https://github.com/finnishcancerregistry/directadjusting

Compute estimates of weighted averages with confidence intervals.
https://github.com/finnishcancerregistry/directadjusting

biostatistics confidence-intervals data-analysis direct-adjusting direct-adjustment epidemiology health-statistics r r-package statistical-adjusting statistical-adjustment weighted-analysis weighted-average weighted-averages

Last synced: about 1 year ago
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Compute estimates of weighted averages with confidence intervals.

• Host: GitHub
• URL: https://github.com/finnishcancerregistry/directadjusting
• Owner: FinnishCancerRegistry
• License: other
• Created: 2019-08-21T18:08:25.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-12-12T10:26:05.000Z (over 1 year ago)
• Last Synced: 2025-01-31T14:38:05.540Z (over 1 year ago)
• Topics: biostatistics, confidence-intervals, data-analysis, direct-adjusting, direct-adjustment, epidemiology, health-statistics, r, r-package, statistical-adjusting, statistical-adjustment, weighted-analysis, weighted-average, weighted-averages
• Language: R
• Homepage:
• Size: 67.4 KB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


# Package `directadjusting`

Compute estimates and confidence intervals of weighted averages quickly and easily.

[![R-CMD-check](https://github.com/FinnishCancerRegistry/directadjusting/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/FinnishCancerRegistry/directadjusting/actions/workflows/R-CMD-check.yaml)

# Recommended installation

```r

devtools::install_github(

  "FinnishCancerRegistry/directadjusting",

  ref = readline("enter latest tag on github: ")

)

```

# Example

```r

library("directadjusting")

# suppose we have poisson rates that we want to adjust for by age group.

# they are stratified by sex.

library("data.table")

set.seed(1337)

offsets <- rnorm(8, mean = 1000, sd = 100)

baseline <- 100

sex_hrs <- rep(1:2, each = 4)

age_group_hrs <- rep(c(0.75, 0.90, 1.10, 1.25), times = 2)

counts <- rpois(8, baseline * sex_hrs * age_group_hrs)

# raw estimates

my_stats <- data.table(

  sex = rep(1:2, each = 4),

  ag = rep(1:4, times = 2),

  e = counts / offsets

)

my_stats[["v"]] <- my_stats[["e"]] / offsets

# adjusted by age group

my_adj_stats <- direct_adjusted_estimates(

  stats_dt = my_stats,

  stat_col_nms = "e",

  var_col_nms = "v",

  conf_lvls = 0.95,

  conf_methods = "log",

  stratum_col_nms = "sex",

  adjust_col_nms = "ag",

  weights = c(200, 300, 400, 100)

)

```