Health economic simulation modeling

Overview

hesim is a modular and computationally efficient R package for health economic simulation modeling and decision analysis that provides a general framework for integrating statistical analyses with economic evaluation. The package supports cohort discrete time state transition models (DTSTMs), N-state partitioned survival models (PSMs), and individual-level continuous time state transition models (CTSTMs), encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. It heavily utilizes Rcpp and data.table, making individual-level simulation, probabilistic sensitivity analysis (PSA), and incorporation of patient heterogeneity fast.

Features of the current version can be summarized as follows:

  • Cohort DTSTMs, individual-level CTSTMs, and N-state PSMs that encompass Markov and semi-Markov processes
  • Options to build models directly from fitted statistical models or by defining them in terms of expressions
  • Parameter estimates from either an R based model or from an external source
  • Convenience functions for sampling model parameters from parametric distributions or via bootstrapping
  • Parameter uncertainty propagated with PSA
  • Modeling patient heterogeneity
  • Performing cost-effectiveness analyses and representing decision uncertainty from PSAs
  • Simulation code written in C++ to boost performance

Installation

You can install the current release from CRAN or the most up to date development version from GitHub.

# Install from CRAN:
install.packages("hesim")

# Install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("hesim-dev/hesim")

Getting started

There are two good places to start:

  1. The Introduction to hesim article provides a quick introduction.

  2. The hesim manuscript describes the package (including mathematical details) more thoroughly.

You might also want to explore our example analyses which can be found in the manuscript and web articles. They are summarized in the table below, with some drawn from the Decision Modeling for Health Economic Evaluation textbook. Key areas of focus are the (i) statistical models of disease progression (in terms of the baseline risk and relative treatment effects) and (ii) the available data (either individual patient data (IPD) or aggregate-level data).

Baseline risk

Treatment effect

Name

Model

Disease model

Disease data

Disease model

Disease data

Application

1

Manuscript 4.1

iCTSTM

Multi-state model

IPD

Coefficient (AFT)

IPD

Oncology

2

Manuscript 4.2

PSM

Survival models

IPD

Coefficient (AFT)

Aggregate

Oncology

3

Manuscript 4.3

cDTSTM

Multi-state model (panel data)

IPD

RR

Aggregate

Oncology

4

Simple Markov cohort

cDTSTM

Multinomial

Aggregate

RR

Aggregate

HIV

5

Time inhomogeneous Markov (cohort)

cDTSTM

Custom

Aggregate

Coefficient (HR)

Aggregate

Hip replacement

6

Multinomial logit

cDTSTM

Multinomial logit

IPD

Coefficient (OR)

IPD

Generic

7

Time inhomogeneous Markov (individual)

iCTSTM

Custom

Aggregate

Coefficient (HR)

Aggregate

Hip replacement

8

Semi-Markov multi-state

iCTSTM

Multi-state model

IPD

Coefficient (AFT)

IPD

Generic

9

4-state PSM

PSM

Survival models

IPD

Coefficient (AFT)

IPD

Oncology

Note: iCTSTM = Individual-level continuous time state transition model; PSM = partitioned survival model; cDTSTM = Cohort discrete time state transition model. AFT = accelerated failure time; RR = relative risk; HR = hazard ratio; OR = odds ratio. IPD = individual patient data.