computeMdrr | R Documentation |
Compute the minimum detectable relative risk
computeMdrr(
population,
alpha = 0.05,
power = 0.8,
twoSided = TRUE,
modelType = "cox"
)
population |
A data frame describing the study population as created using the
|
alpha |
Type I error. |
power |
1 - beta, where beta is the type II error. |
twoSided |
Consider a two-sided test? |
modelType |
The type of outcome model that will be used. Possible values are "logistic", "poisson", or "cox". Currently only "cox" is supported. |
Compute the minimum detectable relative risk (MDRR) and expected standard error (SE) for a given study population, using the actual observed sample size and number of outcomes. Currently, only computations for Cox and logistic models are implemented. For Cox model, the computations by Schoenfeld (1983) is used. For logistic models Wald's z-test is used.
A data frame with the MDRR and some counts.
Schoenfeld DA (1983) Sample-size formula for the proportional-hazards regression model, Biometrics, 39(3), 499-503
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