PwrSampleSize {SynergyLMM} | R Documentation |
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
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. -
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
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
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)