mr_divw function

Debiased inverse-variance weighted method

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

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.

Returns

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.

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.

Examples

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

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.