Application of estimand framework to the design and analysis of multi-regional clinical trials.

With the increasing globalization of drug development and the publication of the International Council for Harmonisation (ICH) E17 guideline (ICH International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use 2017), multi-regional clinical trials (MRCTs) have become a preferred option to accelerate the availability of new medical products by design, execution and simultaneous submission under one protocol. MRCTs, with the participation of all major regions including countries from both developed and emerging markets, surely make new drug development more efficient. Even though the proposed estimand framework (ICH E9 (R1) (2019), came later in 2019 and was not mentioned in ICH E17, the application of the estimand framework has the potential to enhance the design, execution, and analysis in MRCTs. Defining an estimand within the regional context in MRCTs is an important issue that requires careful consideration. Given that consistency evaluation of treatment effects across regions is critical in MRCTs, the utilization of the estimand framework for regional consistency evaluation is also worth discussion. This paper aims to address these two questions. The five attributes of the estimand definition are discussed within a multi-regional context. It is imperative to thoroughly consider regional intrinsic/extrinsic factors when planning the estimand and estimation of MRCTs. A holistic approach is summarized to conduct consistency evaluation. When a regional inconsistency is observed, the possible reasons need to be further explored under five attributes of the estimand framework. Two real case studies are discussed to illustrate the application of the estimand framework in the consistency evaluation.

Application of estimand framework in ICH E9 (R1) to vaccine trials.

Over the past decades, the primary interest in vaccine efficacy or immunogenicity evaluation mostly focuses on the biological effect of immunization in complying with the vaccination schedule in a targeted population. The safety questions, which are essential for vaccines as they are generally given to large healthy populations, need to be clearly defined to reflect the risk assessment of interest. ICH E9 (R1) provides a structured framework to clarify the clinical questions and formulate the treatment effect as an estimand. This paper applies the estimand framework to vaccine clinical trials on common clinical questions regarding efficacy, immunogenicity, and safety.

Application of estimand framework in ICH E9 (R1) to safety evaluation.

Defining the right question of interest is important to a clinical study. ICH E9 (R1) introduces the framework of an estimand and its five attributes, which provide a basis for connecting different components of a study with its clinical questions. Most of the applications of the estimand framework focus on efficacy instead of safety assessment. In this paper, we expand the estimand framework into the safety evaluation and compare/contrast the similarity and differences between safety and efficacy estimand. Furthermore, we present and discuss applications of a safety estimand to oncology trials and pooled data analyses. At last, we also discuss the potential usage of safety estimand to handle the impacts of COVID-19 pandemic on safety assessment.

Estimation of causal effect in integrating randomized clinical trial and observational data - An example application to cardiovascular outcome trial.

Safety evaluation of drug development is a comprehensive process across the product lifecycle. While a randomized clinical trial (RCT) can provide high-quality data to assess the efficacy and safety of a new intervention, the pre-marketing trials are limited in statistical power to detect causal elevation of rare but potentially serious adverse events. On the other hand, real-world data (RWD) sources play a critical role in further understanding the safety profile of the new intervention. Bringing together the breadth and strength of RWD and RCT data, we can maximize the utility of RWD and answer broader questions. In this manuscript, we propose a three-step statistical framework to corroborate findings from both RCT and RWD for evaluating important safety concerns identified in the pre-marketing setting. By the proposed approach, we first match the observational study to RCT, then the causal estimation is validated via the matched observational study with the target RCT by targeted maximum likelihood estimation (TMLE) method, and lastly the evidence from RCT and RWD can be combined in an integrative analysis. A potential application to cardiovascular outcome trials for type 2 diabetes mellitus is illustrated. Finally, simulation results suggest that the heterogeneity of patient population from RCT and RWD can lead to varying degrees of treatment effect estimation and the proposed approach may be able to mitigate such difference in the integrative analysis.

Bayesian hierarchical model for safety signal detection in multiple clinical trials.

Clinical safety signal detection is of great importance in establishing the safety profile of new drugs and biologics during drug development. Bayesian hierarchical meta-analysis has proven to be a very effective method of identifying potential safety signals by considering the hierarchical structure of clinical safety data from multiple randomized clinical trials conducted under an Investigational New Drug (IND) application or Biological License Application (BLA). This type of model can integrate information across studies, for instance by grouping related adverse events using the MedDRA system-organ-class (SOC) and preferred terms (PT). It therefore improves the precision of parameter estimates compared to models that do not consider the hierarchical structure of the safety data. We propose to extend an existing four-stage Bayesian hierarchical model and consider the exposure adjusted incidence rate, assuming the number of adverse events (AEs) follows a Poisson distribution. The proposed model is applied to a real-world example, using data from three randomized clinical trials of a neuroscience drug and examine in three simulation studies motivated by real-world examples. Comparison is made between the proposed method and other existing methods. The simulation results indicate that our proposed model outperforms other two candidate models in terms of power and false detection rate.

A predictive Bayesian approach to the design and analysis of bridging studies.

Pharmaceutical product development culminates in confirmatory trials whose evidence for the product's efficacy and safety supports regulatory approval for marketing. Regulatory agencies in countries whose patients were not included in the confirmatory trials often require confirmation of efficacy and safety in their patient populations, which may be accomplished by carrying out bridging studies to establish consistency for local patients of the effects demonstrated by the original trials. This article describes and illustrates an approach for designing and analyzing bridging studies that fully incorporates the information provided by the original trials. The approach determines probability contours or regions of joint predictive intervals for treatment effect and response variability, or endpoints of treatment effect confidence intervals, that are functions of the findings from the original trials, the sample sizes for the bridging studies, and possible deviations from complete consistency with the original trials. The bridging studies are judged consistent with the original trials if their findings fall within the probability contours or regions. Regulatory considerations determine the region definitions and appropriate probability levels. Producer and consumer risks provide a way to assess alternative region and probability choices. [Supplemental materials are available for this article. Go to the Publisher's online edition of the Journal of Biopharmaceutical Statistics for the following free supplemental resource: Appendix 2: R code for Calculations.].

Handling missing data in vaccine clinical trials for immunogenicity and safety evaluation.

In clinical trials, study subjects are usually followed for a period of time after treatment, and the missing data issue is almost inevitable due to various reasons, including early dropout or lost-to-follow-up. It is important to take the missing data into consideration at the study design stage to minimize its occurrence throughout the study and to prospectively account for it in the analyses. There are many methods available in the literature that are designed to handle the missing data issue under various settings. Vaccines are biological products that are primarily designed to prevent infectious diseases, and are different from pharmaceutical products, which traditionally have been chemical products designed to treat or cure diseases. While a lot of similarities exist between clinical trials for vaccines and those for pharmaceutical products, there are some unique issues in vaccine trials, including how to handle the missing data, which calls for special considerations. In this report we present a variety of statistical approaches for analyses of vaccine immunogenicity and safety trials in the presence of missing data. The methods are illustrated with numerical simulations and vaccine trial examples.

Effect of a zoster vaccine on herpes zoster-related interference with functional status and health-related quality-of-life measures in older adults.

OBJECTIVE: To determine the efficacy of a zoster vaccine on herpes zoster (HR)-related interference with activities of daily living (ADLs) and health-related quality of life (HRQL). DESIGN: Randomized double-blind placebo controlled trial. SETTING: Twenty-two U.S. sites. PARTICIPANTS: Thirty eight thousand five hundred forty-six women and men aged 60 and olcer. MEASUREMENTS: HZ burden of interference with ADLs and HRQL using ratings from the Zoster Brief Pain Inventory (ZBPI) and Medical Outcomes Study 12-item Short Form Survey (SF-12) mental component summary (MCS) and physical component summary (PCS) scores. Vaccine efficacy was calculated for the modified-intention-to-treat trial population and solely in participants who developed HZ. RESULTS: For the modified-intention-to-treat population, the overall zoster vaccine efficacy was 66% (95% confidence interval (CI)=55-74%) for ZBPI ADL burden of interference score and 55% (95% CI=48-61%) for both the SF-12 MCS and PCS scores. Of participants who developed HZ, zoster vaccine reduced the ZBPI ADL burden of interference score by 31% (95% CI=12-51%) and did not significantly reduce the effect on HRQL. CONCLUSIONS: Zoster vaccine reduced the burden of HZ-related interference with ADLs in the population of vaccinees and in vaccinees who developed HZ. Zoster vaccine reduced the effect of HZ on HRQL in the population of vaccinees but not in vaccinees who developed HZ.

Comparison of the levels of immunogenicity and safety of Zostavax in adults 50 to 59 years old and in adults 60 years old or older.

Zostavax has been shown to be efficacious in the prevention of herpes zoster and generally well tolerated in clinical trials among subjects 60 years old or older. This prespecified combined analysis from two studies compares the levels of immunogenicity and safety of Zostavax in subjects 50 to 59 years old versus those in subjects >or=60 years old. Varicella-zoster virus (VZV) antibody (Ab) titers were measured by glycoprotein enzyme-linked immunosorbent assay at baseline and 4 weeks postvaccination. Noninferiority was evaluated by estimated geometric mean severalfold rise (GMFR) ratio (50 to 59 years old/>or=60 years old) and two-sided 95% confidence interval (CI). Success was defined by a lower bound (LB) of the 95% CI of the GMFR ratio of >0.67. Acceptability of postvaccination VZV Ab was defined by an LB of the 95% CI of the GMFR of >1.4. Safety data were recorded for 28 days postvaccination by standardized vaccination report card. The estimated GMFRs from baseline to 4 weeks postvaccination were 2.6 (95% CI, 2.4, 2.9) in subjects 50 to 59 years old and 2.3 (95% CI, 2.1, 2.4) in subjects >or=60 years old. The estimated GMFR ratio (50 to 59 years old/>or=60 years old) was 1.13 (95% CI, 1.02, 1.25). No serious Zostavax-related adverse experiences were reported. After a dose of Zostavax, the GMFR of the VZV Ab response in subjects 50 to 59 years old was noninferior to that in subjects >or=60 years old. The VZV Ab response was acceptable in both age groups. Zostavax was generally well tolerated in both age groups.

Safety and immunogenicity profile of the concomitant administration of ZOSTAVAX and inactivated influenza vaccine in adults aged 50 and older.

OBJECTIVES: To evaluate the safety and immunogenicity of ZOSTAVAX administered concomitantly with inactivated influenza vaccine or sequentially in adults aged 50 and older. DESIGN: Randomized, blinded, placebo-controlled study. SETTING: Thirteen U.S. and seven European study sites. PARTICIPANTS: Three hundred eighty-two concomitantly, 380 sequentially vaccinated subjects. INTERVENTION: The concomitant vaccination group received influenza vaccine and ZOSTAVAX at separate injection sites on Day 1 and placebo at Week 4. The nonconcomitant vaccination group received influenza vaccine and placebo at separate injection sites on Day 1 and ZOSTAVAX at Week 4. MEASUREMENTS: Primary safety endpoints: vaccine-related serious adverse experiences (AEs) within 28 days postvaccination (PV); and diary card-prompted local and systemic AEs. Primary immunogenicity endpoints: geometric mean titer (GMT) and geometric mean fold rise (GMFR) from baseline of varicella-zoster virus (VZV) antibody (Ab) at 4 weeks PV according to glycoprotein enzyme-linked immunosorbent assay (gpELISA) and GMT of influenza Ab for the three vaccine strains (2005-2006 influenza season) at 4 weeks PV according to hemagglutination inhibition assay. Secondary immunogenicity endpoint: influenza seroconversion rates (SCRs). RESULTS: No serious AEs related to ZOSTAVAX were observed during the study. VZV Ab GMTs 4 weeks PV for the concomitant and sequential groups were 554 and 597 gpELISA U/mL, respectively. The estimated VZV Ab GMT ratio was 0.9 (95% confidence interval (CI)=0.8-1.0), indicating noninferior (P<.001 for the null hypothesis of GMT ratio <0.67) responses. Estimated VZV Ab GMFR from baseline in the concomitant group was 2.1 (95% CI=2.0-2.3), indicating acceptable fold rise. Estimated GMT ratios (concomitant/sequential) for influenza strains A(H1N1), A(H3N2), and B were 0.9 (95% CI=0.8-1.1), 1.1 (95% CI=0.9-1.3), and 0.9 (95% CI=0.8-1.1), respectively, and SCRs were comparable across both groups, with more than 85% achieving titers of 1:40 or greater, meeting regulatory criteria. CONCLUSION: ZOSTAVAX and influenza vaccine given concomitantly are generally well tolerated in adults aged 50 and older. Ab responses were similar whether ZOSTAVAX and influenza vaccine were given concomitantly or sequentially.

Correlation coefficient inference on censored bioassay data.

In vaccine clinical trials, immunologic responses sometimes can not be accurately measured by bioassays. For example, a serial dilution assay usually reports the range of the response instead of the exact value. In some other assays, the measurement is not available if the response is lower than the assay's detection limit. In both cases, the measurements are censored. We are interested in computing the confidence interval for the correlation coefficient of two assay measurements that are subject to censoring. We propose using the maximum likelihood method to estimate the correlation coefficient, and constructing its confidence interval based on the second-order Taylor's expansion of the Fisher Z transformation. The method can be viewed as an extension of the Fisher Z transformation to the case of censored data. Extensive simulations show that the proposed method provides satisfactory coverage probabilities under finite sample sizes. The proposed method performs well compared with existing methods, but it is computationally much simpler. In addition, the proposed method works with many types of censored data in a similar way. Furthermore, we proposed an Monte Carlo exact test to assess the goodness-of-fit of the model.