decision_tree_cluster: Decision tree

In n8thangreen/ltbiScreenLite: ltbiScreenLite

Description

Calculate decision tree expected costs and QALY loss for each simulation.

Usage

decision_tree_cluster(params, N.mc = 2,
  cost_dectree = "data/osNode_cost_2009.Rds",
  health_dectree = "data/osNode_health_2009.Rds", out_datatree = FALSE)

Arguments

params	an element of a scenario list with probabilities and costs to substitue into decision tree; long format array
N.mc	number of simulations; integer
cost_dectree	data.tree saved as Rds file names (string); default to package folder
health_dectree	data.tree saved as Rds file names (string); default to package folder
out_datatree	Output full datatree object? This may be large. Will also save to csv for checking; default: FALSE

Value

list

• mc_cost: each simulation total expected cost

• mc_health: each simulation total expected QALY loss

• subset_pop: cohort population sizes and probabilities at specific node or groups of nodes. Specifically calculates for individuals with LTBI since these are the subset of particular interest in term of cure; dataframe headings are

  • LTBI_pre

  • tests

  • positive

  • startTx

  • completeTx

  • cured

  • LTBI_post

  • p_LTBI_to_cured

  • LTBI_tests

  • LTBI_positive

  • LTBI_startTx

  • LTBI_completeTx

• osNode.cost: data.tree object

• osNode.health: data.tree object

• call: original call with arguments

• N.mc: number of Monte-Carlo simulations

n8thangreen/ltbiScreenLite documentation built on May 28, 2020, 9:37 p.m.