#### Oxford: smooth fit to log-odds ratios

Breslow and Clayton (1993) re-analyse 2 by 2 tables of cases (deaths from childhood cancer) and controls tabulated against maternal exposure to X-rays, one table for each of 120 combinations of age (0-9) and birth year (1944-1964). The data may be arranged to the following form.

Their most complex model is equivalent to expressing the log(odds-ratio) ψi for the table in stratum i as

logψi = α + β1yeari + β2(yeari2 - 22) + bi

bi ~ Normal(0, τ)
They use a quasi-likelihood approximation of the full hypergeometric likelihood obtained by conditioning on the margins of the tables.

We let r0i denote number of exposures among the n0i controls in stratum i, and r1i denote number of exposures for the n1i cases. The we assume

r0i ~ Binomial(p0i, n0i)

r1i ~ Binomial(p1i, n1i)

logit(p0i) = μi

logit(p1i) = μi + logψi

Assuming this model with independent vague priors for the μi's provides the correct conditional likelihood. The appropriate graph is shown below

BUGS language for Oxford example:
model
{
for (i in 1 : K) {
r0[i] ~ dbin(p0[i], n0[i])
r1[i] ~ dbin(p1[i], n1[i])
logit(p0[i]) <- mu[i]
logit(p1[i]) <- mu[i] + logPsi[i]
logPsi[i] <- alpha + beta1 * year[i] + beta2 * (year[i] * year[i] - 22) + b[i]
b[i] ~ dnorm(0, tau)
mu[i] ~ dnorm(0.0, 1.0E-6)
}
alpha ~ dnorm(0.0, 1.0E-6)
beta1 ~ dnorm(0.0, 1.0E-6)
beta2 ~ dnorm(0.0, 1.0E-6)
tau ~ dgamma(1.0E-3, 1.0E-3)
sigma <- 1 / sqrt(tau)
}
##### Data
``` list(r1 = c(3, 5, 2, 7, 7, 2, 5, 3, 5, 11, 6, 6, 11, 4, 4, 2, 8, 8, 6, 5, 15, 4, 9, 9, 4, 12, 8, 8,   6, 8, 12, 4, 7, 16, 12, 9, 4, 7, 8, 11, 5, 12, 8, 17, 9, 3, 2, 7, 6, 5, 11, 14, 13, 8,          6, 4, 8, 4, 8, 7, 15, 15, 9, 9, 5, 6, 3, 9, 12, 14, 16, 17, 8, 8, 9, 5, 9, 11, 6, 14,          21, 16, 6, 9, 8, 9, 8, 4, 11, 11, 6, 9, 4, 4, 9, 9, 10, 14, 6, 3, 4, 6, 10, 4, 3, 3,          10, 4, 10, 5, 4, 3, 13, 1, 7, 5, 7, 6, 3, 7),       n1 = c(28, 21, 32, 35, 35, 38, 30, 43, 49, 53, 31, 35, 46, 53, 61, 40, 29, 44, 52, 55, 61, 31, 48, 44, 42, 53, 56, 71,      43, 43, 43, 40, 44, 70, 75, 71, 37, 31, 42, 46, 47, 55, 63, 91, 43, 39, 35, 32, 53, 49, 75, 64, 69, 64,       49, 29, 40, 27, 48, 43, 61, 77, 55, 60, 46, 28, 33, 32, 46, 57, 56, 78, 58, 52, 31, 28, 46,42 , 45, 63,             71, 69, 43, 50, 31, 34, 54, 46, 58, 62, 52, 41, 34, 52, 63, 59, 88, 62, 47, 53, 57, 74, 68, 61, 45, 45,             62, 73, 53, 39, 45, 51, 55, 41, 53, 51, 42, 46, 54, 32),       r0 = c(0, 2, 2, 1, 2, 0, 1, 1, 1, 2, 4, 4, 2, 1, 7, 4, 3, 5, 3, 2, 4, 1, 4, 5, 2, 7, 5, 8,      2, 3, 5, 4, 1, 6, 5, 11, 5, 2, 5, 8, 5, 6, 6, 10, 7, 5, 5, 2, 8, 1, 13, 9, 11, 9,          4, 4, 8, 6, 8, 6, 8, 14, 6, 5, 5, 2, 4, 2, 9, 5, 6, 7, 5, 10, 3, 2, 1, 7, 9, 13,             9, 11, 4, 8, 2, 3, 7, 4, 7, 5, 6, 6, 5, 6, 9, 7, 7, 7, 4, 2, 3, 4, 10, 3, 4, 2,             10, 5, 4, 5, 4, 6, 5, 3, 2, 2, 4, 6, 4, 1),       n0 = c(28, 21, 32, 35, 35, 38, 30, 43, 49, 53, 31, 35, 46, 53, 61, 40, 29, 44, 52, 55, 61, 31, 48, 44, 42, 53, 56, 71,      43, 43, 43, 40, 44, 70, 75, 71, 37, 31, 42, 46, 47, 55, 63, 91, 43, 39, 35, 32, 53, 49, 75, 64, 69, 64,             49, 29, 40, 27, 48, 43, 61, 77, 55, 60, 46, 28, 33, 32, 46, 57, 56, 78, 58, 52, 31, 28, 46, 42, 45, 63,             71, 69, 43, 50, 31, 34, 54, 46, 58, 62, 52, 41, 34, 52, 63, 59, 88, 62, 47, 53, 57, 74, 68, 61, 45, 45,             62, 73, 53, 39, 45, 51, 55, 41, 53, 51, 42, 46, 54, 32),      year = c(-10, -9, -9, -8, -8, -8, -7, -7, -7, -7, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4,      -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1,         -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2,                2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6,               6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 9, 9, 10), K = 120) ```
##### Inits for chain 1
``` list(alpha=0,beta1=0,beta2=0,tau=1,mu=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),b=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)) ```

##### Inits for chain 2
``` list(alpha=1.0,beta1=1.0,beta2=1.0,tau=0.1,mu=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),b=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)) ```
Results

These estimates compare well with Breslow and Clayton (1993) PQL estimates of α = 0.566 +/- 0.070, β1 = -0.469 +/- 0.0167, β2 = 0.0071 +/- 0.0033, σ = 0.15 +/- 0.10.