Estimates disease prevalence for a given index date, using existing registry data extended with Monte Carlo simulations.
rprev estimates disease prevalence at a specified index date from registry data. To improve the estimate accuracy, Monte Carlo simulation techniques are used to simulate incident cases in years for which incidence data is unavailable. Disease survival is modelled with parametric Weibull regression. See the user_guide vignette for more details about the implementation, and the original publication for details of the algorithm, available at http://www.ncbi.nlm.nih.gov/pubmed/24656754.
To install from CRAN, simply use
install.packages('rprev'). The code is currently not available elsewhere.
Fixed an issue caused by the latest version of
This function has been renamed to be more descriptive of what the function actually does, and reparameterised to allow the user to specify the ending date of the time interval of interested instead.
raw_incidence is still included but it throws a deprecated warning and suggests the use of
The original function name isn't very descriptive for what it does (provides the yearly end points of a specific time interval) and so have renamed it to better reflect its purpose.
determine_yearly_limits has a slighlty different argument list to
determine_registry_years to allow for the specification of the closing date in the interval rather than the opening.
prevalenceno longer runs the simulation when there is more registry data available than needed to estimate N-year prevalence
prevalenceno longer requires a population size as an argument. Absolute prevalence is always calculated, with relative rates provided if population size is specified
user_manual: Updated to include a link to the specific webpage where the ONS data set is obtained from and improved formatting
summary.prevalencecorrectly displays posterior age distributions of simulated cases and now displays the prevalence estimates themselves
The posterior age distribution, returned from
prevalence as in the
simulated object, is now stored in the format of a nested list rather than a matrix as before. The first dimension of the list corresponds to each sex (if applicable), the next indexing the number of years of simulated cases, and the final corresponds to the bootstrap samples. The final level comprises a vector holding the ages of the simulated cases which are still contributing to prevalence at the index date from the corresponding sex, year, and bootstrap sample number.
Minor bug fixes and a slight change to the parameterisation of prevalence:
First release of the package, working with all features necessary to provide estimates of point prevalence. Issues which we'd like to address in future releases are: