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Data from 23 respondents each choosing preference from 18 choice cards. Choice cards were randomly generated from 108 total choices. For details see article by Mukhopadhyay, S., et al. "Hierarchical Bayesian Benefit-Risk Modeling and Assessment Using Choice Based Conjoint." Statistics in Biopharmaceutical Research 11.1 (2019): 52-60.
A list consisting of pilot response data from 23 experts and design information to be used to fit the HBBR model
A data frame with 23x18 = 414 rows and 15 columns consists of responses from 23 experts each providing tradeoff responses to 18 choice pairs. The 1st column consists of responders' id. The 2nd column contains binary responses (1 indicating 1st of the choice pair was selected, 0 indicating 2nd was selected). Remaining 13 columns contain the design matrix X taking values 0, 1, or -1; a value of 1 or -1 is used to indicate presence of an attribute level in the 1st choice or in the 2nd choice of the choice pair, respectively; a value of 0 is used to indicate absence of an attribute in the choice pair. See Details below for more about the discrete choice experiment that is coded as design matrix X.
A list of structure (b, r, bl, rl), where b and r indicate number of benefit and risk attributes, bl is a vector of integers of size b consisting number of levels within each benefit attribute; similarly rl is a vector of integers of size r consisting number of levels within each risk attribute.
The discrete choice experiment (DCE) included 3 benefit attributes (b=3): overall survival (OS), objective response rate (ORR), fatigue reduction (FTG); and 2 risk attributes (r=2): febrile neutropenia (FebNEU) and severe pneumonia (SevPNA). There were 4 levels for each of the benefit attributes (ORR, OS, and FTG) (i.e. bl= rep(4,3)) and 3 levels for each of the 2 risk attributes (FebNEU and SevPNA) (i.e. rl = rep(3,2)). The DCE produced b*r*(4 choose 2)*(3 choose 2) = 108 distinct non-dominant choice pairs each with one benefit and one risk attribute. Panels (questionnaires) were generated with 18 randomly selected choice pairs per panel from the set of 108 choice pairs. Since the part-worth of various levels within each attribute are to be measured relatively to the part-worth of the 1st level of the attribute, columns for the 1st level of the attributes are not required. Thus, we have sum(bl)-b + sum(br)-r = 13 columns are needed to obtain information on the X matrix which are stored as the last 13 columns of brdta.
Mukhopadhyay, S. et al. "Hierarchical Bayesian Benefit–Risk Modeling and Assessment Using Choice Based Conjoint." Statistics in Biopharmaceutical Research 11.1 (2019): 52-60.
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