primetime: Estimation of the PRIMEtime-CE Obesity model
In seamuskent/PRIMEtime-CE-Obesity: PRIMEtime-CE Obesity Model

Description Usage Arguments Value Examples

primetime generates estimates of disease incidence, life-years, quality-adjusted life-years, and health and social care costs over the specified time-horizon up to age 100 years by sex and age group (in 5-year bands).

primetime(time.horizon = NULL, age.range = c(20, 100),
  cost.of.treatment = NULL, bmi.target.min = NULL, bmi.target.max = NULL,
  mean.wt.loss.yr1 = NULL, se.diff = NULL, time.to.trt.effect = NULL,
  time.to.weight.regain = NULL, some.wt.loss.maintained = FALSE,
  mean.wt.loss.maintained = NULL, discount.rate.health = NULL,
  discount.rate.cost = NULL, deterministic = TRUE,
  bmi.directEffect.onMortality = TRUE, bmi.directEffect.onQoL = FALSE,
  model.incidence.trends = TRUE, whole.uk.population = TRUE,
  population.characteristics = NULL, subgroup = "All adults")

`time.horizon`	A positive integer. Outcomes are estimated up to age 100 years.
`age.range`	A two element numeric vector giving the lowest and highest ages over which to estimate outcomes.
`cost.of.treatment`	A numeric vector with length equal to the number of active interventions giving intervention costs in year 1.
`bmi.target.min`	A positive integer giving the lowest BMI group at whom the intervention(s) is targeted.
`bmi.target.max`	A positive integer giving the highest BMI group at whom the intervention(s) is targeted. A value of Inf means no upper bound.
`mean.wt.loss.yr1`	A numeric vector with length equal to the number of active interventions to assess. Values are mean weight loss in kilograms 1-year following intervention.
`se.diff`	A non-negative integer giving the standard error of the difference in effect between two treatment options. Only required if deterministic = FALSE.
`time.to.trt.effect`	A non-negative integer giving the delay between initiation of treatment and health effects.
`time.to.weight.regain`	A positive integer giving the years till weight returns to baseline, or in the case of some long-term residual weight difference, the time till the weight difference stabilises.
`some.wt.loss.maintained`	A logical value indicating whether there is some residual weight difference between treatment and the control groups after weight regain.
`mean.wt.loss.maintained`	A numeric vector with length equal to the number of active interventions to assess. Values are mean weight loss in kilograms. Only required in some.wt.loss.maintained = TRUE.
`discount.rate.health, discount.rate.cost`	Non-negative values giving the annual discount rate for health benefits and costs. A value of 3.5 means a discount rate of 3.5%.
`deterministic`	Whether to run a deterministic (default) or probabilistic analysis.
`whole.uk.population`	Whether to model outcomes over the whole UK population of selected age (default) or a user-specified population.
`subgroup`	Indicates whether to model outcomes across all adults ("All adults"; default) or in subgroup of individuals with type-2 diabetes ("Diabetics"") or without ("Non-diabetics").
`population.characeristics`	A five-element numeric vector reporting in order: the proportion of men, mean age in men, standard deviation of age in men, mean age in women, and standard deviation of age in women. Only required when whole.uk.population = TRUE.

Mean per-person health and economic outcomes in groups defined by sex and age (in 5-year bands) for each year of analysis, for the control and treatment groups, and differences between treatment and control groups. The output can be accessed by other functions to produce standardised outputs (e.g. summary tables), or can be used directly by the user for bespoke analyses.

primetime(time.horizon = 25,age.range = c(20, 80),cost.of.treatment = c(34.06, 1001.58),bmi.target.min = 30, bmi.target.max = 60,mean.wt.loss.yr1 = c(3.1, 10.3),time.to.trt.effect = 1,time.to.weight.regain = 5,some.wt.loss.maintained = FALSE,mean.wt.loss.maintained = NULL,discount.rate.health = 3.5,discount.rate.cost = 3.5,whole.uk.population = TRUE)