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Get Monte Carlo confidence intervals

Source: R/summarize_uncertainty.R
summarize_uncertainty.Rd

This function obtains a summary of uncertainty (based on central, lower and upper estimates of at least one input variable) using a Monte Carlo simulation.

Input variables that will be processed are:

  • relative_risk (rr_...)

  • exposure (exp_...)

  • cutoff (cutoff_...)

  • baseline health data (bhd_...)

  • disability weight (dw_...)

  • duration (duration_...)

Usage

summarize_uncertainty(output_attribute, n_sim, seed = NULL)

Arguments

output_attribute

variable in which the output of a healthiar::attribute_...() function call are stored.

n_sim

numeric value indicating the number of simulations to be performed.

seed

numeric value for fixing the randomization. Based on it, each geographic unit is assigned a different. If empty, 123 is used as the base seed per default. The function preserves and restores the user's original random seed (if set prior to calling the function) upon function completion.

Value

This function returns a list containing:

1) uncertainty_main (tibble) containing the numeric summary uncertainty central estimate and corresponding lower and upper confidence intervals for the attributable health impacts obtained through Monte Carlo simulation;

2) uncertainty_detailed (list) containing detailed (and interim) results.

  • impact_by_sim (tibble) containing the results for each simulation

  • uncertainty_by_geo_id_micro (tibble) containing results for each geographic unit under analysis (specified in geo_id_micro argument in the preceding attribute_health call)

The two results elements are added to the existing output.

Details

Function arguments seed If the seed argument is specified then the parallel package is used to generate independent L’Ecuyer random number streams. One stream is allocated per variable (or per variable–geography combination, as needed), ensuring reproducible and independent random draws across variables and scenarios.

Methodology

This function summarizes the uncertainty of the attributable health impacts (i.e.a single confidence interval instead of many combinations). For this purpose, it applies a Monte Carlo simulation (Robert and Casella 2004; Rubinstein and Kroese 2016) .

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

References

Robert CP, Casella G (2004). Monte Carlo Statistical Methods, Springer Texts in Statistics. Springer Science & Business Media. doi:10.1007/978-1-4757-4145-2 .

Rubinstein RY, Kroese DP (2016). Simulation and the Monte Carlo Method. John Wiley & Sons. doi:10.1002/9781118631980 .

See also

Author

Alberto Castro & Axel Luyten

Examples

# Goal: obtain summary uncertainty for an existing attribute_health() output
# First create an assessment
attribute_health_output <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369,
  rr_lower = 1.124,
  rr_upper = 1.664,
  rr_increment = 10,
  exp_central = 8.85,
  exp_lower = 8,
  exp_upper = 10,
  cutoff_central = 5,
  bhd_central = 30747,
  bhd_lower = 28000,
  bhd_upper = 32000
)
# Then run Monte Carlo simulation
results <- summarize_uncertainty(
  output_attribute = attribute_health_output,
  n_sim = 100
)
results$uncertainty_main$impact # Central, lower and upper estimates
#> [1] 3444.221 1401.985 5614.813