mtc.hy.prior | R Documentation |
These functions generate priors for the heterogeneity parameter in mtc.model
.
Priors can be set explicitly or, for outcomes on the log odds-ratio scale, based on empirical research.
mtc.hy.prior(type, distr, ...)
mtc.hy.empirical.lor(outcome.type, comparison.type)
type |
Type of heterogeneity prior: 'std.dev', 'var', or 'prec' for standard deviation, variance, or precision respectively. |
distr |
Prior distribution name (JAGS syntax). Typical ones would be 'dunif' (uniform), 'dgamma' (Gamma), or 'dlnorm' (log-normal). Use 'dhnorm' for the half-normal. Note that, as in JAGS, the precision (and not the variance or standard deviation) is used for the normal distribution and its derivatives. |
... |
Arguments to the |
outcome.type |
The type of outcome to get an empirical prior for. Can be one of 'mortality' (all-cause mortality), 'semi-objective' (e.g. cause-specific mortality, major morbidity event, drop-outs), or 'subjective' (e.g. pain, mental health, dichotomous biomarkers). |
comparison.type |
The type of comparison to get an empirical prior for. Can be one of 'pharma-control' (pharmacological interventions versus control), 'pharma-pharma' (pharmacological versus pharmacological interventions) and 'non-pharma' (any other comparisons). |
The generated prior is a list, the structure of which may change without notice. It can be converted to JAGS compatible code using as.character
.
Empirical priors for the log odds-ratio (LOR) are taken from [Turner et al. 2012].
A value to be passed to mtc.model
.
Gert van Valkenhoef
mtc.model
# NOTE: the mtc.run commands below are for illustrative purposes, such a small
# number of iterations should obviously not be used in practice.
# set a uniform prior standard deviation
model1 <- mtc.model(smoking, hy.prior=mtc.hy.prior("std.dev", "dunif", 0, 2))
result <- mtc.run(model1, n.adapt=10, n.iter=10)
# set an empirical (log-normal) prior on the variance
model2 <- mtc.model(smoking, hy.prior=mtc.hy.empirical.lor("subjective", "non-pharma"))
result <- mtc.run(model2, n.adapt=10, n.iter=10)
# set a gamma prior on the precision
model3 <- mtc.model(smoking, hy.prior=mtc.hy.prior("prec", "dgamma", 0.01, 0.01))
result <- mtc.run(model3, n.adapt=10, n.iter=10)
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