RCMsize: Sample Size Calculation in Reversible Catalytic Models

Description

Sample size and confidence interval calculations in reversible catalytic models, with applications in malaria research. Further details can be found in the paper by Sepúlveda and Drakeley (2015, doi:10.1186/s12936-015-0661-z).

Author(s)

Maintainer: Márcia Graça marcia_da_graca@hotmail.com

Authors:

• Fernanda Diamantino [thesis advisor]

• Nuno Sepúlveda [contributor, thesis advisor]

See Also

Useful links:

• https://CRAN.R-project.org/package=RCMsize

• Report bugs at https://github.com/marciagraca/RCMsize/issues

Confidence Interval for the Seroconversion Rate (SCR)

Description

Calculates the confidence interval for the seroconversion rate (SCR) using the confidence interval of seroprevalence.

Usage

IC_SCR(SP_interval, SRR, ages, A_max, limits = c(0, 1))

Arguments

Value

A vector with the lower and upper limits for the seroconversion rate SCR.

Examples

A_max <- 80
age_distribution <- rep(1 / A_max, A_max)
IC_SCR(c(0.1, 0.2), 0.01, age_distribution, A_max, limits = c(0, 1))

Confidence Interval for Seroprevalence

Description

Calculates the confidence interval for a seroprevalence estimate with a specified confidence level.

Usage

IC_SP(SP, n, conf.level = 0.95, method = "asymptotic")

Arguments

Value

A vector with the lower and upper limits of the confidence interval.

References

The methods available in this function are some of the available in the binom package. For more information, see https://CRAN.R-project.org/package=binom

Examples

IC_SP(0.25, 100, conf.level = 0.95, method = "asymptotic")

Confidence Interval for Seroprevalence with Continuity Correction (Wald Method)

Description

Calculates the confidence interval for seroprevalence using the Wald method with continuity correction.

Usage

IC_SP_Waldcc(SP, n, conf.level = 0.95)

Arguments

Value

A vector with the lower and upper limits of the confidence interval.

Examples

IC_SP_Waldcc(0.25, 100, conf.level = 0.95)

Calculation of Seropositivity Probability

Description

This function calculates the probability of seropositivity based on the age and the seroconversion and seroreversion rates, using a reversible catalytic model.

Usage

prob_seropositive(SCR, SRR, t)

Arguments

Value

The probability of seropositivity for age 't'.

References

For more information on the reversible catalytic model, see https://link.springer.com/article/10.1186/s12936-015-0661-z

Examples

prob_seropositive(0.03, 0.01, 45)

Sample Size Calculation

Description

Estimates the required sample size so that the confidence interval width for SCR does not exceed a specified limit.

Usage

sample_s(
  SCR,
  RL,
  SRR,
  ages,
  A_max,
  limits,
  max_iter = 10000,
  conf.level = 0.95,
  method = "asymptotic"
)

Arguments

Details

**Disclaimer**: The sample size function may not produce accurate values for scenarios involving extremely low SCR (e.g., elimination scenarios). Users are advised to exercise caution and consider the results critically when applying this function to such cases.

Value

A list with the required sample size, the confidence interval for seroprevalence, and the confidence interval for SCR.

Examples

A_max <- 80
age_distribution <- rep(1 / A_max, A_max)
sample_s(0.03, 1, 0.01, age_distribution, A_max, limits = c(0, 1))

Seroprevalence Calculation

Description

Calculates the seroprevalence considering an age distribution and a reversible catalytic model.

Usage

seroprevalence(ages, A_max, SCR, SRR)

Arguments

Value

The total seroprevalence weighted by the age distribution.

Examples

A_max <- 80
age_distribution <- rep(1 / A_max, A_max)
seroprevalence(age_distribution, A_max, 0.03, 0.01)