Example discrete time health state transitions data simulated using multinomial logistic regression. Costs and utility are also included to facilitate cost-effectiveness analysis.
multinom3_exdataA list containing the following elements:
A data frame containing patient transitions between health states at discrete time intervals (i.e., on a yearly basis).
A list of data frames. The first data frame contains drug cost data and the second contains summary medical cost estimates.
A data frame of summary utility estimates.
The data frame has the following columns:
Patient identification number.
Treatment strategy identification number.
Treatment strategy name.
Patient age (in years).
A factor variable with 3 age groups: (i) age less than 40, (ii) age at least 40 and less than 60, and (iii) age at least 60.
1 if a patient is female; 0 if male.
The year since the start of data collection with the first year equal to 1.
State making a transition from.
State making a transition to.
Factor variable for year with 3 categories: (i) year 3 and below, (ii) year between 3 and 6, and (iii) year 7 and above.
The cost list contains two data frames. The first data frame contains data on the drug costs associated with each treatment strategy.
The treatment strategy identification number.
The treatment strategy name.
Annualized drug costs.
The second data frame contains summary data on medical costs by health state, and contains the following columns:
The health state identification number.
The name of the health state.
Mean medical costs.
Standard error of medical costs.
The data frame has the following columns:
The health state identification number.
The name of the health state.
Mean utility
Standard error of utility.