example_data2

The observed states and associated observation times for 1000 patients simulated from a 4 state process non-homogeneous Markov model with misclassification to adjacent transient states.

datasets

Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) <doi:10.1111/j.1541-0420.2010.01550.x>. Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities.

Andrew Titman

Non-Homogeneous Markov and Hidden Markov Multistate Models

example_data2 function

A data frame with 3864 rows and 5 variables:<dl>
 <dt>state</dt>
<dd>Observed state at the time of observation</dd> <dt>time</dt>
<dd>Time at which the observation occurred</dd> <dt>id</dt>
<dd>Patient identification number</dd> <dt>cov1</dt>
<dd>Binary covariate</dd> <dt>cov2</dt>
<dd>Continuous covariate</dd> 
</dl>

Format

Example of data on a progressive 4 state process with state misclassification — example_data2

A data frame with 3864 rows and 5 variables:<dl>
 <dt>state</dt>
<dd>Observed state at the time of observation</dd>

 <dt>time</dt>
<dd>Time at which the observation occurred</dd>

 <dt>id</dt>
<dd>Patient identification number</dd>

 <dt>cov1</dt>
<dd>Binary covariate</dd>

 <dt>cov2</dt>
<dd>Continuous covariate</dd>

 
</dl>

example_data2: Example of data on a progressive 4 state process with state misclassification

Description

Usage

Arguments

Format