The mr_clr function calculates confidence intervals based on inverting the conditional likelihood ratio and other identification-robust tests.
Usage
mr_clr(object, nx, ny, alpha = 0.05, CIMin = -10, CIMax = 10, CIStep = 0.01)
# S4 method for MRInput
mr_clr(object, nx, ny, alpha = 0.05, CIMin = -10, CIMax = 10, CIStep = 0.01)Arguments
- object
An
MRInputobject.- nx
The sample size used to compute genetic associations with the exposure.
- ny
The sample size used to compute genetic associations with the outcome.
- alpha
The significance level used to calculate the confidence interval. The default value is 0.05.
- CIMin
The smallest value to use in the search to find the confidence interval (default is -10).
- CIMax
The largest value to use in the search to find the confidence interval (default is +10).
- CIStep
The step size to use in the search to find the confidence interval (default is 0.01). Using a lower value (such as 0.001) will give more precise confidence intervals, but increase run time.
Value
The output from the function is an CLR object containing:
- Exposure
A character string with the name given to the exposure.
- Outcome
A character string with the names given to the outcome.
- Correlation
The matrix of genetic correlations.
- ARlower
The lower bounds of the causal estimate based on inverting Anderson and Rubin's test.
- ARupper
The upper bounds of the causal estimate based on inverting Anderson and Rubin's test.
- Klower
The lower bounds of the causal estimate based on inverting Kleibergen's test.
- Kupper
The upper bounds of the causal estimate based on inverting Kleibergen's test.
- CLRlower
The lower bounds of the causal estimate based on inverting Moreira's conditional likelihood ratio test.
- CLRupper
The upper bounds of the causal estimate based on inverting Moreira's conditional likelihood ratio test.
- CIMin
The smallest value used in the search to find the confidence interval.
- CIMax
The largest value used in the search to find the confidence interval.
- CIStep
The step size used in the search to find the confidence interval.
- Alpha
The significance level used when calculating the confidence intervals.
Details
In weak instrument settings, usual inference based on point estimates and standard errors may not be accurate. This method calculates confidence intervals based on inverting identification-robust tests proposed in Wang and Kang (2021, Biometrics) that provide valid inferences regardless of instrument strength.
This includes conditional likelihood ratio (CLR), Kleibergen (K), and Anderson and Rubin (AR) tests.
Evidence from the econometrics literature suggests that CLR inference is the best option in terms of power under a wide range of settings.
Please note that these methods do not provide point estimates, only confidence intervals. While most examples provide a confidence interval that is a single range of values, in some cases the confidence interval may comprise multiple ranges of values. In other cases, a valid confidence interval may not exist.
References
Description of the CLR method: "Weak-instrument robust tests in two-sample summary-data Mendelian randomization", S. Wang and H. Kang, Biometrics, 2021.
Examples
mr_clr(mr_input(bx = calcium, bxse = calciumse,
by = fastgluc, byse = fastglucse, correl = calc.rho), nx=6351, ny=133010)
#>
#> --------------------------------------------------------------------------
#>
#> 95 % confidence intervals using identification-robust methods
#>
#> Anderson--Rubin: [ 0.16 , 5.30 ]
#> Kleibergen: [ 0.98 , 3.88 ]
#> Conditional likelihood ratio: [ 0.96 , 3.92 ]
#> --------------------------------------------------------------------------