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 on the risk ratio scale (through an approximate conversion if needed when outcome is common) as well as E-values for the point estimate and the confidence interval limit closer to the null.

1 2 3 4 5 6 7 8 9 10 11 |

`est` |
The point estimate: a risk, odds, or hazard ratio. An object of
class "estimate", it should be constructed with functions |

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

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

`true` |
The true value 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 function prints a
message about which parameters the selection bias E-value refers to given
the assumptions made. See the cited article for details.

1 2 3 4 5 6 7 8 9 10 | ```
# Examples from Smith and VanderWeele 2019
# Zika virus example
selection_evalue(OR(73.1, rare = TRUE), lo = 13.0)
# Endometrial cancer example
selection_evalue(OR(2.30, rare = TRUE), true = 11.98, S_eq_U = TRUE, risk_inc = TRUE)
# Obesity paradox example
selection_evalue(RR(1.50), lo = 1.22, sel_pop = TRUE)
``` |

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