How many tumor indications should be initially screened in development of next generation immunotherapies?
An experimental oncology immunotherapy may have the potential to be effective in a large number of tumor indications. We have considered a staggered approach for efficacy screening in subjects with an unmet medical need. A cohort of tumor indications is selected for the first wave investigation and the second wave investigation in a different cohort of tumor indications is initiated only after the drug has been demonstrated to be effective in the first wave. The effectiveness of an experimental immunotherapy is unknown at the planning stage, and the assumptions at the planning stage are subject to revision later on. How many tumor indications should be investigated in the first wave for the development program to be cost-effective amid the uncertainties? We attempt to answer this question by maximizing a benefit-cost ratio, defined to be the expected number of effective tumor indications correctly identified in the two waves (benefit) divided by the expected total sample size in the two waves and the subsequent trials for more definitive testing triggered by those with a positive outcome in the first wave (cost). It is found from the benefit-cost ratio analyses that, depending on resource availability, three to five tumor indications may be initiated in the first wave to properly balance the risk and benefit, and adequate investment is important to maintain the quality of statistical design.
Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA. Electronic address: cong_chen@merck.com.
Cong
C
Chen
Biostatistics and Data Science, Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Rd, Ridgefield, CT 06811, USA.
Qiqi
Q
Deng
Department of Population Health, New York University, 650 First Avenue, New York, NY 10016, USA.
Linchen
L
He
Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA.
Devan V
DV
Mehrotra
Oncology Early Development, Merck & Co., Inc., Kenilworth, NJ 07033, USA.
Eric H
EH
Rubin
Department of Oncology, Lombardi Comprehensive Cancer Center, Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, Suite 110, Washington, DC 20007, USA; Department of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center, Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, Suite 110, Washington, DC 20007, USA.
Robert A
RA
Beckman
D003362:Cost-Benefit Analysis; D020000:Decision Support Systems, Clinical; D055088:Early Detection of Cancer / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data; D015276:Health Care Rationing; D006801:Humans; D007167:Immunotherapy / Q000009:adverse effects / Q000379:methods; D009369:Neoplasms / Q000175:diagnosis / Q000628:therapy; D018579:Patient Selection; D018570:Risk Assessment
Decision analysis; Two-stage design; Type III error