mr_divw: Debiased inverse-variance weighted method

In MendelianRandomization: Mendelian Randomization Package

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 Aug. 9, 2023, 1:05 a.m.