mr_divw: Debiased inverse-variance weighted method

mr_divwR Documentation

Debiased inverse-variance weighted method

Description

The mr_divw function implements the debiased inverse-variance weighted method.

Usage

mr_divw(object, over.dispersion = TRUE, alpha = 0.05, diagnostics = FALSE)

## S4 method for signature 'MRInput'
mr_divw(object, over.dispersion = TRUE, alpha = 0.05, diagnostics = FALSE)

Arguments

object

An MRInput object.

over.dispersion

Should the method consider overdispersion (balanced horizontal pleiotropy)? Default is TRUE.

alpha

The significance level used to calculate the confidence intervals. The default value is 0.05.

diagnostics

Should the function returns the q-q plot for assumption diagnosis. Default is FALSE.

Details

The debiased inverse-variance weighted method (dIVW) removes the weak instrument bias of the IVW method and is more robust under many weak instruments.

Value

The output from the function is a DIVW object containing:

Over.dispersion

TRUE if the method has considered balanced horizontal pleiotropy, FALSE otherwise.

Exposure

A character string giving the name given to the exposure.

Outcome

A character string giving the name given to the outcome.

Estimate

The value of the causal estimate.

StdError

Standard error of the causal estimate calculated using bootstrapping.

CILower

The lower bound for the causal estimate based on the estimated standard error and the significance level provided.

CIUpper

The upper bound for the causal estimate based on the estimated standard error and the significance level provided.

Alpha

The significance level used when calculating the confidence intervals.

Pvalue

The p-value associated with the estimate (calculated using Estimate/StdError as per a Wald test) using a normal distribution.

SNPs

The number of genetic variants (SNPs) included in the analysis.

Condition

A measure (average F-statistic -1)*sqrt(# snps) that needs to be large for reliable asymptotic approximation based on the dIVW estimator. It is recommended to be greater than 20.

References

Ting Ye, Jun Shao, Hyunseung Kang (2021). Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization. The Annals of Statistics, 49(4), 2079-2100. Also available at https://arxiv.org/abs/1911.09802.

Examples

mr_divw(mr_input(bx = ldlc, bxse = ldlcse, by = chdlodds, byse = chdloddsse))


MendelianRandomization documentation built on May 29, 2024, 11:36 a.m.