Package '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)

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Simulated data from the stata hcost package

Description

Simulated data from the stata hcost package

Usage

data(hcost)

Format

A data frame with 9882 rows and 7 variables:

id

id seperating individuals

start

start of specified cost

stop

end of specified cost

cost

cost in given period

trt

treatment variable

delta

event variable, 0 = censored

surv

survival period

Source

Blog

References

Chen S, Rolfes J, Zhao H (2015). “Estimation of Mean Health Care Costs and Incremental Cost-effectiveness Ratios with Possibly Censored Data.” The Stata Journal: Promoting communications on statistics and Stata, 15(3), 698–711. ISSN 1536-867X. doi:10.1177/1536867X1501500305. The Stata Journal.

Examples

data(hcost)

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Adding to the generic plot function

Description

Adding to the generic plot function

Usage

## S3 method for class 'ccobject'
plot(x, ...)

Arguments

x	The ccobject
...	passthrough

Value

a plot

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Adding to the generic print function

Description

Adding to the generic print function

Usage

## S3 method for class 'ccobject'
print(x, ...)

Arguments

x	The ccobject
...	passthrough

Value

a plot

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Simulates censored cost data

Description

This function can be used to demonstrate the bias and coverage of the estimators in the ccmean function

Usage

simCostData(n = 100, dist = "unif", censor = "light", cdist = "exp", L = 10)

Arguments

n	Number of individuals to simulate
dist	Survival distribution either "unif" = unif(0,10) o r "exp" = exp (1/6)
censor	Censoring "light" ~ 25% or "heavy" ~ 40%, changes a bit depending on cdist
cdist	Distribution used to censor, "exp" exponential or "unif" uniform
L	Number of years to summarize over

Details

The function simulates survival times from either an uniform distribution or an exponential distribution, and a cost history. There are two options for censoring, heavy (~40 light (~25

Value

Simulation of censored cost

References

Lin DY, Feuer EJ, Etzioni R, Wax Y (1997). “Estimating Medical Costs from Incomplete Follow-Up Data.” Biometrics, 53(2), 419. ISSN 0006341X. doi:10.2307/2533947.

Examples

# The simulated data can be used to show how the estimators perform

simCostData(n = 100, dist = "unif", censor = "light", cdist = "exp", L = 10)

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