OptOTrials

The functions below were deprecated in 1.0.3, which warned on every call, and

are removed here. If a script written for 1.0.2 stops working, the replacement is listed below.

• ruleF() and ruleFS() become rule(..., stopping = "F") and rule(..., stopping = "FS").
• Decision_rule_S.F(), Decision_rule_M.F() and Decision_rule_W.F() become rule(..., test = "S"/"M"/"W", stopping = "F").
• Decision_rule_S.FS(), Decision_rule_M.FS() and Decision_rule_W.FS() become rule(..., test = "S"/"M"/"W", stopping = "FS").
• Decision_rule_S_1stage(), Decision_rule_M_1stage() and Decision_rule_W_1stage() become rule(..., test = "S"/"M"/"W", stopping = "none").
• op.F(), op.FS() and op.1stage() become op(design, nsim, seed), where design is the object returned by rule().

The helpers p_plus(), p_minus() and pq_fun() are no longer exported.

They are intermediate quantities inside the variance calculations rather than

part of the interface, and they are still used internally.

V_S.over.nk(), QR_fun() and W_W() remain exported and are now documented

with examples. They give the asymptotic variance of the score,

Mann-Whitney-Wilcoxon and win odds statistics, the quantities defined in the

appendix of the accompanying article, and they let a threshold be computed at a

sample size of the user's choosing rather than at the optimal one.

Designs and operating characteristics are unchanged: the new interface

reproduces every value the old one produced, and the package test suite checks

this against the tables published in the article.

Fixed

• design_table() and print() on an operating-characteristic object now round the expected sample sizes the same way. EN0, ENa and EN are each shown to two decimal places, rounded once from the value actually simulated; EN is no longer recomputed from the already-rounded EN0 and ENa, which made design_table() print a third decimal that op() did not. Because each column is rounded independently, (EN0 + ENa)/2 taken from the printed columns can differ from the printed EN by up to 0.01.
• The printed operating-characteristic summary no longer reports a Monte Carlo standard error keyed to a nominal level of 0.05, which was wrong for any other choice of alpha. The table already gives the standard error of each rejection probability, computed from that probability.
• The FS design's continuation probability under the alternative, beta3 = Pr(T1 <= t1s | Ha), was computed for the score test without the factor n1 in the mean of the statistic. The score statistic has mean theta * V * n, unlike the rank-based statistics, whose means do not scale with the sample size, so the omission made the value far too large (0.98 against a true 0.45 in the article's Example 1 configuration). This entered the expected sample size under the alternative and therefore the objective of criteria 2, 3 and 5, so rule(..., test = "S", stopping = "FS") could return a design that did not minimise what it was asked to minimise. The Mann-Whitney-Wilcoxon and win odds expressions were already correct, and the F design does not use this quantity, so neither table in the accompanying article is affected. The internal variable is now named beta3, matching the article's notation, rather than beta2, which denotes beta - beta1 in the F design.

New

• Input validation on all exported functions: p1 and p2 are checked for numeric type, equal length, at least two categories, absence of missing values, non-negativity, and summation to one within 1e-6.
• Proportional_odds_assumption() explains itself and warns instead of returning a bare NA.
• rule() detects a degenerate optimum, warns, and by default substitutes the single-stage design; controlled by on_degenerate.
• op() reports futility and superiority stopping probabilities separately, with Monte Carlo standard errors on the rejection probabilities, and refuses to simulate an unsubstituted degenerate design unless allow_degenerate.
• Decision boundaries are reported on an interpretable effect-size scale, the odds ratio for the score test and the win odds for the rank-based tests.
• A vignette, "Designing two-stage trials with ordered categorical outcomes".
• An automated test suite under tests/testthat.