PwrSampleSize: _A Priori_ Synergy Power Analysis Based on Sample Size

In SynergyLMM: Statistical Framework for in Vivo Drug Combination Studies

A Priori Synergy Power Analysis Based on Sample Size

Description

A priori power calculation for a hypothetical two-drugs combination study of synergy evaluation using linear-mixed models depending on the sample size per group.

Usage

PwrSampleSize(
  npg = c(5, 8, 10),
  time = c(0, 3, 5, 10),
  grwrControl = 0.08,
  grwrA = 0.07,
  grwrB = 0.06,
  grwrComb = 0.03,
  sd_ranef = 0.01,
  sgma = 0.1,
  method = "Bliss",
  ...
)

Arguments

npg	A vector with the sample size (number of subjects) per group to calculate the power of the synergy analysis.
time	Vector with the times at which the tumor volume measurements have been performed.
grwrControl	Coefficient for Control treatment group tumor growth rate.
grwrA	Coefficient for Drug A treatment group tumor growth rate.
grwrB	Coefficient for Drug B treatment group tumor growth rate.
grwrComb	Coefficient for Combination (Drug A + Drug B) treatment group tumor growth rate.
sd_ranef	Random effects standard deviation for the model.
sgma	Residuals standard deviation for the model.
method	String indicating the method for synergy calculation. Possible methods are "Bliss" and "HSA", corresponding to Bliss and highest single agent, respectively.
...	Additional parameters to be passed to nlmeU::Pwr.lme method.

Details

PwrSampleSize allows the user to define an hypothetical drug combination study, customizing several experimental parameters, such as the sample size, time of measurements, or drug effect, for the power evaluation of synergy for Bliss and HSA reference models. The power calculation is based on F-tests of the fixed effects of the model as previously described (Helms, R. W. (1992), Verbeke and Molenberghs (2009), Gałecki and Burzykowski (2013)).

The focus the power analysis with PwrSampleSize is on the sample size per group. The function allows for the evaluation of how the statistical power changes when the sample size per group varies while the other parameters are kept constant. For other a priori power analysis see also APrioriPwr() and PwrTime().

• time, grwrControl, grwrA, grwrB, grwrComb, sd_ranef and sgma are parameters referring to the initial exemplary data set and corresponding fitted model. These values can be obtained from a fitted model, using lmmModel_estimates(), or be defined by the user.

• npg is a vector indicating the different sample sizes for which the statistical power is going to be evaluated, keeping the rest of parameters fixed.

Value

The functions returns two plots:

• A plot representing the hypothetical data, with the regression lines for each treatment group according to grwrControl, grwrA, grwrB and grwrComb values. The values assigned to sd_ranef and sgma are also shown.

• A plot showing the values of the power calculation depending on the values assigned to npg.

The function also returns the data frame with the power for the analysis for each sample size specified in npg.

References

• Helms, R. W. (1992). Intentionally incomplete longitudinal designs: I. Methodology and comparison of some full span designs. Statistics in Medicine, 11(14–15), 1889–1913. https://doi.org/10.1002/sim.4780111411

• Verbeke, G. & Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. Springer New York. https://doi.org/10.1007/978-1-4419-0300-6

• Andrzej Galecki & Tomasz Burzykowski (2013) Linear Mixed-Effects Models Using R: A Step-by-Step Approach First Edition. Springer, New York. ISBN 978-1-4614-3899-1

See Also

PostHocPwr, APrioriPwr(), PwrTime().

Examples

PwrSampleSize(npg = 1:20)

SynergyLMM documentation built on April 4, 2025, 4:13 a.m.