Debiased inverse-variance weighted method — mr

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 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.

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.

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.

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))
#> 
#> Debiased inverse-variance weighted method
#> (Over.dispersion:TRUE)
#> 
#> Number of Variants : 28 
#> ------------------------------------------------------------------
#>  Method Estimate Std Error 95% CI       p-value Condition
#>    dIVW    2.940     0.531 1.900, 3.980   0.000   142.920
#> ------------------------------------------------------------------