title: An application of decision theory to patient screening for an autologous tumour vaccine trial.
abstract: Patients are eligible for accrual onto a phase I autologous tumour vaccine clinical trial if their resected and dissociated tumour achieves a minimum viable cell count. Because tumour pre-processing and cell count determination are expensive, there has been developed a screening procedure based on tumour mass to screen out those tumours unlikely to yield sufficient viable cells. If theta is the ratio of the expected benefit of an accrual onto the study to the cost of tumour pre-processing and cell counting, then we maximize long-run benefit by pre-processing and counting only those tumours whose masses exceed a cutoff mc, such that Pr(sufficient tumour cells masses = mc) = 1/theta. We derive algorithms for estimating mc and evaluate them under a variety of assumptions concerning the cell count/mass relationship. These include explicit equations for mc under parametric assumptions as well as more general algorithms based on non-parametric smoothing techniques. We show that when theta deviates substantially from 2, these methods outperform simple inverse interpolation.
authors.affiliation: Washington University School of Medicine, Division of General Medical Sciences, St. Louis, MO 63110, USA.
authors.forename: W D
authors.initials: WD
authors.lastname: Shannon
authors.forename: J
authors.initials: J
authors.lastname: Bryant
authors.forename: T F
authors.initials: TF
authors.lastname: Logan
authors.forename: R
authors.initials: R
authors.lastname: Day
mesh_terms: D000230:Adenocarcinoma / Q000276:immunology / Q000473:pathology; D000465:Algorithms; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D003662:Decision Theory; D006801:Humans; D016219:Immunotherapy, Adoptive / Q000191:economics / Q000706:statistics & numerical data; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D008545:Melanoma / Q000276:immunology / Q000473:pathology; D015999:Multivariate Analysis; D018579:Patient Selection; D018709:Statistics, Nonparametric; D014407:Tumor Cells, Cultured