disbayes | R Documentation |
Estimates a three-state disease model from incomplete data. It is designed to estimate case fatality and incidence, given data on mortality and at least one of incidence and prevalence. Remission may also be included in the data and modelled.
disbayes(
data,
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_model = "smooth",
inc_model = "smooth",
rem_model = "const",
prev_zero = FALSE,
inc_trend = NULL,
cf_trend = NULL,
cf_init = 0.01,
eqage = 30,
eqagehi = NULL,
sprior = c(1, 1, 1),
hp_fixed = NULL,
rem_prior = c(1.1, 1),
inc_prior = c(2, 0.1),
cf_prior = c(2, 0.1),
method = "opt",
draws = 1000,
iter = 10000,
stan_control = NULL,
bias_model = NULL,
...
)
data |
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. |
inc_num |
Numerator for the incidence data, assumed to represent the
observed number of new cases within a year among a population of size
|
inc_denom |
Denominator for the incidence data. The function 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. |
inc_prob |
Estimate of the incidence probability |
inc_lower |
Lower credible limit for the incidence estimate |
inc_upper |
Upper credible limit for the incidence estimate |
prev_num |
Numerator for the estimate of prevalence, i.e. number of people currently with a disease. |
prev_denom |
Denominator for the estimate of prevalence (e.g. the size of the survey used to obtain the prevalence estimate) |
prev_prob |
Estimate of the prevalence probability |
prev_lower |
Lower credible limit for the prevalence estimate |
prev_upper |
Upper credible limit for the prevalence estimate |
mort_num |
Numerator for the estimate of the mortality probability, i.e number of deaths attributed to the disease under study within a year |
mort_denom |
Denominator for the estimate of the mortality probability (e.g. the population size, if the estimates were obtained from a comprehensive register) |
mort_prob |
Estimate of the mortality probability |
mort_lower |
Lower credible limit for the mortality estimate |
mort_upper |
Upper credible limit for the mortality estimate |
rem_num |
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. |
rem_denom |
Denominator for the estimate of the remission probability |
rem_prob |
Estimate of the remission probability |
rem_lower |
Lower credible limit for the remission estimate |
rem_upper |
Upper credible limit for the remission estimate |
age |
Variable in the data indicating the year of age. This must start at age zero, but can end at any age. |
cf_model |
Model for how case fatality rate varies with age.
|
inc_model |
Model for how incidence rates vary with age.
|
rem_model |
Model for how remission rates vary with age, which are typically less well-informed by data, compared to incidence and case fatality.
|
prev_zero |
If |
inc_trend |
Matrix of constants representing trends in incidence
through calendar time by year of age. There are To produce this format from a long data frame, filter to the appropriate
outcome and subgroup, and use
|
cf_trend |
Matrix of constants representing trends in case fatality
through calendar time by year of age, in the same format as
|
cf_init |
Initial guess at a typical case fatality value, for an average age. |
eqage |
Case fatalities (and incidence and remission rates) are assumed to be equal for all ages below this age, inclusive, when using the smoothed model. |
eqagehi |
Case fatalities (and incidence and remission rates) are assumed to be equal for all ages above this age, inclusive, when using the smoothed model. |
sprior |
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 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. |
hp_fixed |
A list with one named element for each hyperparameter to be fixed. The value should be either
If the element is either The hyperparameters that can be fixed are
For example, to fix the case fatality smoothness to 1.2 and fix the incidence
smoothness to its posterior mode,
specify |
rem_prior |
Vector of two elements giving the Gamma shape and rate parameters of the
prior for the remission rate, used in both |
inc_prior |
Vector of two elements giving the Gamma shape and rate parameters of the
prior for the incidence rate. Only used if |
cf_prior |
Vector of two elements giving the Gamma shape and rate parameters of the
prior for the case fatality rate. Only used if |
method |
String indicating the inference method, defaulting to
If If If the optimisation fails to converge (non-zero return code), try increasing the
number of iterations from the default 1000, e.g. If there is an error message that mentions If |
draws |
Number of draws from the normal approximation to the posterior
when using |
iter |
Number of iterations for MCMC sampling, or maximum number of iterations for optimization. |
stan_control |
( |
bias_model |
Experimental model for bias in the incidence estimates due
to conflicting information between the different data sources. If
Otherwise there are assumed to be two alternative curves of incidence by age (denoted 2 and 1) where curve 2 is related to curve 1 via a constant hazard ratio that is estimated from the data, given a standard normal prior on the log scale. Three distinct curves would not be identifiable from the data. If
If |
... |
Further arguments passed to |
A list including the following components
call
: Function call that was used.
fit
: An object containing posterior samples from the fitted model,
in the stanfit
format returned by the stan
function in the rstan package.
method
: Optimisation method that was chosen.
nage
: Number of years of age in the data
dat
: A list containing the input data in the form of numerators
and denominators.
stan_data
: Full list of data supplied to Stan
stan_inits
: Full list of parameter initial values supplied to Stan
hp_fixed
Values of any hyperparameters that are fixed during the main model fit.
Use the tidy.disbayes
method to return summary statistics
from the fitted models, simply by calling tidy()
on the fitted model.
Jackson C, Zapata-Diomedi B, Woodcock J. (2023) "Bayesian multistate modelling of incomplete chronic disease burden data" Journal of the Royal Statistical Society, Series A, 186(1), 1-19 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/jrsssa/qnac015")}
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