ccmean {ccostr}

Calculates estimates of the mean cost with censored data

Description

This function calculates the mean cost for right-censored cost data over a period of L time units (days, months, years,...)

Usage

ccmean(x, L = max(x$surv), addInterPol = 0)

Arguments

x

A dataframe with columns: id, cost, delta and surv. If Cost history is available it can be specified by: start and stop,

L

Limit. Mean cost is calculated up till L, if not specified L = max(surv)

addInterPol

This parameter affects the interpolation of cost between two observed times. Defaults to zero.

Details

The function returns four estimates. The first two are simple and biased downwards, and included for comparison. The estimates are:

- AS: "Available Sample estimator" - The simple sample mean

- CC: "Complete Case estimator" - The mean of fully observed cases

- BT: "Weighted Complete Case estimator" - Bang and Tsiatis's estimator

- ZT: "Weighted Available estimator" - Zhao and Tian's estimator

The function needs the following in a dataframe:

- id: The id separating each individual

- cost: The total cost, or if start and stop provided the specific cost

- start: Start of cost

- stop: End of cost, if one time cost then start = stop

- delta: Event variable, 1 = event, 0 = no event

- surv: Survival

Value

An object of class "ccobject".

References

Bang H, Tsiatis AA (2000). “Estimating medical costs with censored data.” Biometrika, 87(2), 329–343. ISSN 00063444, doi: 10.1093/biomet/87.2.329.

Zhao H, Tian L (2001). “On Estimating Medical Cost and Incremental Cost-Effectiveness Ratios with Censored Data.” Biometrics, 57(4), 1002–1008. ISSN 0006341X, doi: 10.1111/j.0006-341X.2001.01002.x.

Examples

hcost
ccmean(hcost, L = 1461, addInterPol = 1)