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Estimate an event stop-rate ratio with exposure offsets

Source: R/rates-baseline.R
sl_rate_ratio.Rd

Fits n ~ ethnicity with log(population-time) offset within each area and other grouping stratum, pooling submitted months. Poisson intervals are exact conditional intervals for two event counts; zero counts are supported without adding pseudocounts. Quasi-Poisson and negative binomial require replicated cells and use model-based log intervals. Dispersion is Pearson chi-square divided by residual degrees of freedom. This is not a person-level risk ratio.

Usage

sl_rate_ratio(
  rates,
  reference = "White",
  comparison = "Black",
  method = c("poisson", "quasipoisson", "negbin"),
  conf_level = 0.95
)

Arguments

rates

Output from sl_rates.

reference, comparison

Self-defined ethnicity groups.

method

Count model family.

conf_level

Sampling confidence level.

Value

An sl_rate_ratio tibble with sampling intervals, dispersion, counts, exposures and fitted model list column, carrying the ingestion contract.

See also

sl_rates(), sl_missing_ethnicity_bounds()

Other rates: sl_counts(), sl_rates()

Examples

r <- readRDS(system.file("extdata", "example-rates.rds",
  package = "searchlight"
))
head(sl_rate_ratio(r))
#> # A tibble: 4 × 18
#>   force_id       geography_code reference comparison ratio conf_low conf_high
#>   <chr>          <chr>          <chr>     <chr>      <dbl>    <dbl>     <dbl>
#> 1 west-yorkshire E02002237      White     Black      0      0           7.45 
#> 2 west-yorkshire E02002454      White     Black      0.201  0.00502     1.16 
#> 3 west-yorkshire E02006875      White     Black      0.281  0.0574      0.833
#> 4 west-yorkshire E02006948      White     Black      0.147  0.0391      0.390
#> # ℹ 11 more variables: conf_level <dbl>, method <chr>, dispersion <dbl>,
#> #   n_reference <int>, n_comparison <int>, exposure_reference <dbl>,
#> #   exposure_comparison <dbl>, estimable <lgl>, model <list>,
#> #   excluded_pair_events <int>, unknown_events <int>