Cargo packages

library("tidyverse")

Loading tidyverse: ggplot2
Loading tidyverse: tibble
Loading tidyverse: tidyr
Loading tidyverse: readr
Loading tidyverse: purrr
package 'ggplot2' was built under R version 3.4.4Conflicts with tidy packages -------------------------------------------------------------------------
filter(): dplyr, stats
lag():    dplyr, stats

Abro .csv de plantilla internet

kappa <- read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vTjtxklEjstcGZumUaxWWb9hq6G6aaaFc5gIEwnfmo4pE0XdIMo0bHcEXJT96IfvLhmlAKSgiIPGTAm/pub?gid=25532625&single=true&output=csv")

El kappa (el primero)

kappam.fleiss(kappa, detail=TRUE)

 Fleiss' Kappa for m Raters

 Subjects = 20 
   Raters = 3 
    Kappa = 0.41 

        z = 4.82 
  p-value = 1.44e-06 

  Kappa     z p.value
1 0.506 3.923   0.000
2 0.400 3.098   0.002
3 0.196 1.522   0.128
4 0.346 2.683   0.007

Como estimo muestra en base al kappa que quiero obtener, ejemplo

kappaSize::CIBinary(kappa0 = 0.80, # el valor de kappa esperado
         kappaL = 0.60 , # el l<U+00ED>mite inferior
         kappaU =  NA, # el l<U+00ED>mite superior, lo dejo en blanco
         props  = .341 , # la proporci<U+00F3>n o prevalencia esperada. Considerando Goyal 2016 = 0.341
         raters = 2, # Marco + Silvia
         alpha = 0.05) # esto es standar

A minimum of 48 subjects are required for this study of interobserver agreement.

referencias

Goyal, S., Verma, P., Raj, S.S., 2016. Radiographic Evaluation of the Status of Third Molars in Sriganganagar Population - A Digital Panoramic Study. Malays. J. Med. Sci. 23, 103<e2><80><93>112.

Donner A, Rotondi MA. 2010. Sample Size Requirements for Interval Estimation of the Kappa Statistic for Interobserver Agreement Studies with a Binary Outcome and Multiple Raters. International Journal of Biostatistics 6:31.

Realizo tabla de presencia de infraoclusion por sexo

tabla1 <- matrix(c(103, 106, 81, 69), ncol = 2)

There were 16 warnings (use warnings() to see them)

Nombres de variables

tabla1

          No presenta Presenta
Femenino          103       81
Masculino         106       69

veo proprciones

prop.table(tabla1)*100

          No presenta Presenta
Femenino     28.69081 22.56267
Masculino    29.52646 19.22006

Prueba del chi-2

chisq.test(tabla1)


    Pearson's Chi-squared test with Yates' continuity correction

data:  tabla1
X-squared = 0.60056, df = 1, p-value = 0.4384
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