4: Paired contrasts

In ratesci: Confidence Intervals and Tests for Comparisons of Binomial Proportions or Poisson Rates

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
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options(digits = 3)

library(ratesci)

Confidence intervals and tests for paired comparisons of binomial proportions

The input data for paired proportions takes a different structure, compared with the data for independent proportions:

| | | | | | |---------------|---------------|---------------|---------------|---------------| | | | Event B | | | | | | Success | Failure | Total | | Event A | Success | $a \ (p_{11})$ | $b \ (p_{12})$ | $x_1 = a + b \ (p_1)$ | | | Failure | $c \ (p_{21})$ | $d \ (p_{22})$ | $c + d \ (1 -p_1)$ | | | Total | $x_2 = a + c \ (p_2)$ | $b + d \ (1 - p_2)$ | $N$ |

SCAS and other asymptotic score methods for RD and RR

To calculate a confidence interval (CI) for a paired risk difference ($\hat \theta_{RD} = \hat p_1 - \hat p_2$, where $\hat p_1 = (a+b)/N$, $\hat p_2 = (a+c)/N$), or relative risk ($\hat \theta_{RR} = \hat p_1 / \hat p_2$), the skewness-corrected asymptotic score (SCAS) method is recommended, as one that succeeds, on average, at containing the true parameter $\theta$ with the appropriate nominal probability (e.g. 95%), and has evenly distributed tail probabilities (Laud 2025, under review). It is a modified version of the asymptotic score methods by [@tango1998] for RD, and [@nam2002] and [@tang2003] for RR, incorporating both a skewness correction and a correction in the variance estimate.

The plots below illustrate the one-sided and two-sided interval coverage probabilities achieved by SCAS compared to some other popular methods[^1], when $N=40$ and the correlation coefficient is 0.25. A selection of coverage probability plots for other sample sizes and correlations can be found in the "plots" folder of the cpplot GitHub repository.

[^1]: SCASu = SCAS omitting variance bias correction; AS = Tang; MOVER-NJ = Method of Variance Estimates Recovery, based on Newcombe's correlation adjustment but using Jeffreys equal-tailed intervals instead of Wilson; MOVER-W = MOVER using Wilson intervals, and omitting Newcombe's correlation correction; BP = Bonett-Price

pairbinci() takes input in the form of a vector of length 4, comprising the four values c(a, b, c, d) from the above table, which are the number of paired observations having each of the four possible pairs of outcomes.

For example, using the dataset from a study of airway reactivity in children before and after stem cell transplantation, as used in [@fagerland2014]:

out <- pairbinci(x = c(1, 1, 7, 12))
out$estimates

The underlying z-statistic is used to obtain a two-sided hypothesis test against the null hypothesis of no difference (pval2sided). Note that this is equivalent to an 'N-1' adjusted version of the McNemar test. The facility is also provided for a custom one-sided test against any specified null hypothesis value $\theta_0$, e.g. for non-inferiority testing (pval_left and pval_right).

out$pval

For a confidence interval for paired RR, use:

out <- pairbinci(x = c(1, 1, 7, 12), contrast = "RR")
out$estimates
out$pval

To obtain the legacy Tango and Tang intervals for RD and RR respectively, you may set the skew and bcf arguments to FALSE. Also switching to method = "Score_closed" takes advantage of closed-form calculations for these methods (whereas the SCAS method is solved by iteration).

MOVER methods

For application of the MOVER method to paired RD or RR, an estimate of the correlation coefficient is included in the formula. A correction to the correlation estimate, introduced by Newcombe, is recommended, obtained with method = "MOVER_newc". As for unpaired MOVER methods, the default base method used for the individual (marginal) proportions is the equal-tailed Jeffreys interval, rather than the Wilson Score as originally proposed by Newcombe (obtained using moverbase = "wilson"). The combination of the Newcombe correlation estimate and the Jeffreys intervals gives the designation "MOVER-NJ". This method is less computationally intensive than SCAS, but coverage properties are inferior, and there is no corresponding hypothesis test.

pairbinci(x = c(1, 1, 7, 12), contrast = "RD", method = "MOVER_newc")$estimates

For cross-checking against published example in [@fagerland2014]

pairbinci(x = c(1, 1, 7, 12), contrast = "RD", method = "MOVER_newc", moverbase = "wilson")$estimates

Conditional odds ratio

Confidence intervals for paired odds ratio are obtained conditional on the number of discordant pairs, by transforming a confidence interval for the proportion $b / (b+c)$. Transformed SCAS (with or without a variance bias correction, to ensure consistency with the above SCAS hypothesis tests for RD and RR) or transformed mid-p intervals are recommended (Laud 2025, under review).

out <- pairbinci(x = c(1, 1, 7, 12), contrast = "OR")
out$estimates
out$pval

To select an alternative method, for example transformed mid-p:

pairbinci(x = c(1, 1, 7, 12), contrast = "OR", method = "midp")$estimates

References

ratesci documentation built on June 21, 2025, 1:07 a.m.