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Summarise a mixture-cure CFR fit

Source: R/summarise_cfr.R
summary.cfrnow_fit.Rd

Reports the corrected CFR and the onset-to-death delay (mean and sd, in days) as posterior quantiles with convergence diagnostics (rhat, ess_bulk). A fit with a loss_prior also reports the estimated probability of being lost to follow-up, as loss, or as loss_death and loss_recovery when the two outcomes have their own. The naive deaths / cases ratio is returned as an attribute; in real time it underestimates the corrected CFR because not every fatal case has died by the cut-off.

Usage

# S3 method for class 'cfrnow_fit'
summary(
  object,
  probs = c(0.025, 0.5, 0.975),
  info_tol = 0.9,
  ascertainment_ratio = 1,
  ...
)

# S3 method for class 'cfrnow_fit'
print(x, ...)

Arguments

object

A cfrnow_fit from fit_cfr().

probs

Quantiles to report.

info_tol

Low-information threshold: flag when the CFR posterior sd is more than this fraction of the prior sd. Defaults to 0.9.

ascertainment_ratio

Ratio r of the ascertainment probability of fatal to non-fatal cases (see Details). A single positive number; defaults to 1 (no correction).

...

Unused.

x

A cfrnow_fit.

Value

A data frame with one row per quantity: prob (or one prob[<group>] row per group for a prob ~ group fit), delay_mean and delay_sd, recovery_mean and recovery_sd for a two-outcome fit, and loss (or loss_death and loss_recovery) for a fit with an estimated loss_prior. It carries naive_cfr, n_cases, n_deaths, cfr_prior_sd, cfr_low_information and ascertainment_ratio attributes.

Details

When few deaths have resolved (a young outbreak) the CFR is only weakly identified and its posterior stays close to the prior. This is reported via the cfr_low_information attribute: TRUE when the CFR posterior sd exceeds info_tol times the prior sd.

For a prob ~ group fit the CFR varies by group, so one prob[<group>] row is reported per group (grouping predictors must be factors or characters), and the cfr_low_information flag is NA (only defined for a single CFR).

The CFR the model fits is the fatality risk among ascertained cases. When ascertainment is outcome-dependent – fatal and non-fatal cases entering the line list at different rates – this differs from the population CFR. ascertainment_ratio (r) is the ratio of the ascertainment probability of fatal to non-fatal cases; the reported CFR is shifted on the logit scale by -log(r), so r > 1 (fatal cases over-ascertained) lowers it and r < 1 (e.g. deaths not linked back to cases) raises it. It is supplied, not fitted, and defaults to 1 (no correction); because the correction is a post-hoc logit shift, sweep a range of r to show its leverage rather than trusting a single value.

See also

Other fit: fit_cfr(), posterior_cumulative_incidence(), posterior_prob_death(), pp_check_cfr()