bbssr

• The Z-pooled branch indexed the matrix of binomial probabilities by the responder count rather than by the position of that count among the retained values. The two coincided in the configurations reached by the old code, so the results were correct, but the indexing broke as soon as the retained counts were not contiguous.
• The Boschloo branch discarded outcomes whose Fisher p-value was not below the smallest Fisher p-value among outcomes with a non-positive risk difference. The truncation was sound but had no natural counterpart for a two-sided alternative, and it has been removed. The whole outcome grid is now scanned, which the move to compiled code makes affordable.
• pmin(1, x) with a scalar first argument drops the dim attribute of x. This affected the matrix of p-values returned for a two-sided chi-squared test.
• The allocation ratio was not preserved by the re-estimation when r was not 1. The interim size of group 1 was ceiling(omega N1) and the second-stage size was ceiling(r N22), so the final size of group 1 was rounded up three separate times and did not equal ceiling(r) times the final size of group 2. With r = 2, N2 = 33 and omega = 0.8 the interim allocation was 53 to 27 rather than 54 to 27, and a re-estimated control group of 40 gave 79 rather than 80 in group 1. Both the interim and the final size of group 1 are now obtained from the size of group 2 by a single ceiling(r ...), and the second stage follows as the difference. The allocation ratio is therefore exact whenever r is a whole number, and as close as whole numbers allow otherwise. Results for r = 1 are unchanged. BinaryBSSR() brings group 1 to ceiling(r N2.final) for the same reason, which also corrects an imbalance that is already present in the observed interim data.

New features

• All five tests accept alternative = 'greater' (the default, and the behaviour of version 1) or alternative = 'two.sided'. For the conditional tests the two-sided p-value follows the convention selected by tsmethod, either 'minlike', which matches stats::fisher.test, or 'central'. The chi-squared and Z-pooled tests order outcomes by the absolute value of the Z statistic when the alternative is two-sided.
• The number of grid points used to search over the nuisance parameter of the unconditional tests is now the argument n.grid, which defaults to 100 and reproduces the grid of version 1. A finer grid gives larger p-values and a more accurate test.
• The Berger-Boos procedure is available through the argument bb.gamma. A positive value restricts the search over the nuisance parameter to an exact Clopper-Pearson confidence interval computed from the observed number of responders, and adds bb.gamma to the resulting p-value. The default of 0 disables the procedure.
• BinaryBSSR() re-estimates the sample size from the blinded data of a trial that is under way. It takes the interim sample size of each group and the pooled number of responders, and returns the number of patients still to enrol in each group. This complements BinaryPowerBSSR(), which evaluates a re-estimation rule at the planning stage.
• Every function returns a classed object with a print() method, and BinaryRR(), BinaryPower(), BinarySampleSize() and BinaryPowerBSSR() also have a plot() method. The classes are bbssr_rr, bbssr_power, bbssr_samplesize, bbssr_powerbssr and bbssr_bssr.
• The plot() methods accept main, sub, xlab, ylab, base_size and colours, so the title, the axis labels, the font size and the palette can be replaced without editing the returned object. A label left at NULL keeps the text built from the object and a label set to NA is dropped. Where a legend is drawn, legend.title and legend.labels rename it, and where a vertical scale is continuous, ylim sets its range through coord_cartesian(), which hides rather than removes the points outside the range. The reference lines are no longer fixed: plot.bbssr_powerbssr() and plot.bbssr_samplesize() take ref.line, and plot.bbssr_samplesize() also takes ref.line.N2. The methods that draw a curve also take show.points, which removes the marker at each evaluated point and leaves the curve alone, so a fine grid stays legible. This matters when Delta.T is 0, since the two power columns of BinaryPowerBSSR() are then rejection probabilities under the null hypothesis and the line belongs at the level of significance rather than at the target power. The defaults reproduce the earlier plots exactly.
• BinaryPowerBSSR() reports the expected total sample size in a new column E.N, and carries the interim sample sizes as the attributes n1.interim and n2.interim.
• Arguments are validated, and invalid sample sizes, significance levels, allocation ratios and grid sizes now produce an informative error.

Performance

• The maximization over the nuisance parameter and the summation of the binomial mass over the rejection region are implemented in C++ through Rcpp.
• BinaryPowerBSSR() evaluates BinarySampleSize() once per distinct pair of recovered response probabilities and BinaryRR() once per distinct pair of final sample sizes, rather than once per interim outcome.

Interface changes that are not backward compatible

• The weighted argument of BinaryPowerBSSR() has been removed, along with the weighted design it selected. Calls that supply it will fail with an unused argument error.
• The asmd.p1 and asmd.p2 arguments of BinaryPowerBSSR() have been removed. They entered the calculation only through the weights of the weighted design, so with that design gone they had no effect on the result. The planning assumption is carried by Delta.A together with the initial sample sizes N1 and N2. Calls that supply them will fail with an unused argument error.
• BinaryPower() returns a one-row-per-scenario data frame of class bbssr_power rather than a bare numeric vector. Code that used the returned value directly should now read the Power column.
• BinaryRR() returns a logical matrix carrying the class bbssr_rr and the design settings as attributes. The matrix itself is unchanged, so indexing and sum() behave as before, but printing now goes through print.bbssr_rr().
• BinarySampleSize() and BinaryPowerBSSR() gained columns in their output, and BinarySampleSize() returns integer rather than double sample sizes.
• restricted now defaults to FALSE in BinaryPowerBSSR() instead of having no default.
• ggplot2 and Rcpp have moved into Imports, so a compiler is required to install the package from source.

Documentation

• The vignettes have been rewritten and a fourth has been added. bbssr-introduction covers the whole package, bbssr-statistical-methods derives the tests and the re-estimation rules, bbssr-interim-reestimation follows a single trial from planning to the interim decision, and bbssr-validation compares the package against stats, Exact and exact2x2 and reports timings.
• The test suite has been rewritten around reference implementations that follow the definitions of the tests directly, so the p-values are checked against a direct evaluation rather than against stored values.