Update prevalence using new case prevalence rates

Updated prevalence and confidence intervals using new case prevalence rates

update_prevalence(
  pi0_new,
  x,
  gamma = 0.05,
  print = NULL,
  plot = NULL,
  col_line = "#2e5dc1",
  col_ci = "#2E5DC133",
  ...
)

Arguments

pi0_new

A numeric or vector of new case prevalence rates

x

A cpreval object.

gamma

A numeric that used to compute a (1 - gamma) confidence region for the proportion. Default value is 0.05.

print

A boolean indicating whether or not the output should be print.

plot

A boolean indicating whether or not a plot should be made.

col_line

Color of the estimated prevalence.

col_ci

Color of the estimated prevalence confidence interval.

...

Additional arguments.

Value

A matrix object whose colunms corresponds to pi0, estimate, sd and CI.

Author

Stephane Guerrier

Examples

# Austrian data (November 2020)
pi0 = 93914/7166167
data("covid19_austria")

# Weighted sampling
n = nrow(covid19_austria)
R1w = sum(covid19_austria$weights[covid19_austria$Y == 1 & covid19_austria$Z == 1])
R2w = sum(covid19_austria$weights[covid19_austria$Y == 0 & covid19_austria$Z == 1])
R3w = sum(covid19_austria$weights[covid19_austria$Y == 1 & covid19_austria$Z == 0])
R4w = sum(covid19_austria$weights[covid19_austria$Y == 0 & covid19_austria$Z == 0])

# Assumed measurement errors
alpha0 = 0
alpha = 1/100
beta = 10/100

# MME
mme = moment_estimator(R3 = R3w, n = n, pi0 = pi0, alpha = alpha, beta = beta,
                       alpha0 = alpha0, V = mean(covid19_austria$weights^2))

mme
#> Method: Moment Estimator
#> 
#> Estimated proportion: 2.0794%
#> Standard error      : 0.3699%
#> 
#> Confidence interval at the 95% level:
#> Asymptotic Approach: 1.3544% - 2.8045%
#> 
#> Assumed measurement error: alpha  = 1%, beta = 10%,
#>                            alpha0 = 0% 
#> 
#> Estimated false negative rate of the
#> official procedure: beta0 = 36.98%
#> CI at the 95% level: 15.00% - 58.95%
#> 
#> Estimated ascertainment rate: 
#> pi0/pi = 63.02%
#> CI at the 95% level: 41.05% - 85.00%
#> 
#> Sampling: Stratified with V = 1.51

# Update prevalence using a new pi0, say = 1.5%, instead of 1.31%
update_prevalence(1.5/100, mme)
#> Estimated proportion: 2.3801%
#> Standard error      : 0.4846%
#> Confidence interval at the 95% level: 1.4303% - 3.3299%

pi0_new = seq(from = 0.005, to = 0.03, length.out = 100)
update_prevalence(pi0_new, mme)