standardize

Convert <code>surveil</code> model results to age standardized rates using a fixed age distribution

Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).

Connor Donegan

surveil

Time Series Models for Disease Surveillance

standardize function

<dl><dt>x</dt>
<dd>A fitted <code>surveil</code> model</dd>
<dt>label</dt>
<dd>Labels (character strings) for the age groups that correspond to the values of <code>stand_pop</code>. All levels of the grouping variable used to fit the model (i.e., <code>names(x$data$cases)</code>) must be present in <code>label</code>.</dd>
<dt>standard_pop</dt>
<dd>Standard population values corresponding to the age groups specified by <code>label</code>.</dd></dl>

Arguments

Age-standardized rates — standardize

<dl>

<dt>x</dt>
<dd>A fitted <code>surveil</code> model</dd>


<dt>label</dt>
<dd>Labels (character strings) for the age groups that correspond to the values of <code>stand_pop</code>. All levels of the grouping variable used to fit the model (i.e., <code>names(x$data$cases)</code>) must be present in <code>label</code>.</dd>


<dt>standard_pop</dt>
<dd>Standard population values corresponding to the age groups specified by <code>label</code>.</dd>

</dl>

standardize: Age-standardized rates

Description

Usage

Value

Arguments

See Also

Examples