hct {HCT}

Comparison of a single armed study to a collection of study results from multiple previous clinical trials.

Description

When comparing a single armed study to historical controls it is necessary to take into account that there may be variation in the underlying treatment/placebo effect from study to study. If this among-study variability is not accounted for the type one and two errors may be inaccurate. Given a historical database of study data, such as one might have in a meta-analysis the program calculates the criteria for significance for a new study that uses the database as an historical control and calculates the power of such as study as a function of sample size and difference to be detected.

Usage

hct(data, estimate, standardError, N, iter = 2000, rseed = NA,
silent=TRUE,constantStderr=TRUE)

Arguments

data

A data frame of historical data one study per row.

estimate

The name or column number of the variable in data containing the estimated outcome.

standardError

The name or column number of the variable in data containing the standard error of the estimated outcome.

N

The name or column number of the variable in data containing the sample size of the study.

iter

The number of interations to use in the MCMC to calculate the posterior distribution of the among-study variation and mean outcome measure.

rseed

Seed for random number generator

silent

Suppresses STAN output to the console

constantStderr

If TRUE it assumes that that standard deviation for each study is known exactly this is appropriate for larger studies. If FALSE it assumes that they are proportional to a chi-square distribution with N-1 degrees of freedom and uses a hierarchical model for the patient-level variance.

Value

A hct object which is a list of four elements.

criteria

A function with signature (p,se,df==NULL) to calculate the cut-off value for statistical significance at a one sided p-value of p, with standard error of the estimate equal to se. When df=NULL a normal test is used otherwise a t-test with df degrees of freedom.

power

A function to calculate the power of a study with signature (t,delta,se,df=NULL), where t is the cuttoff value, se is the standard error of the estimate and delta is the treatment effect

effective.SD

Which is the effective standard deviation of the outcome measure. It is calculated as sqrt(sum(data[,standardError]^2*data[,N]*(data[,N]-1))/(sum(data[,N])-1))), which is what it would be if the parameter estimates were sample means. When df=NULL a normal test is used otherwise a t-test with df degrees of freedom.

fit

An object of class stanfit with the fit of the data

A generic summary function prints out the value of effective.SD and uses the data frame summary function for data.frame(extract(fit,c("mu"","sig"","y_pred"))). The generic print function prints this summary.

Author(s)

David A. Schoenfeld

References

Design and analysis of a clinical trial using previous trials as historical control

Examples

als=data.frame(estimate=c(3.5,2.6,2.3),SE=c(.4,.3,.6),N=c(100,150,76))
ts=hct(als,'estimate','SE','N')
print(ts)
us=ts$criteria(0.025,.3)
ts$power(us,5,.4)