gsdesignnb

name

gsDesignNB

description

Guide users through sample size calculation, group sequential design, and simulation for clinical trials with negative binomial (recurrent event) outcomes using the gsDesignNB R package. Use this skill when the user asks about: negative binomial sample size, recurrent event trials, overdispersed counts, event gaps, rate ratios, Wald test for count data, seasonal event rates, blinded or unblinded sample size re-estimation, group sequential designs for negative binomial endpoints, or the Zhu-Lakkis method.

Sample Size and Simulation for Negative Binomial Outcomes with gsDesignNB

API reference

• Full function docs: references/llms.txt (built from local man pages)
• Workflow patterns: references/code_patterns.md

Key functions

Sample size calculation

• sample_size_nbinom() - Sample size/power for negative binomial outcomes (Zhu-Lakkis Method 3)

Group sequential design

• gsNBCalendar() - Group sequential design with calendar-time analysis schedule
• compute_info_at_time() - Statistical information at a given calendar time
• toInteger() - Round sample sizes to integers preserving allocation ratio
• check_gs_bound() - Check if group sequential bounds are crossed
• summarize_gs_sim() - Summarize operating characteristics from simulations

Simulation

• nb_sim() - Simulate recurrent events (Gamma-Poisson mixture)
• nb_sim_seasonal() - Simulate recurrent events with seasonal variation
• sim_gs_nbinom() - Simulate multiple group sequential trials

Data cutting and analysis timing

• cut_data_by_date() - Cut simulated data at a calendar date
• get_analysis_date() - Find date when target event count is reached
• get_cut_date() - Find earliest date satisfying multiple analysis criteria
• cut_date_for_completers() - Find date when target completers are reached
• cut_completers() - Cut data for completers analysis

Statistical testing and estimation

• mutze_test() - Wald test for treatment rate ratio (NB or Poisson)
• estimate_nb_mom() - Method of moments estimation for NB parameters
• calculate_blinded_info() - Blinded information and dispersion estimation

Sample size re-estimation

• blinded_ssr() - Blinded SSR using Friede & Schmidli method
• unblinded_ssr() - Unblinded SSR using observed group rates

Workflow patterns

For detailed code templates, read references/code_patterns.md.

Topics covered:

• Fixed sample size calculation with piecewise accrual, dropout, event gaps
• Power calculation from a fixed design
• Non-inferiority and super-superiority designs (rr0 parameter)
• Group sequential design with calendar-time analysis schedule
• Simulation of recurrent events and group sequential trials
• Seasonal event rate simulation
• Data cutting at interim/final analyses
• Wald test (mutze_test) for treatment rate ratio
• Blinded and unblinded information estimation at interim
• Blinded and unblinded sample size re-estimation
• Completers-based interim analysis
• Verification of theoretical vs. simulated operating characteristics

Important design considerations

• Dispersion parameter k: Controls overdispersion; k = 0 reduces to Poisson. Larger k means more overdispersion. Can be scalar (common) or length-2 vector (group-specific).
• Event gaps: After each event, patients are "off risk" for event_gap time units. This reduces effective exposure: lambda_eff = lambda / (1 + lambda * gap). Specified in the same time units as rates.
• Variance inflation factor Q: When follow-up varies across patients, Q = E[t^2] / E[t]^2 inflates the variance. sample_size_nbinom() handles this automatically.
• Rate parameterization: Rates lambda1 (control) and lambda2 (experimental) are events per unit time. The treatment effect is the rate ratio RR = lambda2/lambda1.
• Wald test (Mütze et al.): mutze_test() fits a negative binomial GLM with offset for log exposure. Falls back to Poisson when the NB dispersion estimate is very large (> poisson_threshold).
• Calendar-time analysis schedule: gsNBCalendar() takes analysis_times as calendar months. Information at each analysis depends on enrollment pattern, dropout, and follow-up.
• Spending time vs. information fraction: For group sequential designs, usTime/lsTime control alpha spending and may differ from the information fraction. This allows calendar-based or event-based spending schedules.
• Blinded information: calculate_blinded_info() uses the blinded (pooled) rate and dispersion to estimate information. Can produce extreme values when the NB MLE is unstable — bound dispersion or use planning values as a fallback.
• SSR: Blinded SSR (Friede & Schmidli) maintains the blind; unblinded SSR uses observed group rates for more accurate re-estimation but requires unblinding.
• check_gs_bound() info_scale: Use "blinded" (default) or "unblinded" to select which information drives bound updates at analysis time.