AER: Absolute Excess Risk (AER)

Description

Computes the AER, its confidence interval and its associated p-value

Usage

AER(
  futime,
  status,
  age,
  sex,
  entry_date,
  PY.stand = 10000,
  ratetable = survexp.fr::survexp.fr,
  alpha = 0.05
)

Value

A list containing the AER with the corresponding number of person-years (PY.stand argument), its confidence interval, its p-value, the observed number of deaths, the expected number of deaths and the observed number of person-years

Arguments

futime: follow-up time of the subjects in days
status: 0 if censored or 1 if dead at futime
age: age in days
sex: "male" or "female"
entry_date: entry date in the study
PY.stand: value to get the AER for stand person-years
ratetable: a table of event rates, such as survexp.fr or survexp.us
alpha: determines the confidence level (1-alpha) of the confidence interval

Author

Jean-Philippe Jais and Hugo Varet

Details

The Absolute Excess Risk (AER) is defined as: $$AER = O-E$$ where $O$ is the observed number of deaths and $E$ is the expected number based on the patients'characteristics (sex, age and entry date in the study). This function uses an additive Poisson model to compute the AER.

References

N. Breslow and N. Day, Statistical methods in cancer research, Volume II - The design and analysis of cohort studies, World Health Organization, 1987

P. Dickman, A. Sloggett, M. Hills and T. Hakulinen, Regression models for relative survival, Statistics in Medicine, 2004

C. Elie, Y. De Rycke, J.-P. Jais and P. Landais, Appraising relative and excess mortality in population-based studies of chronic diseases such as end-stage renal disease, Clinical Epidemiology, 2011

Examples

Run this code

attach(data.example)
AER(futime, status, age, sex, entry_date)

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