A variant of
disbayes in which data from different areas can be
related in a hierarchical model and, optionally, the effect of gender can be
treated as additive with the effect of area. This is much more computationally
intensive than the basic model in
disbayes. Time trends are not
supported in this function.
disbayes_hier( data, group, gender = NULL, inc_num = NULL, inc_denom = NULL, inc_prob = NULL, inc_lower = NULL, inc_upper = NULL, prev_num = NULL, prev_denom = NULL, prev_prob = NULL, prev_lower = NULL, prev_upper = NULL, mort_num = NULL, mort_denom = NULL, mort_prob = NULL, mort_lower = NULL, mort_upper = NULL, rem_num = NULL, rem_denom = NULL, rem_prob = NULL, rem_lower = NULL, rem_upper = NULL, age = "age", cf_init = 0.01, eqage = 30, eqagehi = NULL, cf_model = "default", inc_model = "smooth", rem_model = "const", prev_zero = FALSE, sprior = c(1, 1, 1), hp_fixed = NULL, nfold_int_guess = 5, nfold_int_upper = 100, nfold_slope_guess = 5, nfold_slope_upper = 100, mean_int_prior = c(0, 10), mean_slope_prior = c(5, 5), gender_int_priorsd = 0.82, gender_slope_priorsd = 0.82, inc_prior = c(1.1, 0.1), rem_prior = c(1.1, 1), method = "opt", draws = 1000, iter = 10000, stan_control = NULL, ... )
Data frame containing some of the variables below. The variables below are provided as character strings naming columns in this data frame. For each disease measure available, one of the following three combinations of variables must be specified:
(1) numerator and denominator (2) estimate and denominator (3) estimate with lower and upper credible limits.
Mortality must be supplied, and at least one of incidence and prevalence. If remission is assumed to be possible, then remission data should also be supplied (see below).
Estimates refer to the probability of having some event within a year, rather than rates. Rates per year $r$ can be converted to probabilities $p$ as $p = 1 - exp(-r)$, assuming the rate is constant within the year.
For estimates based on registry data assumed to cover the whole population, then the denominator will be the population size.
Variable in the data representing the area (or other grouping factor).
NULL (the default) then the data are one homogenous
gender, and there should be one row per year of age. Otherwise, set
gender to a character string naming the variable in the data
representing gender (or other categorical grouping factor). Gender will then
treated as a fixed additive effect, so the linear effect of gender on log
case fatality is the same in each area. The data should have one row per
year of age and gender.
Numerator for the incidence data, assumed to represent the
observed number of new cases within a year among a population of size
Denominator for the incidence data.
ci2num can be used to convert a published
estimate and interval for a proportion to an implicit numerator and
Note that to include extra uncertainty beyond that implied by a published interval, the numerator and denominator could be multiplied by a constant, for example, multiplying both the numerator and denominator by 0.5 would give the data source half its original weight.
Estimate of the incidence probability
Lower credible limit for the incidence estimate
Upper credible limit for the incidence estimate
Numerator for the estimate of prevalence, i.e. number of people currently with a disease.
Denominator for the estimate of prevalence (e.g. the size of the survey used to obtain the prevalence estimate)
Estimate of the prevalence probability
Lower credible limit for the prevalence estimate
Upper credible limit for the prevalence estimate
Numerator for the estimate of the mortality probability, i.e number of deaths attributed to the disease under study within a year
Denominator for the estimate of the mortality probability (e.g. the population size, if the estimates were obtained from a comprehensive register)
Estimate of the mortality probability
Lower credible limit for the mortality estimate
Upper credible limit for the mortality estimate
Numerator for the estimate of the remission probability, i.e number of people observed to recover from the disease within a year.
Remission data should be supplied if remission is permitted in the model, either as a numerator and denominator or as an estimate and lower credible interval. Conversely, if no remission data are supplied, then remission is assumed to be impossible. These "data" may represent a prior judgement rather than observation - lower denominators or wider credible limits represent weaker prior information.
Denominator for the estimate of the remission probability
Estimate of the remission probability
Lower credible limit for the remission estimate
Upper credible limit for the remission estimate
Variable in the data indicating the year of age. This must start at age zero, but can end at any age.
Initial guess at a typical case fatality value, for an average age.
Case fatalities (and incidence and remission rates) are assumed to be equal for all ages below this age, inclusive, when using the smoothed model.
Case fatalities (and incidence and remission rates) are assumed to be equal for all ages above this age, inclusive, when using the smoothed model.
The following alternative models for case fatality are supported:
"default" (the default). Random intercepts and slopes, and no
"interceptonly". Random intercepts, but common slopes.
"increasing". Case fatality is assumed to be an increasing function
of age (note it is constant below
"eqage" in all models) with a
common slope for all groups.
"common" Case fatality is an unconstrained function of age
which is common to all areas, i.e. it has the same parameter values in
every area. This and
"increasing_common" are used in situations
where you want to compare a model with area-specific rates with a single model for
the data aggregated over areas. Modelling the area-disaggregated data using
a common function for all areas is equivalent to a model for the aggregated data,
and can be statistically compared (using cross-validation) with a model with
"increasing_common" Case fatality is an increasing function of age
which is common to all areas.
"const" Case fatality is assumed to be constant with age, for all
ages, but different in each area.
"const_common" Case fatality is a constant over all ages and areas.
In all models, case fatality is a smooth function of age.
Model for how incidence varies with age.
"smooth" (the default). Incidence is modelled as a smooth spline
function of age, independently for each area (and gender).
"indep" Incidence rates for each year of age, area (and gender) are
Model for how remission varies with age. Currently
supported models are
"const" for a constant remission rate over all
"const" for a smooth spline, or
"indep" for a different remission rates estimated
independently for each age with no smoothing.
TRUE, attempt to estimate prevalence at age zero
from the data, as part of the Bayesian model, even if the observed prevalence is zero.
Otherwise (the default) this is assumed to be zero if the count is zero, and estimated
Rates of the exponential prior distributions used to penalise the coefficients of the spline model. The default of 1 should adapt appropriately to the data, but Higher values give stronger smoothing, or lower values give weaker smoothing, if required.
This can be a named vector with names
"inc","cf","rem" in any
order, giving the prior smoothness rates for incidence, case fatality and
remission. If any of these are not smoothed they can be excluded, e.g.
sprior = c(cf=10, inc=1).
This can also be an unnamed vector of three elements, where the first refers to the spline model for incidence, the second for case fatality, the third for remission. If one of the rates (e.g. remission) is not being modelled with a spline, any number can be supplied here and it is just ignored.
A list with one named element for each hyperparameter to be fixed. The value should be either
a number (to fix the hyperparameter at this number)
TRUE (to fix the hyperparameter at the posterior mode from a training run
where it is not fixed)
If the element is either
FALSE, or omitted from the list,
then the hyperparameter is given a prior and estimated as part of the Bayesian model.
The hyperparameters that can be fixed are
scf Smoothness parameter for the spline relating case fatality to age.
sinc Smoothness parameter for the spline relating incidence to age.
scfmale Smoothness parameter for the spline defining how the gender
effect relates to age. Only for models with additive gender and area effects.
sd_int Standard deviation of random intercepts for case fatality.
sd_slope Standard deviation of random slopes for case fatality.
For example, to fix the case fatality smoothness to 1.2, fix the incidence
smoothness to its posterior mode, and estimate all the other hyperparameters,
hp_fixed = list(scf=1.2, sinc=TRUE).
Prior guess at the ratio of case fatality between a high risk (97.5% quantile) and low risk (2.5% quantile) area.
Prior upper 95% credible limit for the ratio in average case fatality between a high risk (97.5% quantile) and low risk (2.5% quantile) area.
This argument and the next argument define the prior distribution for the variance in the random linear effects of age on log case fatality. They define a prior guess and upper 95% credible limit for the ratio of case fatality slopes between a high trend (97.5% quantile) and low risk (2.5% quantile) area. (Note that the model is not exactly linear, since departures from linearity are defined through a spline model. See the Jackson et al. paper for details.).
Vector of two elements giving the prior mean and standard deviation respectively for the mean random intercept for log case fatality.
Vector of two elements giving the prior mean and standard deviation respectively for the mean random slope for log case fatality.
Prior standard deviation for the additive effect of gender on log case fatality
Prior standard deviation for the additive effect of gender on the linear age slope of log case fatality
Vector of two elements giving the Gamma shape and rate parameters of the
prior for the incidence rate. Only used if
inc_model="indep", for each age-specific rate.
Vector of two elements giving the Gamma shape and rate parameters of the
prior for the remission rate, used in both
String indicating the inference method, defaulting to
method="mcmc" then a sample from the posterior is drawn using Markov Chain Monte Carlo
sampling, via rstan's
rstan::sampling() function. This is the most
accurate but the slowest method.
method="opt", then instead of an MCMC sample from the posterior,
disbayes returns the posterior mode calculated using optimisation, via
A sample from a normal approximation to the (real-line-transformed)
posterior distribution is drawn in order to obtain credible intervals.
If the optimisation fails to converge (non-zero return code), try increasing the
number of iterations from the default 1000, e.g.
disbayes(..., iter=10000, ...), or changing the algorithm to
disbayes(..., algorithm="Newton", ...).
If there is an error message that mentions
the computed Hessian matrix is not positive definite at the reported optimum, hence credible intervals
cannot be computed.
This can occur either because of numerical error in computation of the Hessian, or because the
reported optimum is wrong. If you are willing to believe
the optimum and live without credible intervals, then set
draws=0 to skip
computation of the Hessian. To examine the problematic Hessian, set
hessian=TRUE,draws=0, then look at the
$fit$hessian component of the
disbayes return object. If it can be inverted, do
sqrt(diag(solve())) on the Hessian, and
NaNs, indicating the problematic parameters.
Otherwise, diagonal entries of the Hessian matrix that are very small
may indicate parameters that are poorly identified from the data, leading to computational
method="vb", then variational Bayes methods are used, via rstan's
rstan::vb() function. This is labelled as "experimental" by
rstan. It might give a better approximation to the posterior
method="opt", but has not been investigated much for
Number of draws from the normal approximation to the posterior
Number of iterations for MCMC sampling, or maximum number of iterations for optimization.
method="mcmc" only). List of options supplied as the
rstan::sampling() in rstan for the main model fit.
A list including the following components
call: Function call that was used.
fit: An object containing posterior samples from the fitted model,
stanfit format returned by the
function in the rstan package.
method: Optimisation method that was chosen.
nage: Number of years of age in the data
narea: Number of areas (or other grouping variable that defines the hierarchical model).
ng: Number of genders (or other categorical variable whose effect is treated as
additive with the area effect).
groups: Names of the areas (or other grouping variable), taken from the factor levels in the
genders: Names of the genders (or other categorical variable), taken from the factor levels in the
dat: A list containing the input data in the form of numerators
stan_data: Full list of data supplied to Stan
stan_inits: Full list of parameter initial values supplied to Stan
trend: Whether a time trend was modelled
hp_fixed Values of any hyperparameters that are fixed during the main model fit.
Jackson C, Zapata-Diomedi B, Woodcock J. "Bayesian multistate modelling of incomplete chronic disease burden data" https://arxiv.org/abs/2111.14100