Multiplicityadjusted

Multiplicityadjusted refers to statistical results that have been adjusted to account for multiple comparisons. In statistics, multiplicityadjustment addresses the increased risk of false positives that arises when several tests or endpoints are analyzed together rather than in isolation.

Why adjust for multiplicity? When multiple hypotheses are tested, the chance of at least one spurious finding grows. Adjusted procedures aim to control error rates such as the family-wise error rate (FWER) or the false discovery rate (FDR), depending on the analysis goals and the number of tests.

Common methods used to produce multiplicityadjusted p-values or confidence intervals include:

- Bonferroni and Sidak corrections for controlling the FWER

- Holm-Bonferroni, Hochberg, and Hommel procedures, which can be less conservative than simple Bonferroni

- Benjamini-Hochberg and Benjamini-Yekutieli procedures for controlling the FDR, useful when many tests are involved and some true effects are expected

- Other methods that account for dependency structures among tests

Contexts in which multiplicityadjustment is important include clinical trials with multiple endpoints, genome-wide association studies, neuroimaging analyses, and any exploratory research involving numerous comparisons. Best practices advocate reporting both unadjusted and adjusted results, specifying the adjustment method used, and clarifying whether confidence intervals or p-values are adjusted for multiplicity.

Limitations and considerations include the conservative nature of some methods, reduced statistical power, and sensitivity to dependency among tests. Researchers may choose hierarchical or gatekeeping strategies to balance discovery with error control. Software implementations exist in R, SAS, Python, and other statistical packages.