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Each procedure is easy-to-use and is carefully validated for accuracy. Use the links below to jump to a two proportions topic. For each sample size estimation procedure, only a brief summary of the procedure is given.

For more details about a particular procedure, we recommend you download and install the free trial of the software. For most of the sample size estimation procedures in PASS for two proportions, the user may choose to solve for sample size, power, or the population effect size in some manner. In the case of confidence intervals, one could solve for sample size or the distance to the confidence limit. In a typical two proportion test procedure where the goal is to estimate the sample size, the user enters power, alpha, and the desired population proportions.

The procedure is run and the output shows a summary of the entries as well as the sample size estimate. A summary statement is given, as well as references to the articles from which the formulas for the result were obtained. For many of the parameters e. When this is done, estimates are made for every combination of entered values. A numeric summary of these is results is produced as well as easy-to-read sample size or power curve graphs.

This page provides a brief description of the tools that are available in PASS for power and sample size analysis of two proportions. If you would like to examine the formulas and technical details relating to a specific PASS procedure, we recommend you download and install the free trial of the software, open the desired proportions procedure, and click on the help button in the top right corner to view the complete documentation of the procedure.

There you will find summaries, formulas, references, discussions, technical details, examples, and validation against published articles for the procedure. From this window the desired procedure is selected from the menus, the category tree on the left, or with a procedure search.

The procedure opens and the desired entries are made. When you click the Calculate button the results are produced.

You can easily navigate to any part of the output with the navigation pane on the left. This procedure computes power and sample size for hypothesis tests of the difference, ratio, or odds ratio of two independent proportions. The test statistics analyzed by this procedure assume that the difference between the two proportions is zero or their ratio is one under the null hypothesis.

This procedure computes and compares the power achieved by each of several test statistics that have been proposed. The power calculations assume that random samples are drawn from two separate populations. This procedure provides sample size and power calculations for one- or two-sided hypothesis tests of the difference between two independent proportions using the effect size.

The details of procedure are given in Cohen This routine calculates the group sample sizes necessary to achieve a specified interval width of the difference, ratio, or odds ratio of two independent proportions. Calculations may be made for several different confidence interval formulas for differences, ratios, and odds ratios. These procedures provide power analysis and sample size calculation for non-inferiority tests in two-sample designs in which the outcome is binary.

There are four non-inferiority procedures for two proportions. These procedures are identical except for the type of parameterization. The parameterization can be in terms of proportions, differences in proportions, ratios of proportions, and odds ratios.

Users may choose from among several popular test statistics commonly used for running the hypothesis test. These procedures provide power analysis and sample size calculation for equivalence tests in two-sample designs in which the outcome is binary. There are four equivalence procedures for two proportions. These procedures provide power analysis and sample size calculation for superiority by a margin tests in two-sample designs in which the outcome is binary.

There are four superiority procedures for two proportions. There are two procedures for two proportions in a repeated measures design. The parameterization can be in terms of proportions or odds ratios. These procedures calculate the power for testing the time-averaged difference TAD between two proportions in a repeated measures design.

A repeated measures design is one in which subjects are observed repeatedly over time. Measurements may be taken at pre-determined intervals e. This type of time-averaged difference analysis is often used when the outcome to be measured varies with time. For example, suppose that you want to compare two treatment groups based on a certain binary response variable such as the presence or absence of a disease.

The disease status may change over time, depending on various factors unrelated to the treatment. The precision of the experiment is increased by taking multiple measurements from each individual and comparing the time-averaged difference in proportions between the two groups.

Care must be taken in the analysis because of the correlation that is introduced when several measurements are taken from the same individual. The covariance structure may take on several forms depending on the nature of the experiment and the subjects involved.

This procedure allows you to calculate sample sizes and power using four different covariance patterns: These procedures can be used to calculate sample size and power for tests of pairwise contrasts in a mixed models analysis of repeated measures data.

Mixed models analysis of repeated measures data is also employed to provide more flexibility in covariance specification and a greater degree of robustness in the presence of missing data, provided that the data can be assumed to be missing at random. There are a number of group sequential procedures in PASS for the comparison of two proportions. One analytic procedure is available as well as simulation procedures for each of the following test types:.

Either the T-test or the Mann-Whitney-Wilcoxon test may be examined. For one-sided tests, significance and futility boundaries can be produced. The spacing of the looks can be equal or custom specified. The distributions of each of the groups under the null and alternative hypotheses can be specified directly using over ten distributions including normal, exponential, Gamma, Uniform, Beta, and Cauchy.

Futility boundaries can be binding or non-binding. This procedure computes conditional and predicted power for the case when a test is used to test whether the event probabilities of two populations are different. In sequential designs, one or more intermediate analyses of the emerging data are conducted to evaluate whether the experiment should be continued.

This may be done to conserve resources or to allow a data monitoring board to evaluate safety and efficacy when subjects are entered in a staggered fashion over a long period of time.

Conditional power a frequentist concept is the probability that the final result will be significant, given the data obtained up to the time of the interim look. Predictive power a Bayesian concept is the result of averaging the conditional power over the posterior distribution of effect size. Both of these methods fall under the heading of stochastic curtailment techniques. In a stratified design, the subjects are selected from two or more strata which are formed from important covariates such as gender, income level, or marital status.

The number of subjects in each of the two groups in each strata is set fixed by the design. A separate 2-by-2 table is formed for each stratum.

Although response rates may vary among strata, hypotheses about the overall odds ratio can be tested the Cochran-Mantel-Haenszel test. This procedure allows you to determine power and sample size for such a study. A number of procedures are available in PASS for the comparing two proportions in a cluster-randomized design. Below is a list of procedure categories of this type:. A cluster group randomized design is one in which whole units, or clusters, of subjects are randomized to the groups rather than the individual subjects in those clusters.

However, the conclusions of the study concern individual subjects rather than the clusters. Examples of clusters are families, school classes, neighborhoods, and hospital wards. Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually.

For example, it may be difficult for an instructor to use two methods of teaching individuals in the same class. The price of randomizing by clusters is a loss of efficiency—the number of subjects needed to obtain a certain level of precision in a cluster-randomized trial is usually much larger than the number needed when the subjects are randomized individually.

Hence, the basic two-sample methods of sample size estimation cannot be used. A stepped-wedge cluster-randomized design is similar to a cross-over design in that each cluster receives both the treatment and control over time. In a stepped-wedge design, however, the clusters switch or cross-over in one direction only usually from the control group to the treatment group. Once a cluster is randomized to the treatment group, it continues to receive the treatment for the duration of the study.

In a typical stepped-wedge design the all clusters are assigned to the control group at the first time point and then individual clusters are progressively randomized to the treatment group over time. The stepped-wedge design is particularly useful for cases where it is logistically impractical to apply a particular treatment to half of the clusters at the same time. This procedure computes power and sample size for tests for the difference between two proportions in cross-sectional stepped-wedge cluster-randomized designs.

In cross-sectional designs, different subjects are measured within each cluster at each point in time. No one subject is measured more than once. This is not to be confused with cohort studies i. The methods in this procedure should not be used for cohort or repeated measures designs. This procedure permits sample size and power analysis for studies where the analysis is to done based on the McNemar test. A 2-by-M case-control study investigates a risk factor relevant to the development of a disease.

A population of case patients with a disease and control patients without the disease is considered. Some of these patients have had exposure to a risk factor of interest. A random sample of N case patients is selected. Patients are stratified by the levels of a confounding variable such as age, gender, etc. For each selected case patient, a random sample of M matched control patients is drawn from the same strata group.

An estimate of the odds ratio, OR, of developing the disease in exposed and unexposed patients who have equal values of the confounding variable is desired. This odds ratio is assumed to be constant across all levels of the confounding variables. This procedure permits the user to solve for power, or for the number of cases, or for the number of controls per case.

Cluster-randomized designs are those in which whole clusters of subjects classes, hospitals, communities, etc. This sample size and power procedure is used for the case where the subject responses are binary proportion outcome.