ICEuncrt {ICEinfer}

Compute Bootstrap Distribution of ICE Uncertainty for given Shadow Price of Health, lambda

Description

ICEuncrt() uses bootstrap resampling (with replacement) to compute the distribution of uncertainty for 2-sample, 2-variable statistical inference. The 2 variables must be measures of effectiveness (higher values are better) and cost (lower values are better). The 2 samples are of patients receiving only 1 of the 2 possible treatments. The treatment called new is the one with the higher numerical level for the specified treatment indicator variable, while the treatment called std corresponds to the lower numerical level. The pivotal statistic for inference is (DeltaEffe, DeltaCost), which are the head-to-head mean differences for new treatment minus std treatment. Each sample is assumed to provide unbiased estimates of the overall expected effectiveness and cost for that treatment.

Usage

ICEuncrt(df, trtm, xeffe, ycost, lambda = 1, ceunit = "cost", R = 25000, seed = 0)

Arguments

df

Required; Existing data.frame object containing the trtm, xeffe and ycost variables.

trtm

Required; Name of the treatment indicator variable contained within the df data.frame that assumes one of only two different numerical values for each patient.

xeffe

Required; Name of the treatment effectiveness variable within the df data.frame.

ycost

Required; Name of the treatment cost variable within the df data.frame.

lambda

Optional; lambda strictly positive value for the Shadow Price of Health.

ceunit

Optional; ceunit character string containing either cost (default) or effe.

R

Optional; R positive integer value for the number of Bootstrap Replications desired. Minimum allowed value is 50; default value is 25000.

seed

Optional; seed is an integer between 0 and 25000. A seed value of 0 causes a random integer seed between 1 and 25000 to be generated. To reproduce results from a previous invocation of ICEuncrt(), use the seed value saved in its output list object.

Details

A single call to ICEuncrt() is usually made for a particular value of the Shadow Price of Health, lambda. Alternative statistical choices for lambda can be suggested by making calls to ICEscale() with different values for lambda. Because the bootstrap distribution of ICE uncertainty is equivariant under changes in lambda, it is much faster to transform an existing bootstrap distribution than to generate a new one for a different value of lambda. The print.ICEuncrt() and plot.ICEuncrt() functions thus have 2 special parameters, lfact and swa, that can change lambda and switch the ceunit of measurement, respectively, without actually generating a new bootstrap distribution via a call to ICEuncrt().

Value

Object of class ICEuncrt containing an output list with the following items:

df

Saved value of the name of the data.frame input to ICEuncrt.

lambda

Saved positive value of lambda input to ICEuncrt.

ceunit

Saved value of ceunit (cost or effe) input to ICEuncrt.

R

Saved integer value for number of bootstrap replications input to ICEuncrt.

trtm

Saved name of the treatment indicator within the df data.frame.

xeffe

Saved name of the treatment effectiveness variable within the df data.frame.

ycost

Saved name of the treatment cost variable within the df data.frame.

effcst

Saved value of the sorted 3-variable (trtm,effe,cost) data.frame.

t1

Observed value of (DeltaEffe, DeltaCost) when each patient is included exactly once.

t

R x 2 matrix of values of (DeltaEffe, DeltaCost) computed from bootstrap resamples.

seed

Saved value of the seed used to start pseudo random number generation.

Author(s)

Bob Obenchain <[e-mail]>

References

Obenchain RL, Melfi CA, Croghan TW, Buesching DP. Bootstrap analyses of cost-effectiveness in antidepressant pharmacotherapy. PharmacoEconomics 1997; 17: 1200-1206.

Obenchain RL. Resampling and multiplicity in cost-effectiveness inference. Journal of Biopharmaceutical Statistics 1999; 9(4): 563-582.

See Also

ICEscale, plot.ICEuncrt and print.ICEuncrt

Examples

  data(dulxparx)
  # Generating a bootstrap ICE uncertainty distribution is time consuming.
  dpunc <- ICEuncrt(dulxparx, dulx, idb, ru, lambda=0.26)
  plot(dpunc)
  # Transforming an existing bootstrap ICE uncertainty distribution is fast.
  dpuncX <- plot(dpunc, lfact=10)