cancer

Annual cancer cases (all sites) by age group for the United States.

datasets

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

cancer function

A data frame with the following columns:<dl>
<dt>Year</dt>
<dd>Year of diagnosis</dd><dt>Age</dt>
<dd>Age group</dd><dt>Count</dt>
<dd>Number of cancer cases</dd><dt>Population</dt>
<dd>Age-specific population estimates</dd>
</dl>

Format

US cancer incidence by age, 1999-2017 — cancer

A data frame with the following columns:<dl>
<dt>Year</dt>
<dd>Year of diagnosis</dd>

<dt>Age</dt>
<dd>Age group</dd>

<dt>Count</dt>
<dd>Number of cancer cases</dd>

<dt>Population</dt>
<dd>Age-specific population estimates</dd>


</dl>

cancer: US cancer incidence by age, 1999-2017

Description

Usage

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Examples