Description Usage Arguments Details Value

View source: R/ehr_simulation.R

Model: proportional hazards, h(t; cov_mat, beta) = exp(cov_mat indicators for Type I censoring (common censoring time 'tc').

1 2 3 |

`cov_mat` |
n x p matrix of cov_matiates |

`beta` |
p-vector of regression coefficients |

`cens_type` |
typeI censoring or non-informative based on exponential distribution |

`baseline_hazard` |
for modelling death dates |

`cens_hazard` |
log(hazard) for non-informative censoring |

`cens_prob` |
expected censoring fraction (0 <= cens_prob < 1). Used for typeI censoring |

`scale` |
value to scale up the time variable by |

`weibull_shape` |
shape parameter for the weibull distribution. 1 is the same as an exponential |

Weibull_shape is the k (shape) parameter from a weibull distribution

A value of k < 1 indicates that the mortality rate decreases over time. This happens if there is significant infant mortality

A value of k = 1 indicates that the mortality rate is constant over time. This might suggest random external events are causing mortality. This is the same as an exponential distribution

A value of k > 1 indicates that the mortality rate increases with time. This happens if there is an aging process.

Censored exponential survival times and censoring

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