# Non-Inferiority Tests for One Mean

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1 Chapter 45 Non-Inferiority ests for One Mean Introduction his module computes power and sample size for non-inferiority tests in one-sample designs in which the outcome is distributed as a normal random variable. his includes the analysis of the differences between paired values. he details of sample size calculation for the one-sample design are presented in the Inequality ests for One Mean chapter and they will not be duplicated here. his chapter only discusses those changes necessary for noninferiority tests. Sample size formulas for non-inferiority tests of a single mean are presented in Chow et al. (2003) page 50. he one-sample t-test is used to test whether a population mean is different from a specific value. When the data are differences between paired values, this test is known as the paired t-test. his module also calculates the power of the nonparametric analog of the t-test, the Wilcoxon test. Paired Designs Paired data may occur because two measurements are made on the same subject or because measurements are made on two subjects that have been matched according to other variables. Hypothesis tests on paired data can be analyzed by considering the difference between the paired items as the response. he distribution of differences is usually symmetric. In fact, the distribution must be symmetric if the individual distributions of the two items are identical. Hence, the paired t-test and the Wilcoxon signed-rank test are appropriate for paired data even when the distributions of the individual items are not normal. In paired designs, the variable of interest is the difference between two individual measurements. Although the noninferiority hypothesis refers to the difference between two individual means, the actual values of those means are not needed. All that is needed is their difference. he Statistical Hypotheses Both non-inferiority and superiority tests are examples of directional (one-sided) tests and their power and sample size could be calculated using the One-Sample -est procedure. However, at the urging of our users, we have developed this module which provides the input and output options that are convenient for non-inferiority tests. his section will review the specifics of non-inferiority and superiority testing. emember that in the usual t-test setting, the null (H0) and alternative (H) hypotheses for one-sided tests are defined as H 0 :µ X A versus H :µ X > A 45-

2 ejecting H0 implies that the mean is larger than the value A. his test is called an upper-tail test because H0 is rejected in samples in which the sample mean is larger than A. Following is an example of a lower-tail test. H 0 :µ X A versus H :µ X < A Non-inferiority tests are special cases of the above directional tests. It will be convenient to adopt the following specialize notation for the discussion of these tests. Parameter PASS Input/Output Interpretation µ Not used Population mean. If the data are paired differences, this is the mean of those differences. µ Not used eference value. Usually, this is the mean of a reference population. If the data are paired differences, this is the hypothesized value of the mean difference. M M Margin of non-inferiority. his is a tolerance value that defines the magnitude of difference that is not of practical importance. his may be thought of as the largest difference from the reference value that is considered to be trivial. he absolute value symbols are used to emphasize that this is a magnitude. he sign is determined by the specific design. δ D rue difference. his is the value of µ µ, the difference between the mean and the reference value, at which the power is calculated. Note that the actual values of µ and µ are not needed. Only their difference is needed for power and sample size calculations. Non-Inferiority ests A non-inferiority test tests that the mean is not worse than that of the baseline (reference) population by more than a small non-inferiority margin. he actual direction of the hypothesis depends on the whether higher values of the response are good or bad. Case : High Values Good In this case, higher values are better. he hypotheses are arranged so that rejecting the null hypothesis implies that the mean of the treatment group is no less than a small amount below the reference value. he value of δ is often set to zero. Equivalent sets of the null and alternative hypotheses are H 0 :µ µ M versus H :µ > µ M H :µ µ M versus H :µ µ > M 0 H 0 :δ M versus H :δ > M 45-2

3 Case 2: High Values Bad In this case, lower values are better. he hypotheses are arranged so that rejecting the null hypothesis implies that the mean of the treatment group is no more than a small amount above the reference value. he value of δ is often set to zero. Equivalent sets of the null and alternative hypotheses are H 0 :µ µ + M versus H :µ < µ + M H 0 :µ µ M versus H :µ µ < M H 0 :δ M versus H :δ < M Example A non-inferiority test example will set the stage for the discussion of the terminology that follows. Suppose that a test is to be conducted to determine if a new cancer treatment adversely affects the mean bone density. he adjusted mean bone density (AMBD) in the population of interest is gm/cm with a standard deviation of gm/cm. Clinicians decide that if the treatment reduces AMBD by more than 5% ( gm/cm), it poses a significant health threat. he hypothesis of interest is whether the AMBD in the treated group is greater than = he statistical test will be set up so that if the null hypothesis that the AMBD is less than or equal to is rejected, the conclusion will be that the new treatment is non-inferior, at least in terms of AMBD. he value gm/cm is called the margin of non-inferiority. est Statistics his section describes the test statistics that are available in this procedure. One-Sample -est he one-sample t-test assumes that the data are a simple random sample from a population of normallydistributed values that all have the same mean and variance. his assumption implies that the data are continuous and their distribution is symmetric. he calculation of the t-test proceeds as follow where t n = X D0 s X X n X i = = n i s X = n ( X i X ) i= n 2 and D0 is the value of the mean hypothesized by the null hypothesis. he significance of the test statistic is determined by computing the p-value. If this p-value is less than a specified level (usually 0.05), the hypothesis is rejected. Otherwise, no conclusion can be reached. 45-3

4 Wilcoxon Signed-ank est he Wilcoxon signed-rank test is a popular, nonparametric substitute for the t-test. It assumes that the data follow a symmetric distribution. he test is computed using the following steps.. Subtract the hypothesized mean, D0, from each data value. ank the values according to their absolute values. 2. Compute the sum of the positive ranks Sp and the sum of the negative ranks Sn. he test statistic, W, is the minimum of Sp and Sn. 3. Compute the mean and standard deviation of W using the formulas ( ) n n+ µ = σ W W = n n 4 and where t represents the number of times the ith value occurs. 4. Compute the z value using z W ( )( 2 ) 3 n n+ n+ - t - t W µ W = σ he significance of the test statistic is determined by computing the p-value using the standard normal distribution. If this p-value is less than a specified level (usually 0.05), the null hypothesis is rejected in favor of the alternative hypothesis. Otherwise, no conclusion can be reached. Wn n Computing the Power he power is calculated as follows for a directional alternative (one-tailed test) in which D > D0. D is the value of the mean at which the power is computed.. Find t α such that n ( tα ) = α, where ( t ) n degrees of freedom. 2. Calculate x = D0 + t a α σ n. n α D D0 3. Calculate the noncentrality parameter λ =. σ n a 4. Calculate t a = x - D + λ. σ n 5. Calculate the power = n,λ ( ta ), where ( x) is the area to the left of x under a central-t curve with n,λ is the area to the left of x under a noncentral-t curve with degrees of freedom n and noncentrality parameter λ. 45-4

6 Logistic Make the Wilcoxon sample size adjustment assuming that the data actually follow the logistic distribution. Normal Make the Wilcoxon sample size adjustment assuming that the data actually follow the normal distribution. Population Size his is the number of subjects in the population. Usually, you assume that samples are drawn from a very large (infinite) population. Occasionally, however, situations arise in which the population of interest is of limited size. In these cases, appropriate adjustments must be made. When a finite population size is specified, the standard deviation is reduced according to the formula 2 n 2 σ = σ N where n is the sample size, N is the population size, σ is the original standard deviation, and σ is the new standard deviation. he quantity n/n is often called the sampling fraction. he quantity correction factor. n N is called the finite population Power and Alpha Power his option specifies one or more values for power. Power is the probability of rejecting a false null hypothesis, and is equal to one minus Beta. Beta is the probability of a type-ii error, which occurs when a false null hypothesis is not rejected. Values must be between zero and one. Historically, the value of 0.80 (Beta = 0.20) was used for power. Now, 0.90 (Beta = 0.0) is also commonly used. A single value may be entered here or a range of values such as 0.8 to 0.95 by 0.05 may be entered. Alpha his option specifies one or more values for the probability of a type-i error. A type-i error occurs when a true null hypothesis is rejected. Values must be between zero and one. Historically, the value of 0.05 has been used for alpha. his means that about one test in twenty will falsely reject the null hypothesis. You should pick a value for alpha that represents the risk of a type-i error you are willing to take in your experimental situation. You may enter a range of values such as or 0.0 to 0.0 by

7 Sample Size N (Sample Size) his option specifies one or more values of the sample size, the number of individuals in the study. his value must be an integer greater than one. You may enter a list of values using the syntax or 50 to 250 by 50. Effect Size Mean Difference M (Non-Inferiority Margin) his is the magnitude of the margin of non-inferiority. It must be entered as a positive number. When higher means are better, this value is the distance below the reference value that is still considered noninferior. When higher means are worse, this value is the distance above the reference value that is still considered non-inferior. D (rue Value) his is the actual difference between the mean and the reference value. For non-inferiority tests, this value is often set to zero. When this value is non-zero, care should be taken that this value is consistent with whether higher means are better or worse. Effect Size Standard Deviation Standard Deviation his option specifies one or more values of the standard deviation. his must be a positive value. PASS includes a special module for estimating the standard deviation. his module may be loaded by pressing the SD button. efer to the Standard Deviation Estimator chapter for further details. 45-7

8 Example Power Analysis Suppose that a test is to be conducted to determine if a new cancer treatment adversely affects the mean bone density. he adjusted mean bone density (AMBD) in the population of interest is gm/cm with a standard deviation of gm/cm. Clinicians decide that if the treatment reduces AMBD by more than 5% ( gm/cm), it poses a significant health threat. hey also want to consider what would happen if the margin of noninferiority is set to 2.5% ( gm/cm). Following accepted procedure, the analysis will be a non-inferiority test using the t-test at the significance level. Power is to be calculated assuming that the new treatment has no effect on AMBD. Several sample sizes between 20 and 300 will be analyzed. he researchers want to achieve a power of at least 90%. All numbers have been multiplied by 0000 to make the reports and plots easier to read. Setup his section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by expanding Means, then One Mean, then clicking on Non-Inferiority, and then clicking on. You may then make the appropriate entries as listed below, or open Example by going to the File menu and choosing Open Example emplate. Option Value Design ab Solve For... Power Higher Means Are... Better Nonparametric Adjustment... Ignore Population Size... Infinite Alpha N (Sample Size) M (Non-Inferiority Margin) D (rue Difference)... 0 S (Standard Deviation)... 3 Annotated Output Click the Calculate button to perform the calculations and generate the following output. Numeric esults for Non-Inferiority est (H0: Diff -M; H: Diff > -M) Higher Means are Better est Statistic: -est Non-Inferiority Actual Significance Standard Margin Difference Level Deviation Power N (-M) (D) (Alpha) Beta (S) (report continues) 45-8

9 eport Definitions Power is the probability of rejecting H0 when it is false. N is the sample size, the number of subjects (or pairs) in the study. -M is the magnitude and direction of the margin of non-inferiority. Since higher means are better, this value is negative and is the distance below the reference value that is still considered non-inferior. D is the mean difference (treatment - reference value) at which the power is computed. Alpha is the probability of rejecting H0 when it is true, which is the probability of a false positive. Beta is the probability of accepting H0 when it is false, which is the probability of a false negative. S is the standard deviation of the response. It measures the variability in the population. Summary Statements A sample size of 20 achieves 3% power to detect non-inferiority using a one-sided t-test when the margin of equivalence is and the true difference between the mean and the reference value is he data are drawn from a single population with a standard deviation of he significance level (alpha) of the test is

10 he above report shows that for M =.5, the sample size necessary to obtain 90% power is just under 80. However, if M = 0.575, the required sample size is about

11 Example 2 Finding the Sample Size Continuing with Example, the researchers want to know the exact sample size for each value of M. Setup his section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by expanding Means, then One Mean, then clicking on Non-Inferiority, and then clicking on. You may then make the appropriate entries as listed below, or open Example 2 by going to the File menu and choosing Open Example emplate. Option Value Design ab Solve For... Sample Size Higher Means Are... Better Nonparametric Adjustment... Ignore Population Size... Infinite Power Alpha M (Non-Inferiority Margin) D (rue Difference)... 0 S (Standard Deviation)... 3 Output Click the Calculate button to perform the calculations and generate the following output. Numeric esults Numeric esults for Non-Inferiority est (H0: Diff -M; H: Diff > -M) Higher Means are Better est Statistic: -est Non-Inferiority Actual Significance Standard Margin Difference Level Deviation Power N (-M) (D) (Alpha) Beta (S) his report shows the exact sample size requirement for each value of M. 45-

12 Example 3 Validation using Chow Chow, Shao, Wang (2003) pages has an example of a sample size calculation for a non-inferiority trial. heir example obtains a sample size of 8 when D = 0.5, M = 0.5, S =, Alpha = 0.05, and Beta = Setup his section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by expanding Means, then One Mean, then clicking on Non-Inferiority, and then clicking on. You may then make the appropriate entries as listed below, or open Example 3 by going to the File menu and choosing Open Example emplate. Option Value Design ab Solve For... Sample Size Higher Means Are... Better Nonparametric Adjustment... Ignore Population Size... Infinite Power Alpha M (Non-Inferiority Margin) D (rue Difference) S (Standard Deviation)... Output Click the Calculate button to perform the calculations and generate the following output. Numeric esults for Non-Inferiority est (H0: Diff -M; H: Diff > -M) Higher Means are Better est Statistic: -est Non-Inferiority Actual Significance Standard Margin Difference Level Deviation Power N (-M) (D) (Alpha) Beta (S) PASS also obtains a sample size of

13 Example 4 Validation of a Cross-Over Design given in Julious Julious (2004) page 953 gives an example of a sample size calculation for a cross-over design. His example obtains a sample size of 87 when D = 0, M = 0, S = , Alpha = 0.025, and Beta = 0.0. When D is changed to 2, the resulting sample size is 6. Note that in Julius s example, the population standard deviation is given as 20. Assuming that the correlation between items in a pair is 0, the standard deviation of the difference is calculated to be 2 2 S = ( 0)( 20)( 20) = Actually, the value of S probably should be less because the correlation is usually greater than 0 (at least 0.2). Setup his section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by expanding Means, then One Mean, then clicking on Non-Inferiority, and then clicking on. You may then make the appropriate entries as listed below, or open Example 4 by going to the File menu and choosing Open Example emplate. Option Value Design ab Solve For... Sample Size Higher Means Are... Better Nonparametric Adjustment... Ignore Population Size... Infinite Power Alpha M (Non-Inferiority Margin)... 0 D (rue Difference) S (Standard Deviation) Output Click the Calculate button to perform the calculations and generate the following output. Numeric esults for Non-Inferiority est (H0: Diff -M; H: Diff > -M) Higher Means are Better est Statistic: -est Non-Inferiority Actual Significance Standard Margin Difference Level Deviation Power N (-M) (D) (Alpha) Beta (S) PASS also obtains sample sizes of 87 and

14 Example 5 Validation of a Cross-Over Design given in Chow, Shao, and Wang Chow, Shao, and Wang (2004) page 67 give an example of a sample size calculation for a cross-over design. heir example calculates sample sizes of 3 and 4 (3 by formula and 4 from their table) in each sequence (26 or 28 total) when D = -0., M = 0.2, S = 0.2, Alpha = 0.05, and Beta = Setup his section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by expanding Means, then One Mean, then clicking on Non-Inferiority, and then clicking on. You may then make the appropriate entries as listed below, or open Example 5 by going to the File menu and choosing Open Example emplate. Option Value Design ab Solve For... Sample Size Higher Means Are... Better Nonparametric Adjustment... Ignore Population Size... Infinite Power Alpha M (Non-Inferiority Margin) D (rue Difference) S (Standard Deviation) Output Click the Calculate button to perform the calculations and generate the following output. Numeric esults for Non-Inferiority est (H0: Diff -M; H: Diff > -M) Higher Means are Better est Statistic: -est Non-Inferiority Actual Significance Standard Margin Difference Level Deviation Power N (-M) (D) (Alpha) Beta (S) PASS obtains a sample size of 27, which is between the values of 26 and 28 that were obtained by Chow et al. 45-4

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