The meta model method for summary estimates only

Used when no individual level data is available and only the estimates argument is supplied. It takes no primary argument, because summary rows tilt the primary event by the growth_rate metadata of their study rather than by an estimated parameter. Passing one is an error.

Usage

# S3 method for class '`NULL`'
as_epidist_meta_model(data = NULL, estimates = NULL, ...)

Arguments

data

NULL.

estimates

An epidist_estimates_data object of published summary estimates, or NULL.

...

Additional arguments passed to methods.

See also

Other meta_model: as_epidist_meta_model(), as_epidist_meta_model.epidist_aggregate_data(), as_epidist_meta_model.epidist_estimates_data(), as_epidist_meta_model.epidist_linelist_data(), assert_epidist.epidist_meta_model(), epidist_family_model.epidist_meta_model(), epidist_formula_model.epidist_meta_model(), epidist_model_prior.epidist_meta_model(), epidist_newdata.epidist_meta_model(), epidist_transform_data_model.epidist_meta_model(), is_epidist_meta_model(), new_epidist_meta_model()

Examples

estimates <- as_epidist_estimates_data(
  data.frame(
    study = c("A", "A"),
    type = c("mean", "sd"),
    value = c(7.5, 3.6),
    n = c(120, 120),
    relative_obs_time = c(20, 20),
    trunc_adjusted = c(FALSE, FALSE),
    cens_adjusted = c(0, 0)
  )
)
#> ℹ No `pwindow` column supplied, assuming a censoring window of 1 (daily
#>   reporting) for every study.
#> ℹ No `swindow` column supplied, assuming a censoring window of 1 (daily
#>   reporting) for every study.
#> ℹ No trunc_design column supplied, assuming every study that did not adjust for
#>   right truncation followed a cohort with a common observation time rather than
#>   accruing primary events up to a calendar collection stop.
#> ℹ No max_delay column supplied, using the delay beyond which 1% of the second
#>   moment of a lognormal matched to each study's summaries lies (at least 10 and
#>   at most twenty times the largest reported value, in whole secondary windows)
#>   as the grid cutoff, or five times the largest reported value where nothing
#>   can be matched. Raise it if the delay has a longer tail than that, and lower
#>   it to speed up fitting.
as_epidist_meta_model(estimates = estimates)
#> # A tibble: 1 × 20
#>   delay_lwr     n obs_type study_n trunc_adjusted trunc_design cens_adjusted
#>       <int> <dbl>    <int>   <int>          <int>        <int>         <int>
#> 1         0     1        5     120              0            0             0
#> # ℹ 13 more variables: group_start <int>, group_len <int>, chol_start <int>,
#> #   n_quad <int>, relative_obs_time <dbl>, pwindow <dbl>, swindow <dbl>,
#> #   delay_upr <dbl>, delay_min <dbl>, report_se <dbl>, quantile_p <dbl>,
#> #   growth_rate <dbl>, study <chr>