A Bayesian stopping rule for a single arm study: With a case study of stem cell transplantation.
Continuous monitoring of treatment failures is an important issue in clinical studies of a single experimental treatment for high risk therapy such as hematopoietic stem cell transplantation. The sequential probability ratio test (SPRT) of Wald in 1947 and various alternative stopping rules have been proposed for sequential monitoring of adverse events. It is natural to use prior information to improve stopping rules and statistical analysis. A Bayesian stopping rule is developed and applied to an example of an umbilical cord blood transplant study performed at the University of Minnesota. Two strata, based on the number of nucleated cells per kg recipient body weight (the 'dose') are monitored separately and different rules are constructed for each stratum using different prior distributions. It is believed that patients in the lower dose group have a greater chance of graft failure than those in the higher dose group. A program, written in R, is also presented for calculating the stopping rule using the prior beliefs. The program is an improvement upon existing programs and it can be used for larger studies.
Chen
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C
School of Statistics, University of Minnesota, Minneapolis, MN, USA. chaoc@amgen.com
Chaloner
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K
D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D036101:Cord Blood Stem Cell Transplantation / Q000009:adverse effects; D006801:Humans; D017211:Treatment Failure