Prediction of accrual closure date in multi-center clinical trials with discrete-time Poisson process models

Gong Tang, Yuan Kong, Chung Chou Ho Chang, Lan Kong, Joseph P. Costantino

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In a phase III multi-center cancer clinical trial or a large public health study, sample size is predetermined to achieve desired power, and study participants are enrolled from tens or hundreds of participating institutions. As the accrual is closing to the target size, the coordinating data center needs to project the accrual closure date on the basis of the observed accrual pattern and notify the participating sites several weeks in advance. In the past, projections were simply based on some crude assessment, and conservative measures were incorporated in order to achieve the target accrual size. This approach often resulted in excessive accrual size and subsequently unnecessary financial burden on the study sponsors. Here we proposed a discrete-time Poisson process-based method to estimate the accrual rate at time of projection and subsequently the trial closure date. To ensure that target size would be reached with high confidence, we also proposed a conservative method for the closure date projection. The proposed method was illustrated through the analysis of the accrual data of the National Surgical Adjuvant Breast and Bowel Project trial B-38. The results showed that application of the proposed method could help to save considerable amount of expenditure in patient management without compromising the accrual goal in multi-center clinical trials.

Original languageEnglish (US)
Pages (from-to)351-356
Number of pages6
JournalPharmaceutical Statistics
Volume11
Issue number5
DOIs
StatePublished - Sep 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

Fingerprint Dive into the research topics of 'Prediction of accrual closure date in multi-center clinical trials with discrete-time Poisson process models'. Together they form a unique fingerprint.

Cite this