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Supplementary Figure from Updated Overall Survival of Ribociclib plus Endocrine Therapy versus Endocrine Therapy Alone in Pre- and Perimenopausal Patients with HR+/HER2− Advanced Breast Cancer in MONALEESA-7: A Phase III Randomized Clinical Trial

Purpose:Ribociclib plus endocrine therapy (ET) demonstrated a statistically significant progression-free survival and overall survival (OS) benefit in the phase III MONALEESA-7 trial of pre-/perimenopausal patients with hormone receptor (HR)-positive (HR+), HER2-negative (HER2−) advanced breast cancer (ABC). The median OS was not reached in the ribociclib arm in the protocol-specified final analysis; we hence performed an exploratory OS and additional outcomes analysis with an extended follow-up (median, 53.5 months).Patients and Methods:Patients were randomized to receive ET [goserelin plus nonsteroidal aromatase inhibitor (NSAI) or tamoxifen] with ribociclib or placebo. OS was evaluated with a stratified Cox proportional hazard model and summarized with Kaplan–Meier methods.Results:The intent-to-treat population included 672 patients. Median OS was 58.7 months with ribociclib versus 48.0 months with placebo [hazard ratio = 0.76; 95% confidence interval (CI), 0.61–0.96]. Kaplan–Meier estimated OS at 48 months was 60% and 50% with ribociclib and placebo, respectively. Subgroup analyses were generally consistent with the OS benefit, including patients who received NSAI and patients aged less than 40 years. Subsequent antineoplastic therapies following discontinuation were balanced between the ribociclib (77%) and placebo (78%) groups. Use of cyclin-dependent kinase 4/6 inhibitors after discontinuation was higher with placebo (26%) versus ribociclib (13%). Time to first chemotherapy was significantly delayed with ribociclib versus placebo. No drug–drug interactions were observed between ribociclib and either NSAI.Conclusions:Ribociclib plus ET continued to show significantly longer OS than ET alone in pre-/perimenopausal patients, including patients aged less than 40 years, with HR+/HER2− ABC with 53.5 months of median follow-up (ClinicalTrials.gov, NCT02278120).

Supplementary Table from Updated Overall Survival of Ribociclib plus Endocrine Therapy versus Endocrine Therapy Alone in Pre- and Perimenopausal Patients with HR+/HER2− Advanced Breast Cancer in MONALEESA-7: A Phase III Randomized Clinical Trial

In the context of clinical trials, there is interest in the treatment effect for subpopulations of patients defined by intercurrent events, namely disease-related events occurring after treatment initiation that affect either the interpretation or the existence of endpoints. With the principal stratum strategy, the ICH E9(R1) guideline introduces a formal framework in drug development for defining treatment effects in such subpopulations. Statistical estimation of the treatment effect can be performed based on the principal ignorability assumption using multiple imputation approaches. Principal ignorability is a conditional independence assumption that cannot be directly verified; therefore, it is crucial to evaluate the robustness of results to deviations from this assumption. As a sensitivity analysis, we propose a joint model that multiply imputes the principal stratum membership and the outcome variable while allowing different levels of violation of the principal ignorability assumption. We illustrate with a simulation study that the joint imputation model-based approaches are superior to naive subpopulation analyses. Motivated by an oncology clinical trial, we implement the sensitivity analysis on a time-to-event outcome to assess the treatment effect in the subpopulation of patients who discontinued due to adverse events using a synthetic dataset. Finally, we explore the potential usage and provide interpretation of such analyses in clinical settings, as well as possible extension of such models in more general cases.

Rationale: Most studies determining the prevalence of airway obstruction are limited to short time periods.Objectives: Because temporal trends of obstruction in populations are largely unknown, we determined the prevalence of airway obstruction over 20 years in yearly general population samples in Switzerland between 1993 and 2012.Methods: We analyzed data of 85,789 participants aged 35 years and older who provided spirometric measurements as part of the LuftiBus lung function campaign. We linked data from the 2003-2012 period to the Swiss National Cohort to adjust for annual population differences. Spirometry was performed without bronchodilation, according to American Thoracic Society guidelines. We used Global Lung Initiative (GLI) and Hankinson reference equations to identify obstruction.Results: Obstruction prevalence increased between 1993 and 2012 from 6.1% (95% confidence interval [CI], 5.5 to 6.7) to 15.6% (95% CI, 13.8 to 17.3) based on GLI estimates and from 5.3% (95% CI, 4.7 to 5.9) to 15.4% (95% CI, 13.6 to 17.1) based on Hankinson estimates. When adjusted for participant demographics, air pollutant and occupational exposures, altitude, and season, the prevalence ratios of obstruction were 1.54 (95% CI, 1.22 to 1.93) and 1.65 (95% CI, 1.33 to 2.04) for GLI- and Hankinson-defined airway obstruction, respectively, for 2012 compared with 2003.Conclusions: Though prebronchodilator measurements likely overestimate the prevalence of airway obstruction in absolute terms compared with post-bronchodilator measurements, we found an increase in airway obstruction prevalence. Even with adjustment for several well-known risk factors for obstruction to make the populations across the years more comparable, we still saw a statistically significant increase in prevalence over this time period.

Purpose: Ribociclib plus endocrine therapy (ET) demonstrated a statistically significant progression-free survival and overall survival (OS) benefit in the phase III MONALEESA-7 trial of pre-/perimenopausal patients with hormone receptor (HR)-positive (HR+), HER2-negative (HER2−) advanced breast cancer (ABC). The median OS was not reached in the ribociclib arm in the protocol-specified final analysis; we hence performed an exploratory OS and additional outcomes analysis with an extended follow-up (median, 53.5 months). Patients and Methods: Patients were randomized to receive ET [goserelin plus nonsteroidal aromatase inhibitor (NSAI) or tamoxifen] with ribociclib or placebo. OS was evaluated with a stratified Cox proportional hazard model and summarized with Kaplan–Meier methods. Results: The intent-to-treat population included 672 patients. Median OS was 58.7 months with ribociclib versus 48.0 months with placebo [hazard ratio = 0.76; 95% confidence interval (CI), 0.61–0.96]. Kaplan–Meier estimated OS at 48 months was 60% and 50% with ribociclib and placebo, respectively. Subgroup analyses were generally consistent with the OS benefit, including patients who received NSAI and patients aged less than 40 years. Subsequent antineoplastic therapies following discontinuation were balanced between the ribociclib (77%) and placebo (78%) groups. Use of cyclin-dependent kinase 4/6 inhibitors after discontinuation was higher with placebo (26%) versus ribociclib (13%). Time to first chemotherapy was significantly delayed with ribociclib versus placebo. No drug–drug interactions were observed between ribociclib and either NSAI. Conclusions: Ribociclib plus ET continued to show significantly longer OS than ET alone in pre-/perimenopausal patients, including patients aged less than 40 years, with HR+/HER2− ABC with 53.5 months of median follow-up (ClinicalTrials.gov, NCT02278120).

Background: MONALEESA-7 (NCT02278120), the first large randomized phase III clinical trial dedicated to investigating a cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) plus ET vs ET + placebo (PBO) in pre- or perimenopausal patients with HR+/HER2− ABC, previously demonstrated a statistically significant improvement in OS with the addition of ribociclib (RIB) to ET vs PBO + ET (median, not reached vs 40.9 months; HR, 0.71 [95% CI, 0.54-0.95]; P = .00973; Im SA, et al. N Engl J Med. 2019). This concluded the protocol-defined final analysis of OS and the patients and investigators were unblinded to their treatment assignment allowing patients on the PBO arm to cross-over to RIB treatment. Longer follow-up allows for more events to further characterize the long-term survival benefits. Here we report an exploratory update of OS after a minimum of ~ four years of follow-up, an additional 20 months since the last report. Methods: Pre- or perimenopausal patients with HR+/HER2− ABC were randomized 1:1 to receive RIB or PBO plus goserelin with either a nonsteroidal aromatase inhibitor (NSAI; letrozole or anastrozole) or tamoxifen. RIB is approved in combination with an NSAI in pre- or perimenopausal patients. Patients who had received a prior CDK4/6i or ET in the advanced setting were excluded. Patients who received ET in the (neo)adjuvant setting or ≤ 1 prior line of chemotherapy for advanced disease were eligible to enroll. Updated OS were evaluated by Cox proportional hazards model and summarized using Kaplan-Meier methods. Additional post-progression endpoints such as progression-free survival 2 (PFS2), time to chemotherapy (CT) and CT-free survival were also evaluated and summarized. Results: The data cutoff for this updated OS analysis was 29 June 2020, and the median follow-up was 53.5 mo (min, 46.9 mo). These updated results with extended follow-up demonstrated an OS benefit with RIB + ET vs PBO + ET (median, 58.7 vs 48.0 mo; HR, 0.76 [95% CI, 0.61-0.96]). In patients receiving an NSAI, a similar OS benefit was observed with RIB + NSAI vs PBO + NSAI (median, 58.7 vs 47.7 mo; HR, 0.80 [95% CI, 0.62-1.04]). The survival benefit shown in subgroup analyses was consistent with the intent-to-treat (ITT) population. PFS2, time to chemotherapy (CT), and CT-free survival for the ITT and NSAI populations are in the Table. Among the patients who discontinued study treatment, 77.3% and 78.1% in the RIB + ET vs PBO + ET arms received a subsequent antineoplastic therapy, respectively, and 12.9% and 26.1% received a subsequent line of CDK4/6i. Additionally there were 15 patients in the PBO arm that crossed over to the RIB arm following unblinding and prior to disease progression. Conclusions: With an extended follow-up of more than 4 years, RIB + ET continued to demonstrate a clinically relevant OS benefit compared with ET alone in pre- or perimenopausal patients with a median OS ~5 years with RIB +ET in HR+/HER2− ABC. A similar benefit with RIB was observed for PFS2, time to CT, and CT-free survival. ITTNSAI cohortRIB + ETn=335PBO + ETn=337RIB + NSAIn=248PBO + NSAIn=247PFS2Events, n (%)177 (52.8)221 (65.6)131 (52.8)159 (64.4)Median, mo44.231.043.630.4HR (95% CI)0.68 (0.56-0.83)0.69 (0.55-0.87)Time to first CTEvents, n (%)144 (43.0)173 (51.3)107 (43.1)129 (52.2)Median, mo50.936.850.936.0ITT HR(95% CI)0.69 (0.56-0.87)0.66 (0.51-0.85)CT-free survivalEvents, n (%)190 (56.7)236 (70.0)139 (56.0)169 (68.4)Median, mo42.426.442.525.9HR (95% CI)0.67 (0.55-0.81)0.64 (0.51-0.81) Citation Format: Debu Tripathy, Seock-Ah Im, Marco Colleoni, Fabio Franke, Aditya Bardia, Nadia Harbeck, Sara Hurvitz, Louis Chow, Joohyuk Sohn, Keun Seok Lee, Saul Campos-Gomez, Rafael Villanueva Vazquez, Kyung Hae Jung, K Govind Babu, Paul Wheatley-Price, Michelino De Laurentiis, Young-Hyuck Im, Sherko Kümmel, Nagi El-Saghir, Mei-Ching Liu, Sharonjeet Kaur, Claudia Gasch, Craig Wang, Yongyu Wang, Arunava Chakravartty, Yen-Shen Lu. Updated overall survival (OS) results from the phase III MONALEESA-7 trial of pre- or perimenopausal patients with hormone receptor positive/human epidermal growth factor receptor 2 negative (HR+/HER2−) advanced breast cancer (ABC) treated with endocrine therapy (ET) ± ribociclib [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD2-04.

BACKGROUND: Ribociclib plus fulvestrant demonstrated significant progression-free survival (PFS) and overall survival (OS) benefits in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC). Here we present a new landmark in survival follow-up for a phase III cyclin-dependent kinases 4 and 6 inhibitor clinical trial in patients with ABC (median, 56.3 months). PATIENTS AND METHODS: This phase III, randomized, double-blind, placebo-controlled trial was conducted at 174 sites (30 countries). Patients were men and postmenopausal women (age ≥18 years) with histologically/cytologically confirmed HR+/HER2- ABC. Patients could have received ≤1 line of endocrine therapy (ET) but no chemotherapy for ABC. Patients, assigned 2:1, were stratified by the presence/absence of liver/lung metastases and previous ET. Patients received intramuscular fulvestrant (500 mg, day 1 of each 28-day cycle plus day 15 of cycle 1) with oral ribociclib (600 mg/day, 3 weeks on, 1 week off) or placebo. Efficacy analyses were by intention to treat. Safety was assessed in patients receiving ≥1 dose study treatment. OS was a secondary endpoint. MONALEESA-3 is registered with ClinicalTrials.gov (NCT02422615; no longer enrolling). RESULTS: Between 18 June 2015 and 10 June 2016, 726 patients were randomly assigned (484, ribociclib; 242, placebo). At data cut-off (30 October 2020), median OS (mOS) was 53.7 months (ribociclib) versus 41.5 months (placebo) [hazard ratio (HR), 0.73; 95% confidence interval (CI) 0.59-0.90]. Subgroup analyses were consistent with overall population. In the first-line setting, most patients in the ribociclib arm (∼60%) lived longer than median follow-up; mOS was 51.8 months in the placebo arm (HR, 0.64; 95% CI 0.46-0.88). In the second-line setting, mOS was 39.7 months (ribociclib) versus 33.7 months (placebo) (HR, 0.78; 95% CI 0.59-1.04). No apparent drug-drug interaction between ribociclib and fulvestrant or new safety signals were observed. CONCLUSIONS: This analysis reported extended OS follow-up in MONALEESA-3. mOS was ∼12 months longer in patients with HR+/HER2- ABC treated with ribociclib plus fulvestrant compared with fulvestrant monotherapy. ispartof: ANNALS OF ONCOLOGY vol:32 issue:8 pages:1015-1024 ispartof: location:England status: published

TPS1107 Background: ALP (α-selective PI3K inhibitor and degrader) is approved with FUL for pts with PIK3CA-mut HR+, HER2− ABC. HG is a known on-target effect of PI3K inhibition, manageable with oral anti-HG agents such as MET and dose interruptions/modifications of ALP and reversible upon discontinuation of ALP. Although HG management guidelines were refined throughout the SOLAR-1 and BYLieve studies evaluating ALP + FUL/letrozole, there remains a need for optimized strategies beyond initial MET therapy that offers earlier and more sustained improvement of HG, particularly for pts at an increased risk for severe HG. In preclinical models, the addition of a SGLT2 inhibitor to ALP (+/− MET) reduced HG while maintaining ALP efficacy. The aim of this study is to evaluate the safety and efficacy of prophylactic MET XR +/− the SGLT2 inhibitor DAPA in reducing severe HG in pts with PIK3CA-mut HR+, HER2− ABC on ALP + FUL with an increased risk for severe HG (grade ≥ 3) on ALP. Methods: EPIK-B4 is a Phase II, randomized (1:1), open-label, active-controlled study assessing the efficacy and safety of MET XR +/− DAPA (starting at Cycle 1 Day 1 [C1D1]) with ALP (300 mg orally [PO], once daily [QD], starting at C1D8) + FUL (500 mg intramuscularly on C1D1, C1D15, and D1 of subsequent cycles) in pts (N ≈ 132) with HR+, HER2–, PIK3CA-mut ABC after progression on/after endocrine-based treatment (Tx). MET XR is administered PO starting at 500 mg QD (titrated up to 2 g QD) and MET XR + DAPA starting at 500 mg and 5 mg QD (titrated up to 2 g and 10 mg QD), respectively. Eligible pts include adult men or postmenopausal women with confirmed HR+, HER2–, PIK3CA-mut ABC and ≥ 1 risk factor for severe HG (diabetes [fasting plasma glucose (FPG) ≥ 126 mg/dL and/or HbA1c ≥ 6.5%], prediabetes [FPG ≥ 100 to < 126 mg/dL and/or HbA1c 5.7% to < 6.5%], obesity [BMI ≥ 30], age ≥ 75 years); prior endocrine-based Tx (eg, FUL or oral SERD) was permitted. Randomization is stratified by baseline diabetes status. Key exclusion criteria include > 1 line of Tx in the metastatic setting; prior chemotherapy (metastatic setting) or PI3K, mTOR, or AKT inhibitor; type I or II diabetes requiring Tx; or antecedent of pancreatitis or severe cutaneous reaction. The primary endpoint is the occurrence of severe HG (grade ≥ 3 [glucose > 250 mg/dL] based on laboratory assessments) over the first 8 weeks of ALP + FUL. Secondary endpoints include progression-free survival, overall response and clinical benefit rates with confirmed response, safety, and tolerability. A biomarker analysis is planned as an exploratory objective. Recruitment is ongoing with enrollment planned in 56 sites across 8 countries; completion of primary data collection is anticipated in 2023. Clinical trial information: NCT04899349; EUDRACT#2021-001908-15.

The kernel estimator is known not to be adequate for estimating the density of a positive random variable X. The main reason is the well-known boundary bias problems that it suffers from, but also its poor behaviour in the long right tail that such a density typically exhibits. A natural approach to this problem is to first estimate the density of the logarithm of X, and obtaining an estimate of the density of X using standard results on functions of random variables (`back-transformation'). Although intuitive, the basic application of this idea yields very poor results, as was documented earlier in the literature. In this paper, the main reason for this underachievement is identified, and an easy fix is suggested. It is demonstrated that combining the transformation with local likelihood density estimation methods produces very good estimators of R+-supported densities, not only close to the boundary, but also in the right tail. The asymptotic properties of the proposed `local likelihood transformation kernel density estimators' are derived for a generic transformation, not only for the logarithm, which allows one to consider other transformations as well. One of them, called the `probex' transformation, is given more focus. Finally, the excellent behaviour of those estimators in practice is evidenced through a comprehensive simulation study and the analysis of several real data sets. A nice consequence of articulating the method around local-likelihood estimation is that the resulting density estimates are typically smooth and visually pleasant, without oversmoothing important features of the underlying density.

1001 Background: The Phase III MONALEESA-3 trial (NCT02422615) previously demonstrated a statistically significant improvement in OS with RIB, a cyclin-dependent kinase 4/6 inhibitor (CDK4/6i), plus FUL compared with placebo (PBO) plus FUL as first-line (1L) or second-line (2L) treatment in postmenopausal pts with HR+/HER2− ABC (median, not reached vs 40.0 mo; hazard ratio [HR], 0.72; 95% CI, 0.57-0.92, P =.00455). This analysis was final per the protocol; following the unblinding of the study, pts still on study treatment in the PBO arm were allowed to cross over to the RIB arm. We report an exploratory analysis of OS after an additional median 16.9 mo of follow-up, allowing for further characterization of long-term survival benefits of RIB. Methods: Postmenopausal pts with HR+/HER2− ABC were randomized 2:1 to receive RIB + FUL or PBO + FUL in 1L and 2L settings. Updated OS was evaluated by Cox proportional hazards model and summarized using Kaplan-Meier methods. Additional postprogression endpoints such as progression-free survival 2 (PFS2), time to chemotherapy (CT), and CT-free survival were also evaluated and summarized. Results: At the data cutoff (Oct 30, 2020), the median follow-up was 56.3 mo (min, 52.7 mo) and 68 (14.0%) and 21 (8.7%) patients were still on treatment in the RIB vs PBO arms, respectively. With this extended follow-up, RIB + FUL continued to demonstrate an OS benefit vs PBO + FUL (median, 53.7 vs 41.5 mo; HR, 0.73; 95% CI, 0.59-0.90). RIB + FUL had prolonged OS vs PBO + FUL in the 1L (median, not reached vs 51.8 mo; HR, 0.64; 95% CI, 0.46-0.88) and 2L subgroups (median, 39.7 vs 33.7 mo; HR, 0.78; 95% CI, 0.59-1.04). Subgroup analyses also showed a consistent OS benefit compared with the intent-to-treat (ITT) population for most subgroups. PFS2, time to CT, and CT-free survival for the ITT population favored RIB + FUL (Table). Among pts who discontinued study treatment, 81.9% and 86.4% received a next-line subsequent antineoplastic therapy, while 14.0% and 30.0% received a CDK4/6i as any subsequent line in the RIB vs PBO arms, respectively. No new safety signals were observed. Conclusions: The previously demonstrated robust and clinically meaningful OS benefit with RIB + FUL compared with PBO + FUL was maintained after almost 5 years of follow-up in postmenopausal pts with HR+/HER2− ABC. The OS benefit of RIB was observed in the 1L and 2L subgroups, which further supports the use of RIB in these populations. The results also demonstrated a significant delay in the use of subsequent CT with RIB vs PBO. Clinical trial information: NCT02422615 .[Table: see text]

In spatial statistics, data are often collected at different spatial resolutions. Often, it is of interest to (a) carry out multivariate analysis when variables are sampled at different locations, (b) model data collected at misaligned areas, or (c) unravel common latent factors by jointly modelling point and areal data. In this paper, we establish a linkage between the generalized spatial fusion model framework and the various change‐of‐support problems, and we outline how the framework can be adapted in these situations. Moreover, we propose an efficient fusion model implementation by exploiting advantages of nearest neighbour Gaussian process and the Stan modelling language. Our simulation shows that the computational efficiency is several times higher in the new implementation compared with original implementation. We illustrate the performance gain in practice using a case study, which models daily precipitation in Switzerland based on rain gauge and radar data.

In modern spatial statistics, the structure of data that is collected has become more heterogeneous. Depending on the type of spatial data, different modeling strategies for spatial data are used. For example, a kriging approach for geostatistical data; a Gaussian Markov random field model for lattice data; or a log Gaussian Cox process for point-pattern data. Despite these different modeling choices, the nature of underlying scientific data-generating (latent) processes is often the same, which can be represented by some continuous spatial surfaces. In this paper, we introduce a unifying framework for process-based multivariate spatial fusion models. The framework can jointly analyze all three aforementioned types of spatial data (or any combinations thereof). Moreover, the framework accommodates different conditional distributions for geostatistical and lattice data. We show that some established approaches, such as linear models of coregionalization, can be viewed as special cases of our proposed framework. We offer flexible and scalable implementations in R using Stan and INLA. Simulation studies confirm that the predictive performance of latent processes improves as we move from univariate spatial models to multivariate spatial fusion models. The introduced framework is illustrated using a cross-sectional study linked with a national cohort dataset in Switzerland, we examine differences in underlying spatial risk patterns between respiratory disease and lung cancer., Comment: 33 pages, 5 figures

The ovine developmental model represents the standard in vivo model for studies involving maternofetal physiology, amniotic fluid (AF) research, and fetal cell therapy prior to human clinical use. Although being close to the human fetal anatomy, 2 separate extraembryonic fluid compartments remain during gestation, known as the amnion and the allantois. A clear distinction between AF versus allantoic fluid (AL) is therefore indispensable for correct scientific conclusions with regard to human translation. In the presented study, the biochemical composition of AF and AL was evaluated in ovine gravid uteri postmortem (n = 31) over the entire gestation. Four parameters, consisting of Na+, Cl–, Mg2+, and total protein, have been found to allow for specific discrimination of the 2 fetal fluids at all gestational phases and therefore as potential surrogate parameters for gestational age. In addition, volumetric changes of the developing fetus and the 2 fetal fluid cavities were analyzed by contrast-enhanced computed tomography (n = 12). AF showed a significant, linear volumetric increase over gestation, whereas AL volume maintained relatively static independent of gestational age. These results serve as a basis for future studies by providing surrogate markers enabling a reliable distinction of isolated fetal fluids and contained cells in the ovine developmental model over the entire gestation.

The availability of geo-referenced data increased dramatically in recent years, motivating the use of spatial statistics in a variety of research fields, including epidemiology, environmental science, remote sensing, and economics. Combining data measured at both point and areal support can improve parameter estimation and increase prediction accuracy. We propose a new generalized spatial fusion model framework for jointly analyzing point and areal data. Assuming a common latent spatial process, we take a Bayesian hierarchical approach to model both types of data without distributional constraints. The models are implemented with nearest neighbor Gaussian process in Stan modeling language to increase computational efficiency and flexibility. Our simulation study shows that generalized fusion models under this framework model the latent process better than spatial process models. We identify scenarios where fusion models can offer large improvements. We then apply the framework to epidemiological data to identify the spatial risk pattern of respiratory diseases and lung cancer in Canton of Zurich, Switzerland.

Information from historical trials is important for the design, interim monitoring, analysis, and interpretation of clinical trials. Meta-analytic models can be used to synthesize the evidence from historical data, which are often only available in aggregate form. We consider evidence synthesis methods for trials with recurrent event endpoints, which are common in many therapeutic areas. Such endpoints are typically analyzed by negative binomial regression. However, the individual patient data necessary to fit such a model are usually unavailable for historical trials reported in the medical literature. We describe approaches for back-calculating model parameter estimates and their standard errors from available summary statistics with various techniques, including approximate Bayesian computation. We propose to use a quadratic approximation to the log-likelihood for each historical trial based on 2 independent terms for the log mean rate and the log of the dispersion parameter. A Bayesian hierarchical meta-analysis model then provides the posterior predictive distribution for these parameters. Simulations show this approach with back-calculated parameter estimates results in very similar inference as using parameter estimates from individual patient data as an input. We illustrate how to design and analyze a new randomized placebo-controlled exacerbation trial in severe eosinophilic asthma using data from 11 historical trials.

The prevalence of anthelmintic resistance has increased in recent years, as a result of the extensive use of anthelmintic drugs to reduce the infection of parasitic worms in livestock. In order to detect the resistance, the number of parasite eggs in animal faeces is counted. Typically a subsample of the diluted faeces is examined, and the mean egg counts from both untreated and treated animals are compared. However, the conventional method ignores the variabilities introduced by the counting process and by different infection levels across animals. In addition, there can be extra zero counts, which arise as a result of the unexposed animals in an infected population or animals. In this paper, we propose the zero-inflated Bayesian hierarchical models to estimate the reduction in faecal egg counts. The simulation study compares the Bayesian models with the conventional faecal egg count reduction test and other methods such as bootstrap and quasi-Poisson regression. The results show the Bayesian models are more robust and they perform well in terms of both the bias and the coverage. We further illustrate the advantages of our proposed model using a case study about the anthelmintic resistance in Swedish sheep flocks.