View source: R/impact_fraction.R

if_bruzzi | R Documentation |

Internal: Calculation of an impact fraction using the Bruzzi approach

if_bruzzi(data, ind, model, model_type, new_data, response, weight_vec)

`data` |
A dataframe containing variables used for fitting the model |

`ind` |
An indicator of which rows will be used from the dataset |

`model` |
Either a clogit or glm fitted model object. Non-linear effects should be specified via ns(x, df=y), where ns is the natural spline function from the splines library. |

`model_type` |
Either a "clogit", "glm" or "coxph" model object |

`new_data` |
A dataframe (of the same variables and size as data) representing an alternative distribution of risk factors |

`response` |
A string representing the name of the outcome variable in data |

`weight_vec` |
An optional vector of inverse sampling weights |

A numeric estimated impact fraction.

Bruzzi, P., Green, S.B., Byar, D.P., Brinton, L.A. and Schairer, C., 1985. Estimating the population attributable risk for multiple risk factors using case-control data. American journal of epidemiology, 122(5), pp.904-914

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