The name is absent



19

where,

G

ri~N(O,σf), ξij ~∑PjgN(μjg,σf)             (2.4)

9=1

and

(Pji,Pj2, ∙ ∙ ,PjG) ~ Dirichlet(ajι,aj2, ,ajc), j = l,...,J.       (2.5)

Here, βj parameterize the ordinal probit model for zij. Notice that βj does not
include an intercept parameter. An intercept is already implicitly included in μj9.
Consider the implied model for each
vij after marginalizing with respect to ri. The
marginal distribution for each uʊ, j = 1,..., J, is a mixture of normal distributions
sharing the scale parameter
{Jσ'r ÷ σf)> with distinct location parameters (x'iβj +
μjg),
fixed number of components (G) and weights (p99). It can be shown that without
loss of generality we can fix the cutpoints
θjk when working with the mixture of normal
model in (2.4) instead of a single normal. See also the discussion below. While we
assume that
G is fixed, for j = 1,..., J, in (2.4), using different Gj for each toxicity
is possible without additional complications.

The mixture model can alternatively be written as a hierarchical model by intro-
ducing a latent indicator variable
wij. Specifically, conditional on wij = g, βj and
ri, the latent variable t⅛∙ follows a normal distribution: ¾∙ ∣ wij = g,βτl3g ~
N(χTβj + Ti + μjg,
σξ). The prior probability for u⅛ = g is Pr(wij = g) = pjg. Let
Φ(∙) denote the standard normal cdf. Marginalizing with respect to both
ri and the
latent variable ¾∙, we have:

Pr(zij = к I wij = g, βjj9) = Φ f gfe+^'~ ¾ ~     - Φ ( ⅛      ~

]σrl J ]σr+σl

(2.6)

Pr(wij =g) =pjg.                          (2.7)

For each category j the probability of a response at level к is

G

πjk ≡ Pr{zij = k) =    -pr(¾ = k I wij = 9)Pjg             (2-8)

9=1



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