Design and evaluation of multiregional trials with heterogeneous treatment effect across regions

Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data

BACKGROUND

Large observational clinical datasets are becoming increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considered as representations of the landscape of all possible disease conditions, in which a concrete disease state develops through stereotypical routes, characterized by "points of no return" and "final states" (such as lethal or recovery states). Extracting this information directly from the data remains challenging, especially in the case of synchronic (with a short-term follow-up) observations.

RESULTS

Here we suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values, through modeling the geometrical data structure as a bouquet of bifurcating clinical trajectories. The methodology is based on application of elastic principal graphs, which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection, and quantifying the geodesic distances (pseudo-time) in partially ordered sequences of observations. The methodology allows a patient to be positioned on a particular clinical trajectory (pathological scenario) and the degree of progression along it to be characterized with a qualitative estimate of the uncertainty of the prognosis. We developed a tool ClinTrajan for clinical trajectory analysis implemented in the Python programming language. We test the methodology in 2 large publicly available datasets: myocardial infarction complications and readmission of diabetic patients data.

CONCLUSIONS

Our pseudo-time quantification-based approach makes it possible to apply the methods developed for dynamical disease phenotyping and illness trajectory analysis (diachronic data analysis) to synchronic observational data.

Operational Experiences in China and Statistical Issues on the Conduct of Clinical Trials During the COVID-19 Pandemic

The COVID-19 outbreak is impacting clinical trials in many ways, such as patient recruitment, data collection and data analysis. To proceed in this difficult time, the adoption of new technologies and new approaches for conducting clinical trials needs to be accelerated. Simultaneously, regulatory agencies such as the US FDA and EMA have issued guidance to help the pharmaceutical industry conduct clinical trials of medical products during the COVID-19 pandemic. In this article, we will address some statistical issues and operational experiences in the conduction of clinical trials during the COVID-19 pandemic. Specifically, we will share experiences in the applications of remote clinical trials in China. Statistical issues related to protocol modifications caused by COVID-19 will be raised.

Hierarchical Bayes approach for subgroup analysis

In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.

Sample size determination for a specific region in multiregional clinical trials with multiple co-primary endpoints

Recently, multi-regional clinical trials (MRCTs), which incorporate subjects from many countries/regions around the world under the same protocol, have been widely conducted by many global pharmaceutical companies. The objective of such trials is to accelerate the development process for a drug and shorten the drug's approval time in key markets. Several statistical methods have been purposed for the design and evaluation of MRCTs, as well as for assessing the consistency of treatment effects across all regions with one primary endpoint. However, in some therapeutic areas (e.g., Alzheimer's disease), the clinical efficacy of a new treatment may be characterized by a set of possibly correlated endpoints, known as multiple co-primary endpoints. In this paper, we focus on a specific region and establish three statistical criteria for evaluating consistency between the specific region and overall results in MRCTs with multiple co-primary endpoints. More specifically, two of those criteria are used to assess whether the treatment effect in the region of interest is as large as that of the other regions or of the regions overall, while the other criterion is used to assess the consistency of the treatment effect of the specific region achieving a pre-specified threshold. The sample size required for the region of interest can also be evaluated based on these three criteria.

An Analysis of Japanese Patients Enrolled in Multiregional Clinical Trials in Oncology

The Japanese regulatory agency, the Ministry of Health, Labour and Welfare, requires sponsors to enroll a specific number or proportion of Japanese patients in multiregional clinical trials (MRCTs) in order to allow for the appropriate statistical evaluation of the efficacy and safety of an investigational drug in the Japanese population. This means the actual proportion of Japanese patients to the total sample size would need to be determined by taking into account the proportion of patients in other regions as well as the appropriate statistical considerations. Determining the proportion of Japanese patients that satisfies the regulatory agency's statistical requirement, along with taking into account the practical limitations of patient enrollment, would be difficult for sponsors. We believe that recent studies about the proportion of Japanese patients enrolled in MRCTs provides sponsors with useful information about partitioning sample size into individual regions for MRCTs in oncology. In this study, we investigated the proportion of Japanese patients in MRCTs and further compared the efficacy results from the overall population to that of the Japanese population. The proportion of Japanese patients averaged approximately 10.9%, but the proportion varied depending on the drug type. The results of the primary endpoints in Japanese patients were similar to those of the overall population, regardless of the proportion of Japanese patients.

Regional efficacy assessment in multiregional clinical development

It is common in multiregional clinical development that data from a global trial and a local trial (in a target country) together will be used to support local filing in the target country. This approach is considered efficient drug development both globally and in the target country. However, it remains a challenge how to combine global trial data and local trial data toward local filing. To address this challenge, we propose an "interpretation-centric" evaluation criterion based on a weighted estimator that weights data from the target country and outside of the target country. This approach provides an unbiased estimate of a global treatment effect with appropriate representation of the target country patient population, where the "appropriate representation" is the desired proportion of the target country participants in a global trial and is measured by the weight parameter. This natural interpretation can facilitate drug development discussion with local regulatory agencies. Sample size of the local trial can be determined using the proposed weighted estimator. Approaches for weight determination are also discussed.

Extrapolation of efficacy and other data to support the development of new medicines for children: A systematic review of methods

OBJECTIVE

When developing new medicines for children, the potential to extrapolate from adult data to reduce the experimental burden in children is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. We reviewed the literature to identify statistical methods that could be used to optimise extrapolations in paediatric drug development programmes.

METHODS

Web of Science was used to identify papers proposing methods relevant for using data from a 'source population' to support inferences for a 'target population'. Four key areas of methods development were targeted: paediatric clinical trials, trials extrapolating efficacy across ethnic groups or geographic regions, the use of historical data in contemporary clinical trials and using short-term endpoints to support inferences about long-term outcomes.

RESULTS

Searches identified 626 papers of which 52 met our inclusion criteria. From these we identified 102 methods comprising 58 Bayesian and 44 frequentist approaches. Most Bayesian methods (n = 54) sought to use existing data in the source population to create an informative prior distribution for a future clinical trial. Of these, 46 allowed the source data to be down-weighted to account for potential differences between populations. Bayesian and frequentist versions of methods were found for assessing whether key parameters of source and target populations are commensurate (n = 34). Fourteen frequentist methods synthesised data from different populations using a joint model or a weighted test statistic.

CONCLUSIONS

Several methods were identified as potentially applicable to paediatric drug development. Methods which can accommodate a heterogeneous target population and which allow data from a source population to be down-weighted are preferred. Methods assessing the commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects.