Barnard (version 1.6)

barnard.test: Barnard's Unconditional Test

Description

Barnard's unconditional test for superiority applied to 2x2 contingency tables using Score or Wald statistics for the difference between two binomial proportions.

Usage

barnard.test(n1, n2, n3, n4, dp = 0.001, pooled = TRUE)

Arguments

n1,n2,n3,n4

Elements of the 2x2 contingency table

dp

The resolution to search for the nuisance parameter

pooled

Z statistic with pooled (Score) or unpooled (Wald) variance

Value

• statistic.table
  The contingency tables considered in the analysis represented by 'n1' and 'n2', their scores, and whether they are included in the one-sided (1), two-sided (2) tests, or not included at all (0)
• nuisance.matrix
  Nuisance parameters, $p$, and the corresponding p-values for both one- and two-sided tests
• dp
  The resolution of the search space for the nuisance parameter
• contingency.matrix
  The observed 2x2 contingency table
• alternative
  One sided or two sided test
• statistic
  The standardized difference between the observed proportions
• nuisance.parameter
  The nuisance parameter where the p-value is maximized
• p.value
  The p-value for the observed contingency table
• pooled
  Variance estimator of the Z statistic

Details

For a 2x2 contingency table, such as $X=[n_1,n_2;n_3,n_4]$, the normalized difference in proportions between the two categories, given in each column, can be written with pooled variance (Score statistic) as $$T(X)=\frac{\hat{p}_2-\hat{p}_1}{\sqrt{\hat{p}(1-\hat{p})(\frac{1}{c_1}+\frac{1}{c_2})}},$$ where $\hat{p}=(n_1+n_3)/(n_1+n_2+n_3+n_4)$, $\hat{p}_2=n_2/(n_2+n_4)$, $\hat{p}_1=n_1/(n_1+n_3)$, $c_1=n_1+n_3$ and $c_2=n_2+n_4$. Alternatively, with unpooled variance (Wald statistic), the difference in proportions can we written as $$T(X)=\frac{\hat{p}_2-\hat{p}_1}{\sqrt{\frac{\hat{p}_1(1-\hat{p}_1)}{c_1}+\frac{\hat{p}_2(1-\hat{p}_2)}{c_2}}}.$$ The probability of observing $X$ is $$P(X)=\frac{c_1!c_2!}{n_1!n_2!n_3!n_4!}p^{n_1+n_2}(1-p)^{n_3+n_4},$$ where $p$ is the unknown nuisance parameter.

Barnard's test considers all tables with category sizes $c_1$ and $c_2$ for a given $p$. The p-value is the sum of probabilities of the tables having a score in the rejection region, e.g. having significantly large difference in proportions for a two-sided test. The p-value of the test is the maximum p-value calculated over all $p$ between 0 and 1.

References

1. Barnard, G.A. (1945) A new test for 2x2 tables.Nature, 156:177.
2. Barnard, G.A. (1947) Significance tests for 2x2 tables.Biometrika, 34:123-138.
3. Suissa, S. and Shuster, J. J. (1985), Exact Unconditional Sample Sizes for the 2x2 Binomial Trial, Journal of the Royal Statistical Society, Ser. A, 148, 317-327.
4. Cardillo G. (2009) MyBarnard: a very compact routine for Barnard's exact test on 2x2 matrix. URLhttp://www.mathworks.com/matlabcentral/fileexchange/25760
5. Galili T. (2010) URLhttp://www.r-statistics.com/2010/02/barnards-exact-test-a-powerful-alternative-for-fishers-exact-test-implemented-in-r/
6. Lin C.Y., Yang M.C. (2009) Improved p-value tests for comparing two independent binomial proportions.Communications in Statistics-Simulation and Computation, 38(1):78-91.
7. Trujillo-Ortiz, A., R. Hernandez-Walls, A. Castro-Perez, L. Rodriguez-Cardozo N.A. Ramos-Delgado and R. Garcia-Sanchez. (2004). Barnardextest:Barnard's Exact Probability Test. A MATLAB file. [WWW document]. URLhttp://www.mathworks.com/

Examples

barnard.test(8,14,1,3)

## Plotting the search for the nuisance parameter for a one-sided test
bt<-barnard.test(8,14,1,3)
plot(bt$nuisance.matrix[,1:2],
     t="l",xlab="nuisance parameter",ylab="p-value")

## Plotting the tables included in the p-value
bt<-barnard.test(40,14,10,30)
bts<-bt$statistic.table
plot(bts[,1],bts[,2],
     col=hsv(bts[,4]/4,1,1),
     t="p",xlab="n1",ylab="n2")

## Plotting the difference between pooled and unpooled tests
bts<-barnard.test(40,14,10,30,pooled=TRUE)$statistic.table
btw<-barnard.test(40,14,10,30,pooled=FALSE)$statistic.table
plot(bts[,1],bts[,2],
     col=c("black","white")[1+as.numeric(bts[,4]==btw[,4])],
     t="p",xlab="n1",ylab="n2")