ehsanx/SimulationPerformance

Simulation Performance Measures

Data Generating Process

Data was generated from a time-dependent process. True parameter was set to -0.7.

Estimates to save

One model was fitted and saved the following 5 estimates from each simulation iteration (in the current version, done 1,000 times):

• est: parameter estimate from the model
• est.SE: SE estimate obtained from the model
• est.pVal: p-value estimate obtained from the model
• est.LowCI: Lower bound of confidence interval estimate obtained from the model
• est.HiCI: Upper bound of confidence interval estimate obtained from the model

The obtained simulation results/estimates are saved in the Data folder.

Usage

The functions provided in the Code folder are general enough to provide necessary simulation performance measures as described in the reference (Morris et al. 2019).

simres <- readRDS("Data/simres.RDS")

source("Code/function.R")

table4(simResults=simres, estimate = "est", estimateSE = "est.SE", estimateP = "est.pVal", estimateLowCI = "est.LowCI", estimateHiCI = "est.HiCI",trueParam = -0.7, measures = "all", confLevel = 0.95)

Function Author:

Lucy Mosquera; date: 2020-06-01.

Codes written during preparing MSc thesis, citation provided below. This work was supported by BC Support Unit's Real-World Clinical Trials Methods Cluster, Project #2, led by Dr. Karim.

References

• Morris, T. P., White, I. R., & Crowther, M. J. (2019). Using simulation studies to evaluate statistical methods. Statistics in medicine, 38(11), 2074-2102.

• Mosquera, L. (2020). Exploring inverse probability weighted per-protocol estimates to adjust for non-adherence using post-randomization covariates: a simulation study (MSc dissertation, University of British Columbia).