mlr.power {MatchLinReg} | R Documentation |
Monte Carlo based calculation of study power for treatment effect estimation using linear regression on treatment indicator and adjustment covariates.
mlr.power(tr, Z.i = NULL, d, sig.level = 0.05, niter = 1000
, verbose = FALSE, idx = 1:length(tr), rnd = FALSE)
tr |
Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE. |
Z.i |
Matrix of adjustment covariates included in linear regression. We must have |
d |
Standardized effect size, equal to treatment effect divided by standard deviation of generative noise. |
sig.level |
Significance level for rejecting null hypothesis. |
niter |
Number of Monte Carlo simulations used for calculating power. |
verbose |
If |
idx |
Subset of observations to use for power calculation. |
rnd |
Boolean flag. If |
In each Monte Carlo iteration, response variable is generated from a normal distribution whose mean is equal to d * tr
(other coefficients are assumed to be zero since their value does not affect power calculation), and whose standard deviation is 1.0
. Then OLS-based regression is performed on data, and p-value for treatment effect is compared to sig.level
, based on which null hypothesis (no effect) is rejected or accepted. The fraction of iterations where null hypothesis is rejected is taken to be power. Standard error is calculated using a binomial-distribution assumption.
A numeric vector is returned. If rnd
is FALSE
, meand and standard error of calculated power is returned. If rnd
is TRUE
, mean and standard error of power calculated for random subsampling of observations is returned as well.
Alireza S. Mahani, Mansour T.A. Sharabiani