incrate: Age standardized rate analysis
In buddha2490/BERG: A set of standardized functions for ACS BERG analyses

Description Usage Arguments Details Value Author(s) See Also Examples

This function calculates age-standardized incidence and mortality rates for CPSII Mortality and Nutrition Cohorts analyses.

1 2	incrate(dat=df, agedist, agegrps=NULL, dtint, birthday, failtime, outcome, expo)

`dat`	data.frame used in the analysis (default is "df" for use in modeling functions)
`agedist`	Age distribution standard (see nutweightlist documentation for details)
`agegrps`	Vector of start ages for the analysis. Default is 5-year age groups starting at age 40-44, 45-59,... 85+. To collapse ages, provide a vector of the ages you want each group to start with. For example, if you wanted to use 10-year age groups you would provide: c(40,50,60,70,80) to create age groups 40-49, 50-59, 60-69, 70-79, 80+. For 2 age groups (<65 and 65+) you would provide the vector c(40,65).
`dtint`	Variable name for the start of followup. Example: "dtint92", "dtint97", etc.
`birthday`	Variable name exact birthdate, typically "BDAYDATE" in our master file.
`failtime`	Variable name for fail time in days
`outcome`	Variable name for your outcome, formatted 0=control, 1=case
`expo`	Factor variable for your exposure.

These rates are calculated using the pyrs() function in the survival package. Age-specific time and events are tabulated as each CPSII participant moves through calendar time. For example, if a participant enrolls in the study at age 49, they will contribute person time to the age 45-49 age group until their 50th birthday, and then start contributing person time to the 50-54 age group. Events are assessed at this attained age.

The pyrs() function simply calculated age- and strata-specific person years and events. The rest of the function calculates the age-adjustment and final rates.

The function returns a list of age-specific person-years and rates for each exposure level of your variable. Also includes:

Std.Rates

Final age-standardized rates calculated for each exposure level

Brian Carter

weightlist

df <- example.data
df$failtime <- as.numeric(df$dateft)-as.numeric(df$dtint92)
rates <- incrate(dat=df,
                 agedist="YEARS00",
                 agegrps=c(40,65),
                 dtint="dtint92",
                 birthday="bdaydate",
                 failtime="df$failtime",
                 outcome="dead",
                 expo="smoke92")

print(rates$Std.Rate)