Confidence Intervals for Difference Between Two Poisson Rates

Confidence Intervals for Assessing Non-Inferiority with Assay Sensitivity in a Three-Arm Trial with Normally Distributed Endpoints

Various approaches including hypothesis test and confidence interval (CI) construction have been proposed to assess non-inferiority and assay sensitivity via a known fraction or pre-specified margin in three-arm trials with continuous or discrete endpoints. However, there is little work done on the construction of the non-inferiority margin from historical data and simultaneous generalized CIs (SGCIs) in a three-arm trial with the normally distributed endpoints. Based on the generalized fiducial method and the square-and-add method, we propose two simultaneous CIs for assessing non-inferiority and assay sensitivity in a three-arm trial. For comparison, we also consider the Wald-type Bonferroni simultaneous CI and parametric bootstrap simultaneous CI. An algorithm for evaluating the optimal sample size for attaining the pre-specified power is given. Simulation studies are conducted to investigate the performance of the proposed CIs in terms of their empirical coverage probabilities. An example taken from the mildly asthmatic study is illustrated using the proposed simultaneous CIs. Empirical results show that the proposed generalized fiducial method and the square-and-add method behave better than other two compared CIs.

Integrated safety analysis of filgotinib in patients with moderately to severely active rheumatoid arthritis receiving treatment over a median of 1.6 years

OBJECTIVE

To characterise safety of the Janus kinase-1 preferential inhibitor filgotinib in patients with moderately to severely active rheumatoid arthritis.

METHODS

Data were integrated from seven trials (NCT01668641, NCT01894516, NCT02889796, NCT02873936, NCT02886728, NCT02065700, NCT03025308). Results are from placebo (PBO)-controlled (through week (W)12) and long-term, as-treated (all available data for patients receiving ≥1 dose filgotinib 200 (FIL200) or 100 mg (FIL100) daily) datasets. We calculated exposure-adjusted incidence rates (EAIRs)/100 patient-years filgotinib exposure (100PYE) for treatment-emergent adverse events (TEAEs).

RESULTS

3691 patients received filgotinib for 6080.7 PYE (median 1.6, maximum 5.6 years). During the PBO-controlled period, TEAEs, including those of grade ≥3, occurred at comparable rates with filgotinib or PBO; long-term EAIRs of TEAEs grade ≥3 were 6.4 and 7.6/100PYE for FIL200 and FIL100. EAIRs for deaths were 0.6/100PYE for FIL200, FIL100 and PBO; long-term EAIRs were 0.5 and 0.3/100PYE for FIL200 and FIL100. EAIRs for serious infection were 3.9, 3.3 and 2.4/100PYE for FIL200, FIL100 and PBO; long-term EAIRs were 1.6 and 3.1/100PYE for FIL200 and FIL100. EAIRs for herpes zoster were 0.6, 1.1, and 1.1/100PYE for FIL200, FIL100 and PBO; long-term EAIRs were 1.8 and 1.1/100PYE for FIL200 and FIL100. EAIRs for major adverse cardiovascular events were 0, 1.7 and 1.1/100PYE for FIL200, FIL100 and PBO; long-term EAIRs were 0.4 and 0.6/100PYE for FIL200 and FIL100. No venous thromboembolism occurred during the PBO-controlled period; long-term EAIRs were 0.2 and 0/100PYE for FIL200 and FIL100.

CONCLUSIONS

Over a median of 1.6 and maximum of 5.6 years of exposure, safety/tolerability of FIL200 and FIL100 were similar, with a lower incidence of infections with FIL200 among the long-term, as-treated dataset.

Confidence intervals for exposure-adjusted rate differences in randomized trials

Exposure-adjusted event rate is a quantity often used in clinical trials to describe average event count per unit of person-time. The event count may represent the number of patients experiencing first (incident) event episode, or the total number of event episodes, including recurring events. For inference about difference in the exposure-adjusted rates between interventions, many methods of interval estimation rely on the assumption of Poisson distribution for the event counts. These intervals may suffer from substantial undercoverage both, asymptotically due to extra-Poisson variation, and in the settings with rare events even when the Poisson assumption is satisfied. We review asymptotically robust methods of interval estimation for the rate difference that do not depend on distributional assumptions for the event counts, and propose a modification of one of these methods. The new interval estimator has asymptotically nominal coverage for the rate difference with an arbitrary distribution of event counts, and good finite sample properties, avoiding substantial undercoverage with small samples, rare events, or over-dispersed data. The proposed method can handle covariate adjustment and can be implemented with commonly available software. The method is illustrated using real data on adverse events in a clinical trial.

Simultaneous confidence interval for assessing non-inferiority with assay sensitivity in a three-arm trial with binary endpoints

A three-arm trial including an experimental treatment, an active reference treatment and a placebo is often used to assess the non-inferiority (NI) with assay sensitivity of an experimental treatment. Various hypothesis-test-based approaches via a fraction or pre-specified margin have been proposed to assess the NI with assay sensitivity in a three-arm trial. There is little work done on confidence interval in a three-arm trial. This paper develops a hybrid approach to construct simultaneous confidence interval for assessing NI and assay sensitivity in a three-arm trial. For comparison, we present normal-approximation-based and bootstrap-resampling-based simultaneous confidence intervals. Simulation studies evidence that the hybrid approach with the Wilson score statistic performs better than other approaches in terms of empirical coverage probability and mesial-non-coverage probability. An example is used to illustrate the proposed approaches.

Evaluating the performance of simultaneous stepwise confidence intervals for the difference between two Poisson rates

We consider the problem of simultaneously estimating Poisson rate differences via applications of the Hsu and Berger stepwise confidence interval method (termed HBM), where comparisons to a common reference group are performed. We discuss continuity-corrected confidence intervals and investigate the HBM performance with a moment-based confidence interval, and uncorrected and corrected for continuity Wald and Pooled confidence intervals. Using simulations, we compare nine individual confidence intervals in terms of coverage probability and the HBM with nine intervals in terms of family-wise error rate and overall and local power. The simulations show that these statistical properties depend highly on parameter settings.