APrioriPwr {SynergyLMM} | R Documentation |
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
andsgma
are parameters referring to the initial exemplary data set and corresponding fitted model. These values can be obtained from a fitted model, usinglmmModel_estimates()
, or be defined by the user. -
sd_eval
,sgma_eval
, andgrwrComb_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
andgrwrComb
values. The values assigned tosd_ranef
andsgma
are also shown.A plot showing the values of the power calculation depending on the values assigned to
sd_eval
andsgma_eval
. The power result corresponding to the values assigned tosd_ranef
andsgma
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 ofgrwrComb
. 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)
)