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Full-Text Articles in Life Sciences
Generalized Benjamini-Hochberg Procedures Using Spacings, Debashis Ghosh
Generalized Benjamini-Hochberg Procedures Using Spacings, Debashis Ghosh
Debashis Ghosh
For the problem of multiple testing, the Benjamini-Hochberg (B-H) procedure has become a very popular method in applications. We show how the B-H procedure can be interpreted as a test based on the spacings corresponding to the p-value distributions. Using this equivalence, we develop a class of generalized B-H procedures that maintain control of the false discovery rate in finite-samples. We also consider the effect of correlation on the procedure; simulation studies are used to illustrate the methodology.
A Causal Framework For Surrogate Endpoints With Semi-Competing Risks Data, Debashis Ghosh
A Causal Framework For Surrogate Endpoints With Semi-Competing Risks Data, Debashis Ghosh
Debashis Ghosh
In this note, we address the problem of surrogacy using a causal modelling framework that differs substantially from the potential outcomes model that pervades the biostatistical literature. The framework comes from econometrics and conceptualizes direct effects of the surrogate endpoint on the true endpoint. While this framework can incorporate the so-called semi-competing risks data structure, we also derive a fundamental non-identifiability result. Relationships to existing causal modelling frameworks are also discussed.