation of sample size and subject accrual in multi-site, time-to-event clinical trials is often challenging. Such trials are powered based on the number of events needed to detect a clinically significant difference. Sample size based on number of events relates to the expected duration of observation time for each subject. Temporal patterns in site initiation and subject enrollment ultimately affect when subjects can be accrued into the study. Lag times are common as the site start-up process optimizes, resulting in delays that may curtail observational follow-up and therefore undermine power. The proposed method introduces a Program Evaluation and Review Technique (PERT) model into the sample size estimation which accounts for the lag in site start-up. Additionally, a PERT model is introduced into a Poisson-Gamma subject accrual model to predict the quantity of study sites needed. The introduction of the PERT model provides greater flexibility in both a priori power assessment and planning the number of sites, as it specifically allows for the inclusion of anticipated delays in site start-up time. This model results in minimal power loss even when PERT distribution inputs are misspecified compared to the traditional assumption of simultaneous start-up for all sites. Together these updated formulations for sample size and subject accrual models offer an improved method for designing a multi-site time-to-event clinical trial that accounts for a flexible site start-up process.
A priori estimation of sample size and subject accrual in multi-site, time-to-event clinical trials is often challenging. Such trials are powered based on the number of events needed to detect a clinically significant difference. Sample size based on number of events relates to the expected duration of observation time for each subject. Temporal patterns in site initiation and subject enrollment ultimately affect when subjects can be accrued into the study. Lag times are common as the site start-up process optimizes, resulting in delays that may curtail observational follow-up and therefore undermine power. The proposed method introduces a Program Evaluation and Review Technique (PERT) model into the sample size estimation which accounts for the lag in site start-up. Additionally, a PERT model is introduced into a Poisson-Gamma subject accrual model to predict the quantity of study sites needed. The introduction of the PERT model provides greater flexibility in both a priori power assessment and planning the number of sites, as it specifically allows for the inclusion of anticipated delays in site start-up time. This model results in minimal power loss even when PERT distribution inputs are misspecified compared to the traditional assumption of simultaneous start-up for all sites. Together these updated formulations for sample size and subject accrual models offer an improved method for designing a multi-site time-to-event clinical trial that accounts for a flexible site start-up process.
Author Shipes, Virginia B.
Meinzer, Caitlyn
Wolf, Bethany J.
Li, Hong
Kamel, Hooman
Martin, Renee H.
Carpenter, Mathew J.
AuthorAffiliation 1 Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
3 Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, United States
2 The Emmes Company, Rockville, Maryland, United States
5 Department of Neurology, Weill Cornell Medicine, New York, NY, United States
4 Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, United States
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sample size
subject accrual models
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Snippet A priori estimation of sample size and subject accrual in multi‐site, time‐to‐event clinical trials is often challenging. Such trials are powered based on the...
A priori estimation of sample size and subject accrual in multi-site, time-to-event clinical trials is often challenging. Such trials are powered based on the...
A priori estimation of sample size and subject accrual in multi-site, time-to-event clinical trials is often challenging. Such trials are powered based on the...
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Publisher
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SubjectTerms Clinical trials
Humans
PERT
Program Evaluation
Sample Size
subject accrual models
Time Factors
time‐to‐event
Title Designing a phase‐III time‐to‐event clinical trial using a modified sample size formula and Poisson‐Gamma model for subject accrual that accounts for the lag in site initiation using the PERT distribution
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fsim.9935
https://www.ncbi.nlm.nih.gov/pubmed/37926516
https://www.proquest.com/docview/2901981641
https://www.proquest.com/docview/2886598416
https://pubmed.ncbi.nlm.nih.gov/PMC10847961
Volume 42
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