Description Usage Arguments Details Examples

View source: R/EValue_selection.R

Returns a data frame containing point estimates, the lower confidence limit, and the upper confidence limit for the risk ratio (as provided by the user) as well as selection bias E-values for the point estimate and the confidence interval limit closer to the null.

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`est` |
The point estimate |

`lo` |
The lower limit of the confidence interval |

`hi` |
The upper limit of the confidence interval |

`true` |
The true RR to which to shift the observed point estimate. Typically set to 1 to consider a null true effect. |

`sel_pop` |
Whether inference is specific to selected population (TRUE) or entire population (FALSE). Defaults to FALSE. |

`S_eq_U` |
Whether the unmeasured factor is assumed to be a defining characteristic of the selected population. Defaults to FALSE. |

`risk_inc` |
Whether selection is assumed to be associated with increased risk of the outcome in both exposure groups. Defaults to FALSE. |

`risk_dec` |
Whether selection is assumed to be associated with decreased risk of the outcome in both exposure groups. Defaults to FALSE. |

`...` |
Arguments passed to other methods. |

A selection bias E-value is a summary measure that helps assess
susceptibility of a result to selection bias. Each of one or more parameters
characterizing the extent of the bias must be greater than or equal to this
value to be sufficient to shift an estimate (`est`

) to the null or other
true value (`true`

). The parameters, as defined in Smith and VanderWeele
2019, depend on assumptions an investigator is willing to make (see arguments
`sel_pop`

, `S_eq_U`

, `risk_inc`

, `risk_dec`

). The
`svalues.XX`

functions print a message about which parameters the
selection bias E-value refers to given the assumptions made. See the cited
article for details.

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# Examples from Smith and VanderWeele 2019
# Zika virus example
svalues.RR(est = 73.1, lo = 13.0)
# Endometrial cancer example
svalues.RR(est = 2.30, true = 11.98, S_eq_U = TRUE, risk_inc = TRUE)
# Obesity paradox example
svalues.RR(est = 1.50, lo = 1.22, sel_pop = TRUE)
``` |

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