Description Usage Arguments Value References Examples
View source: R/CalculatePower.R
This function calculates power for micro-randomized trials (MRTs) based on methodology developed in Sample Size Calculations for Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | calculatePower(
days,
occ_per_day,
prob,
beta_shape,
beta_mean,
beta_initial,
beta_quadratic_max,
tau_shape,
tau_mean,
tau_initial,
tau_quadratic_max,
dimB,
sample_size,
sigLev
)
|
days |
The duration of the study. |
occ_per_day |
The number of decision time points per day. |
prob |
The randomization probability, i.e. the probability of assigning the treatment at a decision time point. This can be constant, or time-varying probabilities can be specified by a vector specifying randomization probabilities for each day or decision time. |
beta_shape |
The trend for the proximal treatment effect, choices are constant, linear or quadratic. Note:
|
beta_mean |
The average of proximal treatment effect. |
beta_initial |
The initial value of proximal treatment effect when beta_shape is linear or quadratic. |
beta_quadratic_max |
The day of maximal proximal treatment effect when beta_shape is quadratic. |
tau_shape |
The pattern for expected availability; choices can be constant, linear or quadratic. Note:
|
tau_mean |
The average of expected availability. |
tau_initial |
The initial Value of expected availability when tau_shape is linear or quadratic. |
tau_quadratic_max |
The changing point of availability when tau_shape is quadratic. |
dimB |
The number of parameters used in the main/average effect of proximal outcome |
sample_size |
The number of participants |
sigLev |
The significance level or type I error rate. |
The achieved power given the input sample size
Seewald, N.J.; Sun, J.; Liao, P. "MRT-SS Calculator: An R Shiny Application for Sample Size Calculation in Micro-Randomized Trials". arXiv:1609.00695
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | calculatePower(days=42,
occ_per_day=5,
prob=0.4,
beta_shape="quadratic",
beta_mean=0.1,
beta_initial=0,
beta_quadratic_max=28,
tau_shape="quadratic",
tau_mean=0.5,
tau_initial=0.7,
tau_quadratic_max=42,
dimB=3,
sample_size=40,
sigLev=0.05)
prob1 <- c(replicate(35,0.7),replicate(35,0.6),replicate(35,0.5),replicate(35,0.4))
calculatePower(days=28,
occ_per_day=5,
prob=prob1,
beta_shape="quadratic",
beta_mean=0.1,
beta_initial=0,
beta_quadratic_max=28,
tau_shape="quadratic",
tau_mean=0.5,
tau_initial=0.7,
tau_quadratic_max=42,
dimB=3,
sample_size=40,
sigLev=0.05)#'
|
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