popEpi: Functions for Epidemiological Analysis using Population Data

pophaz

R Documentation

Expected / Population Hazard Data Sets Usage in popEpi

Description

Several functions in popEpi make use of population or expected hazards in computing the intended estimates (e.g. survtab). This document explains using such data sets in this package.

Details

Population hazard data sets (pophaz for short) in popEpi should be data.frames in the "long" format where one of the columns must be named haz (for hazard), and other columns define the values or levels in variables relating to subjects in your data. For example, popmort contains Finnish population mortality hazards by sex, calendar year, and 1-year age group.

`sex`	`year`	`agegroup`	`haz`
0	1951	0	0.036363176
0	1951	1	0.003616547
0	1951	2	0.002172384
0	1951	3	0.001581249
0	1951	4	0.001180690
0	1951	5	0.001070595

The names of the columns should match to the names of the variables that you have in your subject-level data. Time variables in your pophaz may also correspond to Lexis time scales; see survtab.

Any time variables (as they usually have) should be coded consistently: When using fractional years in your data, the time variables in your pophaz must also be coded in fractional years. When using e.g. Dates in your data, ensure that the pophaz time variables are coded at the level of days (or Dates for calendar time).

The haz variable in your pophaz should also be coded consistently with the used time variables. E.g. haz values in life-tables reported as deaths per person-year should be multiplied by 365.25 when using day-level time variables. Typically you'll have calendar time and age expressed in years, which means haz should be expressed as the number of deaths per person-year.

If you have your population hazards in a ratetable object usable by functions in survival and relsurv, you may transform them to long-format data.frames using ratetable_to_long_dt. Ensure, however, that the created haz column is coded at the right level (events per days or years typically).

National statistical institutions, the WHO, and e.g. the Human Life-Table Database supply life-table data.