testData: Simulated effects on incidence and prognosis

In DudbridgeLab/indexevent: Adjustment for index event bias

Description

A simulated dataset consisting of regression coefficients on incidence and prognosis, with their standard errors, for 10,000 variables (eg SNPs). 500 variables have effects on incidence only, 500 on prognosis only, and 500 on both. The effects on incidence and prognosis are independent. The estimates are obtained from linear regression in a simulated dataset of 20,000 individuals.

Usage

testData

Format

A data frame with 10,000 rows and 4 variables:

xbeta

Regression coefficient on incidence

xse

Standard error of xbeta

ybeta

Regression coefficient on prognosis

yse

Standard error of ybeta

Examples

# Default analysis with Hedges-Olkin adjustment for regression dilution
# Does not calculate a standard error
indexevent(testData$xbeta,testData$xse,testData$ybeta,testData$yse)
# [1] "Coefficient -0.441061156526639"
# [1] "Standard error 0"
# [1] "95% CI -0.441061156526639 -0.441061156526639"

# SIMEX adjustment with 100 simulations for each step
indexevent(testData$xbeta,testData$xse,testData$ybeta,testData$yse,method="SIMEX",B=100)
# [1] "Coefficient -0.446543628582032"
# [1] "Standard error 0.011576233488927"
# [1] "95% CI -0.470301533547 -0.424923532117153"

# First few unadjusted effects on prognosis
testData$ybeta[1:5]
# [1]  0.032240  0.057070 -0.006959  0.080460  0.032820
# Adjusted effects
indexevent(testData$xbeta,testData$xse,testData$ybeta,testData$yse)$ybeta.adj[1:5]
# [1]  0.05219361  0.06110395 -0.01489810  0.08982814  0.01328099

DudbridgeLab/indexevent documentation built on Jan. 23, 2021, 11:35 a.m.