gsDesignNB 0.3.2 (development)

Score-test sizing and inference guidance

• Expanded the paper, sample-size vignette, and score-vs-Wald simulation vignette with recommendations for when the Zhu-Lakkis / Friede-Schmidli / Mutze Wald sample-size formula is appropriate and when to use score-test sizing with separate null and alternative variance factors.
• Added an ai-skills vignette demonstrating how the agent skills under .agents/skills/ can guide package-native score-test and simulation workflows without replacing statistical review.
• Added a published sample-size examples vignette showing how the package-specific AI skill translates literature and protocol descriptions into transparent sample_size_nbinom() calls, including dropout, event-gap, non-inferiority, and group sequential parameterization checks.
• Refined score-test recommendations to distinguish the final analysis test from the sample-size formula: Wald/Zhu-Lakkis sizing remains a useful practical baseline and may provide a power margin when paired with the score test in fixed-design superiority settings, while SSR designs should verify any starting-size margin inside the planned adaptation rule.
• Added a targeted SSR starting-size sensitivity cache comparing Wald- and score-sized starting designs under the score final test in a low-event stress setting. Both starts preserved near-nominal score-test Type I error, and the larger Wald-sized start did not produce a clear SSR power advantage.
• Documented the cached score-vs-Wald simulation results: the Wald test was mildly anti-conservative in several finite-sample scenarios, while the score test preserved Type I error more conservatively and should be paired with simulation-based power checks.
• Corrected the reported variance_null field from sample_size_nbinom() so it is on the same final-analysis scale as variance; the score sample-size calculation itself was already using the null variance factor correctly.

Group sequential simulation vignette

• Corrected the group sequential simulation vignette and cached results so that simulations use the rounded final group sequential sample size, monthly dropout hazard, and helper functions for boundary checking and summary.
• toInteger.gsNB() now preserves calendar-time enrollment quantities at interim analyses and recomputes expected events, exposures, and information after rounding the final sample size.
• toInteger.gsNB() now preserves the shape of piecewise accrual schedules when rescaling calendar-time designs after final sample-size rounding.
• gsNBCalendar(), update_gsNB(), and simulation boundary checks now support harm-bound group sequential designs available in gsDesign 3.10.0 (test.type = 7 or 8, sfharm, sfharmparam, and testHarm).
• summarize_gs_sim() now reports optional sample-size and exposure summaries when available and uses finite trimmed means for information estimates.
• Updated SSR simulation reporting in the manuscript and SSR simulation article to use the production score-test cache: 3,600 replicates per power scenario, 20,000 per RR = 1 main-grid scenario, 1,000 per RR > 1 scenario, and 20,000 per dispersion/test-statistic cell for the non-binding Type I tables.
• Replaced the CRAN-bundled SSR trial-level simulation cache with compact precomputed summaries for tables and figures, while leaving full raw simulation caches available for local development or external archival.
• Converted the score-vs-Wald simulation article from interactive widget output to static vignette tables and figures backed by compact precomputed summaries, reducing the CRAN package size while leaving the full simulation cache available outside the CRAN build.
• Updated the SSR simulation study to compare Wald and score tests at the same nominal one-sided alpha of 0.025, and aligned the SSR power simulations with the score final-analysis recommendation.
• Added a checkpointed production generator for the SSR score-test cache so the long-running power and Type I simulations can be resumed from saved chunks.
• Updated the blinded-information diagnostics article to distinguish historical raw ML pathologies from the current MoM fallback behavior.

Missing data and imputation documentation

• Added documentation clarifying that the primary recurrent-event analysis is an observed-exposure negative binomial likelihood analysis. Under ignorable censoring / MAR assumptions, partially followed subjects contribute their observed events and exposure, and multiple imputation is not required simply because follow-up is censored.
• Expanded MNAR sensitivity-analysis guidance for recurrent-event endpoints, including the need to preserve censoring reason and planned remaining follow-up before post-dropout outcomes can be imputed.
• Added Keene, Roger, Hartley, and Kenward (2014) as the recurrent-event controlled-imputation reference for de facto / reference-based sensitivity analyses, and aligned the manuscript, slides, and simulation vignette around that framing.
• Updated the paper and slide materials to cite package articles as executable supplementary material for the fuller simulation grids and reporting.

Manuscript

• Converted the paper to the Quarto Journal of Statistical Software template, added a semi-real recurrent-event group sequential design example with tables and a figure, added formal R package citations, and simplified the Jensen correction grid table so the formula-heavy entries no longer break the PDF.

gsDesignNB 0.3.1

Robust NB fallback: method-of-moments replaces Poisson under genuine overdispersion

• mutze_test(), calculate_blinded_info(), and unblinded_ssr() now fall back to method-of-moments (MoM) estimation via estimate_nb_mom() when the maximum likelihood negative binomial fit does not converge or returns an extreme-overdispersion shape estimate. Previously, the Poisson fallback was used in both “near-Poisson” and “extreme-overdispersion” regimes; the latter is anti-conservative because the Poisson variance underestimates the true NB variance under genuine overdispersion. The MoM fallback computes the Wald standard error from the observed Fisher information formula \(\mathcal{I} = 1/(1/W_1 + 1/W_2)\) with \(W_g = \sum_i \mu_{g,i}/(1 + \hat{k}\mu_{g,i})\), preserving the NB variance structure without requiring ML convergence.
• mutze_test() gains a mom_threshold argument (default 20, corresponding to \(\hat{k} > 20\)) that controls when the MoM branch is triggered. The existing poisson_threshold default is reduced from 1000 to 50 (\(\hat{k} < 0.02\)) since NB and Poisson Wald standard errors are numerically indistinguishable at that point.
• All three functions now return an additional fallback element in their output ("ml", "mom", or "poisson") so that downstream simulation engines can record which estimator was used at each interim.

gsDesignNB 0.3.0

Sample size methodology deep dive (#14)

Jensen’s inequality correction for event gaps

• Applied a second-order Taylor correction to the effective rate formula when both dispersion (\(k > 0\)) and event gap (\(g > 0\)) are present. The naive formula \(\lambda/(1+\lambda g)\) overestimates the population-level effective rate due to Jensen’s inequality (subject-level frailty makes \(f(x)=x/(1+xg)\) concave). The corrected formula is \(\lambda_{\text{eff}} \approx \frac{\lambda}{1+\lambda g}(1 - k\lambda g/(1+\lambda g)^2)\).
• Correction applied in both sample_size_nbinom() and compute_info_at_time().
• Simulation study across multiple scenarios (10,000 replicates each) confirms the corrected design maintains nominal or conservative power, while the naive formula increasingly underpowers as \(k\) and \(g\) grow.

Documentation improvements

• Restructured the sample-size-nbinom vignette with a consistent notation table and comparison of Zhu-Lakkis, Friede-Schmidli, and Mutze et al. methods.
• Expanded average exposure derivation covering no-dropout, exponential dropout, max follow-up truncation, the \(Q\) variance inflation factor, and event gaps.
• Added a statistical information section covering per-subject Fisher information, total information, blinded vs unblinded estimation (ML and MoM), and the connection to sample size.
• Added simulation verification of average exposure in the vignette.
• Improved roxygen2 documentation for sample_size_nbinom(), calculate_blinded_info(), and compute_info_at_time() with @details sections, consistent notation, and cross-references.
• Updated verification-simulation vignette with a scenario sweep table and a discussion of why the correction is preferred despite partial cancellation with model-based SE bias.

Other

• Added DOIs to bibliography entries; added Schneider et al. (2013) reference.
• Switched pkgdown math rendering from MathJax (CDN) to KaTeX (bundled).

gsDesignNB 0.2.6

• First CRAN release.

gsDesignNB 0.2.5

• Added rr0 parameter to sample_size_nbinom() and blinded_ssr() to support non-inferiority and super-superiority testing.
• Changed default event_gap to 0 in nb_sim().

gsDesignNB 0.2.4

• Fix cut_date_for_completers() to support nb_sim_seasonal() output (no tte column).
• Correct calculate_blinded_info() blinded information calculation to use subject-level exposure.

gsDesignNB 0.2.3

• Fix toInteger.gsNB() to avoid unintended power changes by correctly recomputing information with max_followup, preserving delta1, and improving ratio-aware integer rounding.
• Vignette updates and documentation fixes.

gsDesignNB 0.2.2

Sample size and power

• sample_size_nbinom() computes sample size or power for fixed designs with two treatment groups. Supports piecewise accrual, exponential dropout, maximum follow-up, and event gaps. Implements the Zhu and Lakkis (2014) and Friede and Schmidli (2010) methods.

Group sequential designs

• gsNBCalendar() creates group sequential designs for negative binomial outcomes, optionally attaching calendar-time analysis schedules (via analysis_times) compatible with gsDesign. Inherits from both gsDesign and sample_size_nbinom_result classes.
• compute_info_at_time() computes statistical information for the log rate ratio at a given analysis time, accounting for staggered enrollment.
• toInteger() rounds sample sizes in a group sequential design to integers while respecting the randomization ratio.

Simulation

• nb_sim() simulates recurrent events for trials with piecewise constant enrollment, exponential failure rates, and piecewise exponential dropout. Supports negative binomial overdispersion via gamma frailty and event gaps.
• nb_sim_seasonal() simulates recurrent events where event rates vary by season (Spring, Summer, Fall, Winter).
• Group sequential simulation helpers: sim_gs_nbinom() runs repeated simulations with flexible cut rules via get_cut_date(), check_gs_bound() updates spending bounds based on observed information, and summarize_gs_sim() summarizes operating characteristics across analyses.

Interim data handling

• cut_data_by_date() censors follow-up at a specified calendar time and aggregates events per subject, adjusting for event gaps.
• get_analysis_date() finds the calendar time at which a target event count is reached.
• cut_completers() subsets data to subjects randomized by a specified date.
• cut_date_for_completers() finds the calendar time at which a target number of subjects have completed their follow-up.

Statistical inference

• mutze_test() fits a negative binomial (or Poisson) log-rate model and performs a Wald test for the treatment effect, following Mütze et al. (2019).

Blinded sample size re-estimation

• blinded_ssr() estimates blinded dispersion and event rate from interim data and re-calculates sample size to maintain power, following Friede and Schmidli (2010).
• calculate_blinded_info() estimates blinded statistical information for the log rate ratio from aggregated interim data.

Re-exports from gsDesign

• Re-exports gsDesign(), gsBoundSummary(), and common spending functions (sfHSD(), sfLDOF(), sfLDPocock(), and more) for convenience.