OptOTrials 1.0.3

Transitional release. It adds the unified interface and deprecates the earlier one; nothing is removed in this version, and every function exported by 1.0.2 still works exactly as before.

New

• rule() constructs a decision rule. The test statistic (test), the monitoring scheme (stopping) and the optimality criterion (criterion) are arguments, so one function replaces the eleven separate decision-rule functions.
• op() evaluates a design by simulation, taking the object returned by rule() and reporting both hypotheses in a single call, with the futility and superiority stopping probabilities separated and Monte Carlo standard errors on the rejection probabilities.
• design_table() sweeps a set of tests and criteria and returns a data frame.
• rule() and op() return classed objects with named components and print() methods, so their output need not be indexed by position.
• Input validation wherever probabilities are received: 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 and, by default, substitutes the single-stage design; controlled by on_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”.

Deprecated

The functions below still work and still return exactly what they always returned, but each now warns that it will be removed in version 1.1.0 and names its replacement. See help("OptOTrials-deprecated").

• 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().

Two habits change when converting a script. rule() returns a named object, so res[3] becomes res$n1, and the object is passed straight to op() instead of its elements being transcribed. op() evaluates both hypotheses in one call, so the old pattern of calling the simulator twice, once with (p1, p2) and once with (p1, p1), is no longer needed.

Fixed

• 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. It entered the expected sample size under the alternative and therefore the objective of criteria 2, 3 and 5, so a score-test FS design could fail to 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.

• Internal variable names now follow the article: t1f and t1s for the interim futility and superiority boundaries, and beta3 for Pr(T1 <= t1s | Ha), in place of t1l, t1u and beta2.

• Typographical errors in the help-page titles of QR_fun(), V_S.over.nk(), Decision_rule_M.F(), ruleF() and ruleFS().

• The description of the value returned by op.F(), op.FS() and op.1stage(): the first element is the rejection probability, not the probability of an incorrect decision.

OptOTrials 1.0.2

Initial CRAN release.