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 settings 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,
  delay_min = 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 containing counts of identical linelist items. When specified, the user is declaring that rows with the same values represent the same observation occurring multiple times. This allows for efficient data representation by storing unique patterns with their counts rather than repeating identical rows. The marginal model will further aggregate these counts based on the formula specification. Default is NULL, which assigns a count of 1 to each row. Internally this is used to define the 'n' column of the returned object.

delay_min

Minimum delay (left truncation point). Can be:

• NULL (default): uses a delay_min column from the data if present, otherwise defaults to 0 (no left truncation).

• A numeric scalar: applied to all observations.

• A column name string: looks up the named column in the data. This is passed as the L parameter to primarycensored::dpcens().

...

Not used in this method.

Value

An object of class epidist_marginal_model.

Details

The marginal model performs internal aggregation to optimize computational efficiency while preserving all statistical information. If your data already contains repeated observations with identical characteristics, you can use the weight parameter to provide counts of these duplicates. This allows for more efficient data representation without any loss of information.

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_newdata.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 relative observation times (`relative_obs_time`) greater than 98
#>   (2x the maximum delay) to Inf.
#> ℹ This improves model efficiency by reducing the number of unique observation
#>   times in the data.
#> ℹ The impact on model accuracy should be negligible because these relative
#>   observation times are high enough to cause very limited right truncation.
#> ℹ The original relative observation times are available in
#>   `orig_relative_obs_time`.
#> ℹ Raise `obs_time_threshold` to avoid this behaviour.
#> # A tibble: 8,358 × 23
#>    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 14 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>, delay_min <dbl>