Description Usage Arguments Value References Examples

A key feature of `robust_outcome`

is its resilience to the misspecification
of propensity scores, which is a major limitation of classical
modified outcome approaches.
Except for `shifted_outcome`

, all of
the modified outcome approaches belong to a parameterized class of unbiased
estimators for the risk difference term
*E[Y|A=1,X]-E[Y|A=0,X]*.
Within that class, robust modified outcome is the approach with the least
large-sample variance. For more details about this approach, see Lunceford
and Davidian (2004)

1 | ```
robust_outcome(A, X, Y, propensity, parallel = FALSE, ...)
``` |

`A` |
target variant |

`X` |
rest of the genotype |

`Y` |
phenotype |

`propensity` |
propensity scores |

`parallel` |
whether to perform support estimation in a parallelized fashion |

`...` |
additional arguments to be passed to |

a vector containing the area under the stability selection path for
each variable in `X`

Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine, 23(19), 2937–2960.

1 2 3 4 5 6 7 8 9 | ```
n <- 30
p <- 10
X <- matrix((runif(n * p) < 0.4) + (runif(n * p) < 0.4),
ncol = p, nrow = n) # SNP matrix
A <- rbinom(n, 1, 0.3)
propensity <- runif(n, min = 0.4, max = 0.8)
Y <- runif(n) < 0.4
robust_scores <- robust_outcome(A, X, Y, propensity,
lambda_min_ratio = 0.01 , n_subsample = 1)
``` |

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