Abstract
The Second Generation P-Value (SGPV) measures the overlap between an estimated interval and a composite hypothesis of parameter values. We develop a sequential monitoring scheme of the SGPV (SeqSGPV) to connect study design intentions with end-of-study inference anchored on scientific relevance. We build upon Freedman’s “Region of Equivalence” (ROE) in specifying scientifically meaningful hypotheses called Pre-specified Regions Indicating Scientific Merit (PRISM). We compare PRISM monitoring versus monitoring alternative ROE specifications. Error rates are controlled through the PRISM’s indifference zone around the point null and monitoring frequency strategies. Because the former is fixed due to scientific relevance, the latter is a targettable means for designing studies with desirable operating characters. An affirmation step to stopping rules improves frequency properties including the error rate, the risk of reversing conclusions under delayed outcomes, and bias.
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