| PIVW-class | R Documentation |
An object containing the estimate produced using the penalized inverse-variance weighted (pIVW) method as well as various statistics.
Over.dispersionShould the method consider overdispersion (balanced horizontal pleiotropy)? Default is TRUE.
Boot.FiellerIf Boot.Fieller=TRUE, then the P-value and the confidence interval of the causal effect will be calculated based on the bootstrapping Fieller method. Otherwise, the P-value and the confidence interval of the causal effect will be calculated from the normal distribution. It is recommended to use the bootstrapping Fieller method when Condition (the estimated effective sample size) is smaller than 10. By default, Boot.Fieller=TRUE.
LambdaThe penalty parameter in the pIVW estimator. The penalty parameter plays a role in the bias-variance trade-off of the estimator. It is recommended to choose lambda=1 to achieve the smallest bias and valid inference. By default, lambda=1.
DeltaThe z-score threshold for IV selection. By default, delta=0 (i.e., no IV selection will be conducted).
ExposureThe name of the exposure variable.
OutcomeThe name of the outcome variable.
EstimateThe causal point estimate from the pIVW estimator.
StdErrorThe standard error associated with Estimate.
CILowerThe lower bound of the confidence interval for Estimate, which is derived from the bootstrapping Fieller method or normal distribution. For the bootstrapping Fieller's interval, if it contains multiple ranges, then lower limits of all ranges will be reported.
CIUpperThe upper bound of the confidence interval for Estimate, which is derived from the bootstrapping Fieller method or normal distribution. For the bootstrapping Fieller's interval, if it contains multiple ranges, then upper limits of all ranges will be reported.
AlphaThe significance level used in constructing the confidence interval (default is 0.05).
PvalueP-value associated with the causal estimate from the pIVW estimator.
Tau2The variance of the balanced horizontal pleiotropy. Tau2 is calculated by using all IVs in the data before conducting the IV selection.
SNPsThe number of SNPs after IV selection.
ConditionThe estimated effective sample size. It is recommended to be greater than 5 for the pIVW estimator to achieve reliable asymptotic properties.
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