TY - CHAP
T1 - PKL-Optimality Criterion in Copula Models for Efficacy-Toxicity Response
AU - Deldossi, Laura
AU - Osmetti, Silvia Angela
AU - Tommasi, C.
PY - 2016
Y1 - 2016
N2 - In recent years, there has been an increasing interest in developing dose
finding methods incorporating both efficacy and toxicity outcomes. It is reasonable
to assume that efficacy and toxicity are associated; therefore, we need to model
their stochastic dependence. Copula functions are very useful tools to model different
kinds of dependence with arbitrary marginal distributions. We consider a binary
efficacy-toxicity response with logit marginal distributions. Since the dose which
maximizes the probability of efficacy without toxicity (P-optimal dose) changes depending
on different copula functions, we propose a criterion which is useful for
choosing between the rival copula models but also protects patients against doses
that are far away from the P-optimal dose. The performance of this compromise
criterion (called PKL) is illustrated for different choices of the parameter values.
AB - In recent years, there has been an increasing interest in developing dose
finding methods incorporating both efficacy and toxicity outcomes. It is reasonable
to assume that efficacy and toxicity are associated; therefore, we need to model
their stochastic dependence. Copula functions are very useful tools to model different
kinds of dependence with arbitrary marginal distributions. We consider a binary
efficacy-toxicity response with logit marginal distributions. Since the dose which
maximizes the probability of efficacy without toxicity (P-optimal dose) changes depending
on different copula functions, we propose a criterion which is useful for
choosing between the rival copula models but also protects patients against doses
that are far away from the P-optimal dose. The performance of this compromise
criterion (called PKL) is illustrated for different choices of the parameter values.
KW - Phase I-II clinical trial
KW - bivariate logistic model
KW - Phase I-II clinical trial
KW - bivariate logistic model
UR - http://hdl.handle.net/10807/86455
U2 - 10.1007/978-3-319-31266-8_10
DO - 10.1007/978-3-319-31266-8_10
M3 - Chapter
SN - 9783319312644
T3 - CONTRIBUTIONS TO STATISTICS
SP - 79
EP - 86
BT - mODa11 - Advances in Model-Oriented Design and Analysis
A2 - Kunert, J.
A2 - Muller, C.H.
A2 - Atkinson, A.C.
ER -