View source: R/direct_adjust.R
direct_adjust | R Documentation |
Compute age-adjusted rates using direct standardization.
direct_adjust( df, agegroup, events, person_yrs, std_pop, base = 1e+05, level = 95, decimals = 4 )
df |
A data frame with columns for age group, event counts, and person-years totals as described in the next three arguments |
agegroup |
Age group or other stratifying variable. |
events |
Number of events. |
person_yrs |
Number of person-years at risk. |
std_pop |
Vector of standard population distribution. Can be totals, proportions, or percentages. |
base |
Multiplier; e.g. per 100,000 population. |
level |
Confidence level expressed as percentage. |
decimals |
Decimal places to round results. |
A data table with the following fields:
events
Number of events.
person_yrs
Total person-years at risk.
adj_rate
Age-adjusted rate.
adj_rate_stderr
Standard error of age-adjusted rate.
adj_lci
Lower confidence limit of age-adjusted rate per Tiwari (2006)
adj_uci
Upper confidence limit for age-adusted rate per Tiwari.
crude_rate
Crude (unadjusted) rate.
crude_lci
Lower confidence limit for crude rate, per Garwood (1936).
crude_uci
Upper confidence limit for crude rate, per Garwood.
Confidence limits for adjusted rates are computed using the method of Tiwari et al. (2006). The upper limit is adjusted with a continuity correction prompted by the use of a continuous distribution (gamma) to approximate a discrete random variable (Poisson).
Confidence limits for crude rates are copmuted using the method of Garwood (1936).
Anderson RN and Rosenberg HM (1998) Age standardization of death rates: Implementation of the year 2000 standard. National Vital Statistics Reports 47(3). Hyattsville, Maryland: National Center for Health Statistics. https://www.cdc.gov/nchs/data/nvsr/nvsr47/nvs47_03.pdf
Garwood F (1936) Fiducial limits for the Poisson distribution, Biometrika 28:437-442.
Tiwari RC et al. (2006) Efficient interval estimation for age-adjusted cancer rates. Statistical Methods in Medical Research 15:547-569. https://www.ncbi.nlm.nih.gov/pubmed/17260923
# US age-adjusted cancer rates by year and sex # using standard SEER age groups 0, 1-4, 5-9, 10-14, 15-19, ..., 80-84, library(dplyr) cancer_by_year_sex <- cancer %>% group_by(Year, Sex) %>% group_modify(~ direct_adjust(.x, agegroup, n, pop, std_pop_list$seer_pop)) # same rates by year cancer_by_year <- cancer %>% group_by(Year, agegroup) %>% summarize(across(c(n, pop), sum)) %>% group_modify(~ direct_adjust(.x, agegroup, n, pop, std_pop_list$seer_pop))
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