APrioriPwr: _A Priori_ Synergy Power Analysis Based on Variability and...

In SynergyLMM: Statistical Framework for in Vivo Drug Combination Studies

A Priori Synergy Power Analysis Based on Variability and Drug Effect

Description

A priori power calculation for a hypothetical two-drugs combination study of synergy using linear-mixed models with varying drug combination effect and/or experimental variability.

Usage

APrioriPwr(
  npg = 5,
  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,
  sd_eval = NULL,
  sgma_eval = NULL,
  grwrComb_eval = NULL,
  method = "Bliss",
  ...
)

Arguments

npg	Number of subjects per group.
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 (between-subject variance) for the model.
sgma	Residuals standard deviation (within-subject variance) for the model.
sd_eval	A vector with values representing the standard deviations of random effects, through which the power for synergy calculation will be evaluated.
sgma_eval	A vector with values representing the standard deviations of the residuals, through which the power for synergy calculation will be evaluated.
grwrComb_eval	A vector with values representing the coefficients for Combination treatment group tumor growth rate, through which the power for synergy calculation will be evaluated.
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

APrioriPwr allows for total customization of an hypothetical drug combination study and allows the user to define 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 APrioriPwr is on the drug combination effect and the variability in the experiment. For other a priori power analysis see also PwrSampleSize() and PwrTime().

• npg, 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.

• sd_eval, sgma_eval, and grwrComb_eval are the different values that will be modified in the initial exemplary data set to fit the corresponding model and calculate the power.

Value

The functions returns several 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 sd_eval and sgma_eval. The power result corresponding to the values assigned to sd_ranef and sgma is shown with a red dot.

• A plot showing the values of the power calculation depending on the values assigned to grwrComb_eval. The vertical dashed line indicates the value of grwrComb. The horizontal line indicates the power of 0.80.

The statistical power for the fitted model for the initial data set according to the values given by npg, time, grwrControl, grwrA, grwrB, grwrComb, sd_ranef and sgma is also shown in the console.

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,PwrSampleSize(), PwrTime().

Examples

APrioriPwr(
sd_eval = seq(0.01, 0.2, 0.01),
sgma_eval = seq(0.01, 0.2, 0.01),
grwrComb_eval = seq(0.01, 0.1, 0.005)
)

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