indexevent: Adjust association statistics for index event bias
In DudbridgeLab/indexevent: Adjustment for index event bias

Description Usage Arguments Details Value Author(s) References

Given effect sizes and standard errors for predictors of an index trait and a subsequent trait, this function adjusts the statistics for the subsequent trait for selection bias through the index trait.

indexevent(
  xbeta,
  xse,
  ybeta,
  yse,
  weighted = T,
  prune = NULL,
  method = c("Hedges-Olkin", "Simex"),
  tol = 1e-06,
  B = 10,
  lambda = seq(0.25, 5, 0.25),
  seed = 2018
)

`xbeta`	Vector of effects on the index trait
`xse`	Vector of standard errors of `xbeta`
`ybeta`	Vector of effects on the subsequent trait
`yse`	Vector of standard errors of `ybeta`
`weighted`	If true (default), regression of `ybeta` on `xbeta` is weighted tby the inverse of `yse^2`.
`prune`	Vector containing the indices of an approximately independent subset of the predictors in `xbeta` and `ybeta`. If unspecified, all predictors will be used.
`method`	Method to adjust for regression dilution in the regression of `ybeta[prune]` on `xbeta[prune]`. "Hedges-Olkin" applies a quick but approximate correction. "Simex" applies a more time-consuming, but accurate correction with proper allowance for its uncertainty.
`B`	Number of simulations performed in each stage of the Simex adjustment.
`lambda`	Vector of lambdas for which the Simex simulations are performed.
`seed`	Random number seed for the Simex adjustment

Effect sizes are on a linear scale, so could be the coefficients from linear regression, or log odds ratios, or log hazard ratios. Effects on the subsequent trait are regressed on the effects on the index trait. By default, the regression is weighted by the inverse variances of the subsequent trait effects. The regression is adjusted for sampling variation in the index trait effects, and the residuals then used to obtain adjusted effect sizes and standard errors for the subsequent trait.

The regression should be performed on a subset of predictors that are independent. In the context of a genome-wide association study, these would be LD-pruned SNPs. In terms of the input parameters, the regression command is lm(ybeta[prune]~xbeta[prune],weights=1/yse[prune]^2).

The effects in xbeta and ybeta should be aligned for the same variables and the same direction prior to running indexevent.

The default value of B is 10 to get a quick result, but higher values are recommended, eg 1000.

An object of class "indexevent" which contains:

ybeta.adj Adjusted effects on the subsequent trait
yse.adj Adjusted standard errors of ybeta.adj
ychisq.adj Chi-square statistics for (ybeta.adj/yse.adj)^2
yp.adj P-values for ychisq.adj on 1df
b Coefficient of the regression of ybeta[prune] on xbeta[prune], after correction for regression dilution
b.se Standard error of b
b.ci Lower and upper confidence limits for b
b.raw Regression coefficient without correction for regression dilution
simex.estimates Regression coefficients under simulated measurement error

Frank Dudbridge

Dudbridge F, Allen RJ, Sheehan NA, Schmidt AF, Lee JC, Jenkins RG, Wain LV, Hingorani AD, Patel RS (2019) Adjustment for index event bias in genome-wide association studies of subsequent events. Nat Commun 10:1561

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