CIpvBayes {bdpv} | R Documentation |

Computes confidence intervals for negative and positive predictive values by simulation from the posterior beta-distribution (Stamey and Holt, 2010), assuming a case-control design to estimate sensitivity and specificity, while prevalence estimates of an external study and/or prior knowledge concerning prevalence may be introduced additionally.

CIpvBI(x1, x0, pr, conf.level = 0.95, alternative = c("two.sided", "less", "greater"), B=5000, shapes1=c(1,1), shapes0=c(1,1), ...) CIpvBII(x1, x0, xpr, conf.level = 0.95, alternative = c("two.sided", "less", "greater"), B=5000, shapes1=c(1,1), shapes0=c(1,1), shapespr=c(1,1), ...)

`x1` |
A vector of two (integer) values, specifying the observed number of positive ( |

`x0` |
A vector of two (integer) values, specifying the observed number of positive ( |

`pr` |
A single numeric value between 0 and 1, defining an assumed fixed (known) prevalence (for |

`xpr` |
An optional vector of two (integer) values, specifying the observed number of positive ( |

`conf.level` |
The confidence level, a single numeric value between 0 and 1, defaults to 0.95 |

`alternative` |
A character string specifying whether two-sided ( |

`B` |
A single integer, the number of samples from the posterior to be drawn. |

`shapes1` |
Two positive numbers, the shape parameters (a,b) of the beta prior for the sensitivity, by default a flat beta prior (a=1, b=1) is used. |

`shapes0` |
Two positive numbers, the shape parameters (a,b) of the beta prior for (1-specificity), by default a flat beta prior (a=1, b=1) is used. Note, that this definition differs from that in Stamey and Holt(2010), where the prior is defined for the specificity directly. |

`shapespr` |
Two positive numbers, the shape parameters (a,b) of the beta prior for the prevalence, by default a flat beta prior (a=1, b=1) is used. For |

`...` |
Arguments to be passed to |

`CIpvBI`

implements the method refered to as Bayes I in Stamey and Holt (2010), `CIpvBI`

implements the method refered to as Bayes II in Stamey and Holt (2010), Equation (2) and following description (p. 103-104).

A list with elements

`conf.int ` |
the confidence bounds |

`estimate ` |
the point estimate |

`tab ` |
a 2x2 matrix showing how the input data in terms of true positives and true negatives |

Frank Schaarschmidt

*Stamey JD and Holt MM (2010).* Bayesian interval estimation for predictive values for case-control studies. Communications in Statistics - Simulation and Computation. 39:1, 101-110.

# example data: Stamey and Holt, Table 8 (page 108) # Diseased # Test D=1 D=0 # T=1 240 87 # T=0 178 288 #n1,n0: 418 375 # reproduce the results for the Bayes I method # in Stamey and Holt (2010), Table 9, page 108 # assuming known prevalence 0.03 # ppv 0.0591, 0.0860 # npv 0.9810, 0.9850 CIpvBI( x1=c(240,178), x0=c(87,288), pr=0.03) # assuming known prevalence 0.04 # ppv 0.0779, 0.1111 # npv 0.9745, 0.9800 CIpvBI( x1=c(240,178), x0=c(87,288), pr=0.04) # compare with standard logit intervals tab <- cbind( x1=c(240,178), x0=c(87,288)) tab BDtest(tab, pr=0.03) BDtest(tab, pr=0.04) # reproduce the results for the Bayes II method # in Stamey and Holt (2010), Table 9, page 108 CIpvBII( x1=c(240,178), x0=c(87,288), shapespr=c(16,486)) CIpvBII( x1=c(240,178), x0=c(87,288), shapespr=c(21,481))

[Package *bdpv* version 1.3 Index]