computeMdrr: Compute the minimum detectable relative risk

In escott12/CohortMethod: New-User Cohort Method with Large Scale Propensity and Outcome Models

Description

Compute the minimum detectable relative risk

Usage

computeMdrr(
  population,
  alpha = 0.05,
  power = 0.8,
  twoSided = TRUE,
  modelType = "cox"
)

Arguments

population	A data frame describing the study population as created using the createStudyPopulation function. This should at least have these columns: personSeqId, treatment, outcomeCount, timeAtRisk.
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.

Details

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 models are implemented. For Cox model, the computations by Schoenfeld (1983) is used.

Value

A data frame with the MDRR and some counts.

References

Schoenfeld DA (1983) Sample-size formula for the proportional-hazards regression model, Biometrics, 39(3), 499-503

escott12/CohortMethod documentation built on Dec. 20, 2021, 6:37 a.m.