Description Usage Arguments Value Note References Examples

View source: R/direct_adjust.R

Compute age-adjusted rates using direct standardization.

1 2 3 4 5 6 7 8 9 10 | ```
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

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# 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$seer_pop))
# same rates by year
cancer_by_year <- cancer %>%
group_by(Year, agegroup) %>%
summarize(n = sum(n), pop = sum(pop)) %>%
group_modify(~ direct_adjust(.x, agegroup, n, pop, std_pop_list$seer_pop))
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.