The marginal model method for epidist_linelist_data objects

This method converts linelist data to a marginal model format by calculating delays between primary and secondary events, along with observation times and censoring windows. The likelihood used is imported from the primarycensored package which handles censoring in both primary and secondary events as well as truncation due to observation times. In principle, this method should be more accurate and more computationally efficient than the latent model (as_epidist_latent_model()) approach in most setting except when the number of unique strata approaches the number of observations.

Usage

# S3 method for class 'epidist_linelist_data'
as_epidist_marginal_model(data, obs_time_threshold = 2, weight = NULL, ...)

Arguments

data

An epidist_linelist_data object

obs_time_threshold

Ratio used to determine threshold for setting relative observation times to Inf. Observation times greater than obs_time_threshold times the maximum delay will be set to Inf to improve model efficiency by reducing the number of unique observation times. Default is 2.

weight

A column name to use for weighting the data in the likelihood. Default is NULL. Internally this is used to define the 'n' column of the returned object.

...

Not used in this method.

Details

When a formula is specified in epidist(), the data will be transformed using epidist_transform_data_model.epidist_marginal_model() to prepare it for model fitting. This transformation summarises the data by counting unique combinations of delays, observation times, censoring windows and any variables in the model formula.

See also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_aggregate_data(), epidist_family_model.epidist_marginal_model(), epidist_formula_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), is_epidist_marginal_model(), new_epidist_marginal_model()

Examples

sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_marginal_model()
#> ℹ No primary event upper bound provided, using the primary event lower bound + 1 day as the assumed upper bound.
#> ℹ No secondary event upper bound provided, using the secondary event lower bound + 1 day as the assumed upper bound.
#> ℹ No observation time column provided, using 2015-09-14 as the observation date (the maximum of the secondary event upper bound).
#> ! Setting 8294 observation times beyond 98 (=2x max delay) to Inf. This
#>   improves model efficiency by reducing unique observation times while
#>   maintaining model accuracy as these times should have negligible impact.
#> # A tibble: 8,358 × 22
#>    ptime_lwr ptime_upr stime_lwr stime_upr obs_time    id   age sex   pdate_lwr 
#>        <dbl>     <dbl>     <dbl>     <dbl>    <dbl> <int> <dbl> <chr> <date>    
#>  1         0         1         5         6      484     1    20 Fema… 2014-05-18
#>  2         2         3         7         8      484     2    42 Fema… 2014-05-20
#>  3         2         3         7         8      484     3    45 Fema… 2014-05-20
#>  4         3         4         8         9      484     4    15 Fema… 2014-05-21
#>  5         3         4         8         9      484     5    19 Fema… 2014-05-21
#>  6         3         4         8         9      484     6    55 Fema… 2014-05-21
#>  7         3         4         8         9      484     7    50 Fema… 2014-05-21
#>  8         4         5         9        10      484     8     8 Fema… 2014-05-22
#>  9         4         5         9        10      484     9    54 Fema… 2014-05-22
#> 10         4         5         9        10      484    10    57 Fema… 2014-05-22
#> # ℹ 8,348 more rows
#> # ℹ 13 more variables: sdate_lwr <date>, district <chr>, chiefdom <chr>,
#> #   pdate_upr <date>, sdate_upr <date>, obs_date <date>, pwindow <dbl>,
#> #   swindow <dbl>, relative_obs_time <dbl>, orig_relative_obs_time <dbl>,
#> #   delay_lwr <dbl>, delay_upr <dbl>, n <dbl>