Publication

Variance Stabilizing the Difference of Two Binomial Proportions

Abstract

The article studies estimation of Delta = p(1) - p(2), the difference of two proportions p(1) and p(2), based on two independent Binomial experiments of size n(1) and n(2). The usual estimator, the difference between the two sample proportions, is variance stabilized conditionally on a weighted average of p(1) and p(2). When using this variance stabilized statistic as a test, a new family of confidence intervals for Delta is found. We show with a simulation study that these confidence intervals compare favorably in coverage accuracy and width to two other popular intervals proposed by Newcombe and Agresti and Caffo. Because no additional study weights need estimating, the variance stabilized statistic is also well-suited for combining results from independent studies. This meta analysis is also explained in the article.

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Related concepts (40)
Variance
In probability theory and statistics, variance is the squared deviation from the mean of a random variable. The variance is also often defined as the square of the standard deviation. Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value. It is the second central moment of a distribution, and the covariance of the random variable with itself, and it is often represented by , , , , or .
Binomial proportion confidence interval
In statistics, a binomial proportion confidence interval is a confidence interval for the probability of success calculated from the outcome of a series of success–failure experiments (Bernoulli trials). In other words, a binomial proportion confidence interval is an interval estimate of a success probability p when only the number of experiments n and the number of successes nS are known. There are several formulas for a binomial confidence interval, but all of them rely on the assumption of a binomial distribution.
Binomial distribution
In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success (with probability p) or failure (with probability ). A single success/failure experiment is also called a Bernoulli trial or Bernoulli experiment, and a sequence of outcomes is called a Bernoulli process; for a single trial, i.
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