dectree: Decision Tree Calculation

In n8thangreen/VANTDET: Cost-Effectiveness Decision Tree

Description

Probabilistically incorporates sampling variability (and standard pathway cost and time to diagnosis).

Usage

dectree(
  data,
  nsim = 1000,
  costDistns,
  time_res,
  drug = drug,
  prevalence = 0.25,
  performance,
  c.newtest = 0,
  name.newtest = NA,
  quant = 0.5,
  QALYloss,
  N = nrow(data),
  wholecohortstats = FALSE,
  terminal_health,
  terminal_cost,
  followup_pdf,
  ...
)

Arguments

data	IDEA study data (data.frame)
nsim	Number of sample points (integer) Default: 1000
costDistns	List of distribution names and parameter values for each test/procedure
time_res	time to obtain novel test result (list)
drug	treatment drug cost and quantities (list)
prevalence	TB proportion in cohort (double 0-1) Default: 0.25
performance	Sensitivity and specificity test (list)
c.newtest	Rule-out test unit cost (double) Defalut: 0
name.newtest	Name of rule-out test to get at distribution (string)
quant	Quantile value of time to diagnosis and costs from observed data (double 0-1) Default: 0.5 (median)
QALYloss	QALY loss due to active TB (list)
N	Number of patients. The number in the data is used as default
wholecohortstats	Should the output stats be the total or per patient
terminal_health	function of terminal node total QALY loss
terminal_cost	function of terminal node total costs
followup_pdf	Follow-up appointment time probability distribution function
...

Value

Health and cost realisations (list)

n8thangreen/VANTDET documentation built on May 28, 2020, 8:27 a.m.