Written answers to questions that come up repeatedly in regulatory and trial work, and that have thin coverage elsewhere. Each is drawn from an engagement rather than assembled from the literature.
Classical scan MDC assumes independent counts in discrete intervals. A continuous detector stream is serially correlated, so the effective number of independent observations is smaller than the raw count and the nominal false-positive rate is not the actual one. Generalized midpoint, moving-average and EWMA lag-k differencing restore control; bootstrap gives the bias and precision of the resulting limits.
Most statistical IRs ask a sponsor to make an existing decision auditable, not to change it. The right response answers the question asked, in the reviewer's frame, with the smallest sufficient addition to the record — because a reply that generates a follow-up has cost a full review cycle.
Denial rates are not comparable across plans without adjustment, because the denominator is itself a policy choice. And the deepest limitation is not statistical: claims data cannot observe care that was never requested, so every denial statistic understates burden by an unknown amount.
At challenge-study sample sizes the confidence interval matters more than the point estimate, because the lower bound is what supports the label claim. And the case definition does more work than any model — which is why it has to be pre-specified.
