adaptiveDesign_binomial: Simulate adaptive design where control sample size is...

Description Usage Arguments Details Author(s) References See Also Examples

Description

Simulate adaptive design where control sample size is adapted according to prior effective sample size for binomial outcome as in Schmidli at al (2014).

Usage

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adaptiveDesign_binomial(ctl.prior, treat.prior, 
    N1, Ntarget, Nmin, M, 
    pc, pt, 
    discard.prior = TRUE, 
    vague = mixbeta(c(1, 1, 1)), 
    ess = "ecss", 
    ehss.method = "mix.moment", subtractESSofVague=TRUE,
    min.ecss, D=MSE,
    decision)

Arguments

ctl.prior

RBesT betaMix object (or powerprior object created by as.powerprior) as prior for the control group

treat.prior

RBesT betaMix object (or powerprior object created by as.powerprior) as prior for the treatment group

N1

Sample size in each group at interim

Ntarget

Target sample size in control group

Nmin

Minimum number of samples in control group after interim analysis

M

Final sample size in treatment group

pc

True control rate

pt

True treatment rate

discard.prior

Replace prior by vague prior if ESS<0?

vague

RBesT betaMix object (single component mixture prior) serving as baseline vague prior

ess

either "ecss" or "ehss" for effective current or historical sample size, respectively.

ehss.method

if ess=="ehss". Specify version of EHSS as in ehss.

subtractESSofVague

Removes prior ESS of vague component from interim EHSS

min.ecss

if ess=="ecss". Minimal ECSS of interest (negative). A large absolute value of min.ecss is computational expensive, could be set to -1 if discard.prior=TRUE and no interest in the ECSS estimate itself.

D

A function that measures informatives, e.g. MSE or user-specified function

decision

function created by decision2S.

Details

The traditional approach to prior effective sample size (prior ESS) is aimed at quantifying prior informativeness, but is not aimed at detecting potential prior-data conflict.

The ECSS computes the prior effective sample size in terms of samples from the current data model (i.e., samples with characteristics consistent with the current trial). Under extreme prior-data conflict, the prior may account for a negative number of samples, showing that information is subtracted, rather than added, by the elicited prior. The ECSS quantifies the number of current samples to be added or subtracted to the likelihood in order to obtain a posterior inference equivalent to that of a baseline prior model (e.g. in terms of mean squared error, MSE). Fur further details, see Wiesenfarth and Calderazzo (2019).

Standard approach uses effective historical sample size (ess="ehss"), while Wiesenfarth and Calderazzo (2019) use the effective current sample size (ess="ecss"). When the ECSS is negative, the design provides the option of discarding the prior (discard.prior=TRUE).

Extensive documentation is given in the vignette.

Author(s)

Manuel Wiesenfarth

References

Schmidli, H., Gsteiger, S., Roychoudhury, S., O'Hagan, A., Spiegelhalter, D., and Neuenschwander, B. (2014). Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics, 70(4):1023-103

Wiesenfarth, M., Calderazzo, S. (2019). Quantification of Prior Impact in Terms of Effective Current Sample Size. Submitted.

See Also

vignette("robustMAP",package="RBesT")

Examples

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# see
# vignette("vignetteDesign", package = "ESS")

wiesenfa/ESS documentation built on June 19, 2019, 4:19 p.m.