Parameters | Copy Number | Multiple Testing Method

This drop-down menu enables you to adjust your model for multiple hypothesis tests across all means or LSMeans differences.Note: If you do not specify Means or LSMeans differences, p-value adjustments are made across the Type 3 tests for all of the fixed effects and covariates.The AdaptiveHolm, AdaptiveHochberg, Bonferroni, Holm, Hommel, Sidak, StepBon, and StepSid methods all control for the familywise error rate. The methods based on FDR all control for false discovery rate.

• Select this option to enable the -log10(p-value) Cutoff field (and slider). This selection enables you to specify a -log10(p-value) cutoff directly.

• Requests adjusted p-values by using the Hochberg and Benjamini (1990)^{1}adaptive step-down Bonferroni method.

• Requests adjusted p-values by using the Hochberg and Benjamini (1990)a adaptive step-up Bonferroni method.

• Specifies that the Bonferroni adjustments (number of tests p-value) be computed for each test.

• Note: These adjustments can be extremely conservative and should be viewed with caution.

• Assumes that p-values are independent and uniformly distributed under their respective null hypotheses, Hochberg (1988)^{2}demonstrates that Holm’s step-down adjustments control the familywise error rate even when calculated in step-up fashion. Since the adjusted p-values are uniformly smaller for Hochberg’s method than for Holm’s method, the Hochberg method is more powerful. However, this improved power comes at the cost of having to make the assumption of independence.

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• See StepBon, below.

• Note: These adjustments are slightly less conservative than the Bonferroni adjustments, but they still should be viewed with caution.

• Requests adjusted p-values by using the step-down Bonferroni method of Holm (1988).

• Requests adjusted p-values by using the Šidák method but in step-down fashion.

• Requests adjusted p-values by using the Benjamini and Hochberg (2000)^{4}adaptive linear step-up method.

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• These p-values do not control the familywise error rate, but they do control the false discovery rate.

• Computes the "q-values" of Storey (2002) and Storey, Taylor, and Siegmund (2004). PROC MULTTEST treats these "q-values" as adjusted p-values.

Hochberg, Y. and Benjamini, Y. (1990) More Powerful Procedures for Multiple Significance Testing. Statistics in Medicine 9: 811–818.

Hochberg, Y. (1988). A Sharper Bonferroni Procedure for Multiple Significance Testing. Biometrika 75: 800–803.

Benjamini, Y. and Hochberg, Y. (2000). On the Adaptive Control of the False Discovery Rate in Multiple Testing with Independent Statistics. Journal of Educational and Behavioral Statistics 25: 60–83.

Benjamini, Y. and Yekateuli, D. (2001). The Control of the False Discovery Rate in Multiple Testing under Dependency. Annals of Statistics 29: 1165–1188.

Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, B 57: 289–300.

Refer to p-Value Adjustments for more details about each of these methods.