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Report a study that published summaries of its delays

Source: R/estimates_shapes.R
epidist_estimates_summaries.Rd

Builds the rows as_epidist_meta_model() takes from the summaries a study published in wide form, one argument per kind of summary.

Usage

epidist_estimates_summaries(
  study,
  mean = NULL,
  sd = NULL,
  quantiles = NULL,
  probs = NULL,
  se = NULL,
  n = NULL,
  ...
)

Arguments

study

A string naming the study.

mean

The reported mean delay. Optional.

sd

The reported standard deviation of the delays. Optional.

quantiles

A numeric vector of reported quantiles. Optional.

probs

The probabilities of quantiles, in the same order. Required where quantiles is given.

se

A numeric vector of the reported standard errors of the summaries, ordered mean, standard deviation, then quantiles, skipping any that was not reported. Optional.

n

The number of delays the study summarised. Optional.

...

Study metadata, as documented in as_epidist_estimates_data.data.frame().

Value

An epidist_estimates_data object.

Details

Give the uncertainty of each summary through se, or the number of delays the study summarised through n, which the model uses to derive a sampling uncertainty instead. One of the two is needed for every row.

See also

Other estimates_data: as_epidist_estimates_data(), as_epidist_estimates_data.data.frame(), as_epidist_estimates_data.epidist_estimates_data(), as_epidist_estimates_data.epidist_multivariate(), as_epidist_estimates_data.list(), assert_epidist.epidist_estimates_data(), epidist_estimates_parameters(), is_epidist_estimates_data(), new_epidist_estimates_data()

Examples

epidist_estimates_summaries(
  "study A",
  mean = 7.5, sd = 3.6, n = 120,
  relative_obs_time = 20, trunc_adjusted = FALSE, cens_adjusted = 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.
#> # A tibble: 2 × 16
#>   study   type  value    se     n     p pwindow swindow relative_obs_time
#>   <chr>   <chr> <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>             <dbl>
#> 1 study A mean    7.5    NA   120    NA       1       1                20
#> 2 study A sd      3.6    NA   120    NA       1       1                20
#> # ℹ 7 more variables: trunc_adjusted <lgl>, trunc_design <chr>,
#> #   cens_adjusted <int>, delay_min <dbl>, growth_rate <dbl>, max_delay <dbl>,
#> #   mvn_id <chr>