Use of statistical and pharmacokinetic-pharmacodynamic modeling and simulation to improve decision-making: A section summary report of the trends and innovations in clinical trial statistics conference.

The application of modeling and simulation (M&S) methods to improve decision-making was discussed during the Trends & Innovations in Clinical Trial Statistics Conference held in Durham, North Carolina, USA on May 1-4, 2016. Uses of both pharmacometric and statistical M&S were presented during the conference, highlighting the diversity of the methods employed by pharmacometricians and statisticians to address a broad range of quantitative issues in drug development. Five presentations are summarized herein, which cover the development strategy of employing M&S to drive decision-making; European initiatives on best practice in M&S; case studies of pharmacokinetic/pharmacodynamics modeling in regulatory decisions; estimation of exposure-response relationships in the presence of confounding; and the utility of estimating the probability of a correct decision for dose selection when prior information is limited. While M&S has been widely used during the last few decades, it is expected to play an essential role as more quantitative assessments are employed in the decision-making process. By integrating M&S as a tool to compile the totality of evidence collected throughout the drug development program, more informed decisions will be made.

Proposed best practice for projects that involve modelling and simulation.

Modelling and simulation has been used in many ways when developing new treatments. To be useful and credible, it is generally agreed that modelling and simulation should be undertaken according to some kind of best practice. A number of authors have suggested elements required for best practice in modelling and simulation. Elements that have been suggested include the pre-specification of goals, assumptions, methods, and outputs. However, a project that involves modelling and simulation could be simple or complex and could be of relatively low or high importance to the project. It has been argued that the level of detail and the strictness of pre-specification should be allowed to vary, depending on the complexity and importance of the project. This best practice document does not prescribe how to develop a statistical model. Rather, it describes the elements required for the specification of a project and requires that the practitioner justify in the specification the omission of any of the elements and, in addition, justify the level of detail provided about each element. This document is an initiative of the Special Interest Group for modelling and simulation. The Special Interest Group for modelling and simulation is a body open to members of Statisticians in the Pharmaceutical Industry and the European Federation of Statisticians in the Pharmaceutical Industry. Examples of a very detailed specification and a less detailed specification are included as appendices.

Estimands: discussion points from the PSI estimands and sensitivity expert group.

ICH E9 Statistical Principles for Clinical Trials was issued in 1998. In October 2014, an addendum to ICH E9 was proposed relating to estimands and sensitivity analyses. In preparation for the release of the addendum, Statisticians in the Pharmaceutical Industry held a 1-day expert group meeting in February 2015. Topics debated included definition, development, implementation, education and communication challenges associated with estimands and sensitivity analyses. The topic of estimands is an important and relatively new one in clinical development. A clear message from the meeting was that estimands bridge the gap between study objectives and statistical methods. When defining estimands, an iterative process linking trial objectives, estimands, trial design, statistical and sensitivity analysis needs to be established. Each objective should have at least one distinct estimand, supported by sensitivity analyses. Because clinical trials are multi-faceted and expensive, it is unrealistic to restrict a study to a single objective and associated estimand. The actual set of estimands and sensitivity analyses for a study will depend on the study objectives, the disease setting and the needs of the various stakeholders. Copyright © 2016 John Wiley & Sons, Ltd.

Sensitivity to censored-at-random assumption in the analysis of time-to-event endpoints.

Over the past years, significant progress has been made in developing statistically rigorous methods to implement clinically interpretable sensitivity analyses for assumptions about the missingness mechanism in clinical trials for continuous and (to a lesser extent) for binary or categorical endpoints. Studies with time-to-event outcomes have received much less attention. However, such studies can be similarly challenged with respect to the robustness and integrity of primary analysis conclusions when a substantial number of subjects withdraw from treatment prematurely prior to experiencing an event of interest. We discuss how the methods that are widely used for primary analyses of time-to-event outcomes could be extended in a clinically meaningful and interpretable way to stress-test the assumption of ignorable censoring. We focus on a 'tipping point' approach, the objective of which is to postulate sensitivity parameters with a clear clinical interpretation and to identify a setting of these parameters unfavorable enough towards the experimental treatment to nullify a conclusion that was favorable to that treatment. Robustness of primary analysis results can then be assessed based on clinical plausibility of the scenario represented by the tipping point. We study several approaches for conducting such analyses based on multiple imputation using parametric, semi-parametric, and non-parametric imputation models and evaluate their operating characteristics via simulation. We argue that these methods are valuable tools for sensitivity analyses of time-to-event data and conclude that the method based on piecewise exponential imputation model of survival has some advantages over other methods studied here. Copyright © 2016 John Wiley & Sons, Ltd.

Population genomics of domestic and wild yeasts.

Since the completion of the genome sequence of Saccharomyces cerevisiae in 1996 (refs 1, 2), there has been a large increase in complete genome sequences, accompanied by great advances in our understanding of genome evolution. Although little is known about the natural and life histories of yeasts in the wild, there are an increasing number of studies looking at ecological and geographic distributions, population structure and sexual versus asexual reproduction. Less well understood at the whole genome level are the evolutionary processes acting within populations and species that lead to adaptation to different environments, phenotypic differences and reproductive isolation. Here we present one- to fourfold or more coverage of the genome sequences of over seventy isolates of the baker's yeast S. cerevisiae and its closest relative, Saccharomyces paradoxus. We examine variation in gene content, single nucleotide polymorphisms, nucleotide insertions and deletions, copy numbers and transposable elements. We find that phenotypic variation broadly correlates with global genome-wide phylogenetic relationships. S. paradoxus populations are well delineated along geographic boundaries, whereas the variation among worldwide S. cerevisiae isolates shows less differentiation and is comparable to a single S. paradoxus population. Rather than one or two domestication events leading to the extant baker's yeasts, the population structure of S. cerevisiae consists of a few well-defined, geographically isolated lineages and many different mosaics of these lineages, supporting the idea that human influence provided the opportunity for cross-breeding and production of new combinations of pre-existing variations.

Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models.

The need to use rigorous, transparent, clearly interpretable, and scientifically justified methodology for preventing and dealing with missing data in clinical trials has been a focus of much attention from regulators, practitioners, and academicians over the past years. New guidelines and recommendations emphasize the importance of minimizing the amount of missing data and carefully selecting primary analysis methods on the basis of assumptions regarding the missingness mechanism suitable for the study at hand, as well as the need to stress-test the results of the primary analysis under different sets of assumptions through a range of sensitivity analyses. Some methods that could be effectively used for dealing with missing data have not yet gained widespread usage, partly because of their underlying complexity and partly because of lack of relatively easy approaches to their implementation. In this paper, we explore several strategies for missing data on the basis of pattern mixture models that embody clear and realistic clinical assumptions. Pattern mixture models provide a statistically reasonable yet transparent framework for translating clinical assumptions into statistical analyses. Implementation details for some specific strategies are provided in an Appendix (available online as Supporting Information), whereas the general principles of the approach discussed in this paper can be used to implement various other analyses with different sets of assumptions regarding missing data.

Defining Clinical Trial Estimands: A Practical Guide for Study Teams with Examples Based on a Psychiatric Disorder.

While the ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" was released in late 2019, the widespread implementation of defining and reporting estimands across clinical trials is still in progress and the engagement of non-statistical functions in this process is also in progress. Case studies are sought after, especially those with documented clinical and regulatory feedback. This paper describes an interdisciplinary process for implementing the estimand framework, devised by the Estimands and Missing Data Working Group (a group with clinical, statistical, and regulatory representation) of the International Society for CNS Clinical Trials and Methodology. This process is illustrated by specific examples using various types of hypothetical trials evaluating a treatment for major depressive disorder. Each of the estimand examples follows the same template and features all steps of the proposed process, including identifying the trial stakeholder(s), the decisions they need to make about the investigated treatment in their specific role and the questions that would support their decision making. Each of the five strategies for handling intercurrent events are addressed in at least one example; the featured endpoints are also diverse, including continuous, binary and time to event. Several examples are presented that include specifications for a potential trial design, key trial implementation elements needed to address the estimand, and main and sensitivity estimator specifications. Ultimately this paper highlights the need to incorporate multi-disciplinary collaborations into implementing the ICH E9(R1) framework.

High-resolution mapping of copy-number alterations with massively parallel sequencing.

Cancer results from somatic alterations in key genes, including point mutations, copy-number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to segment equal copy numbers from massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of approximately 14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over twofold better precision for localizing breakpoints (typically, to within approximately 1 kilobase).

Proposed best practice for statisticians in the reporting and publication of pharmaceutical industry-sponsored clinical trials.

In this paper we set out what we consider to be a set of best practices for statisticians in the reporting of pharmaceutical industry-sponsored clinical trials. We make eight recommendations covering: author responsibilities and recognition; publication timing; conflicts of interest; freedom to act; full author access to data; trial registration and independent review. These recommendations are made in the context of the prominent role played by statisticians in the design, conduct, analysis and reporting of pharmaceutical sponsored trials and the perception of the reporting of these trials in the wider community.

Making available information from studies sponsored by the pharmaceutical industry: some current practices.

Since the web-based registry ClinicalTrials.gov was launched on 29 February 2000, the pharmaceutical industry has made available an increasing amount of information about the clinical trials that it sponsors. The process has been spurred on by a number of factors including a wish by the industry to provide greater transparency regarding clinical trial data; and has been both aided and complicated by the number of institutions that have a legitimate interest in guiding and defining what should be made available. This article reviews the history of this process of making information about clinical trials publicly available. It provides a reader's guide to the study registries and the databases of results; and looks at some indicators of consistency in the posting of study information.

The potential for bias in reporting of industry-sponsored clinical trials.

Concerns about potentially misleading reporting of pharmaceutical industry research have surfaced many times. The potential for duality (and thereby conflict) of interest is only too clear when you consider the sums of money required for the discovery, development and commercialization of new medicines. As the ability of major, mid-size and small pharmaceutical companies to innovate has waned, as evidenced by the seemingly relentless decline in the numbers of new medicines approved by Food and Drug Administration and European Medicines Agency year-on-year, not only has the cost per new approved medicine risen: so too has the public and media concern about the extent to which the pharmaceutical industry is open and honest about the efficacy, safety and quality of the drugs we manufacture and sell. In 2005 an Editorial in Journal of the American Medical Association made clear that, so great was their concern about misleading reporting of industry-sponsored studies, henceforth no article would be published that was not also guaranteed by independent statistical analysis. We examine the precursors to this Editorial, as well as its immediate and lasting effects for statisticians, for the manner in which statistical analysis is carried out, and for the industry more generally.

Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints.

Many clinical trials of treatments for patients hospitalised for COVID-19 use an ordinal scale recommended by the World Heath Organisation. The scale represents intensity of medical intervention, with higher scores for interventions more burdensome for the patient, and highest score for death. There is uncertainty about use of this ordinal scale in testing hypotheses. With the objective of assessing the power and Type I error of potential endpoints and analyses based on the ordinal scale, trajectories of the score over 28 days were simulated for scenarios based closely on results of two trials recently published. The simulation used transition probabilities for the ordinal scale over time. No one endpoint was optimal across scenarios, but a ranked measure of trajectory fared moderately well in all scenarios. Type I error was controlled at close to the nominal level for all endpoints. Because not tied to a particular population with regard to baseline severity, the use of transition probabilities allows plausible assessment of endpoints in populations with configurations of baseline score for which data is not yet published, provided some data on the relevant transition probabilities are available. The results could support experts in the choice of endpoint based on the ordinal scale.

Choosing Estimands in Clinical Trials: Putting the ICH E9(R1) Into Practice.

The National Research Council (NRC) Expert Panel Report on Prevention and Treatment of Missing Data in Clinical Trials highlighted the need for clearly defining objectives and estimands. That report sparked considerable discussion and literature on estimands and how to choose them. Importantly, consideration moved beyond missing data to include all postrandomization events that have implications for estimating quantities of interest (intercurrent events, aka ICEs). The ICH E9(R1) draft addendum builds on that research to outline key principles in choosing estimands for clinical trials, primarily with focus on confirmatory trials. This paper provides additional insights, perspectives, details, and examples to help put ICH E9(R1) into practice. Specific areas of focus include how the perspectives of different stakeholders influence the choice of estimands; the role of randomization and the intention-to-treat principle; defining the causal effects of a clearly defined treatment regimen, along with the implications this has for trial design and the generalizability of conclusions; detailed discussion of strategies for handling ICEs along with their implications and assumptions; estimands for safety objectives, time-to-event endpoints, early-phase and one-arm trials, and quality of life endpoints; and realistic examples of the thought process involved in defining estimands in specific clinical contexts.

Defining Efficacy Estimands in Clinical Trials: Examples Illustrating ICH E9(R1) Guidelines.

This paper provides examples of defining estimands in real-world scenarios following ICH E9(R1) guidelines. Detailed discussions on choosing the estimands and estimators can be found in our companion papers. Three scenarios of increasing complexity are illustrated. The first example is a proof-of-concept trial in major depressive disorder where the estimand is chosen to support the sponsor decision on whether to continue development. The second and third examples are confirmatory trials in severe asthma and rheumatoid arthritis respectively. We discuss the intercurrent events expected during each trial and how they can be handled so as to be consistent with the study objectives. The estimands discussed in these examples are not the only acceptable choices for their respective scenarios. The intent is to illustrate the key concepts rather than focus on specific choices. Emphasis is placed on following a study development process where estimands link the study objectives with data collection and analysis in a coherent manner, thereby avoiding disconnect between objectives, estimands, and analyses.

ACCORD: A Multicentre, Seamless, Phase 2 Adaptive Randomisation Platform Study to Assess the Efficacy and Safety of Multiple Candidate Agents for the Treatment of COVID-19 in Hospitalised Patients: A structured summary of a study protocol for a randomised controlled trial.

OBJECTIVES: Stage 1: To evaluate the safety and efficacy of candidate agents as add-on therapies to standard of care (SoC) in patients hospitalised with COVID-19 in a screening stage. Stage 2: To confirm the efficacy of candidate agents selected on the basis of evidence from Stage 1 in patients hospitalised with COVID-19 in an expansion stage. TRIAL DESIGN: ACCORD is a seamless, Phase 2, adaptive, randomised controlled platform study, designed to rapidly test candidate agents in the treatment of COVID-19. Designed as a master protocol with each candidate agent being included via its own sub-protocol, initially randomising equally between each candidate and a single contemporaneous SoC arm (which can adapt into 2:1). Candidate agents currently include bemcentinib, MEDI3506, acalabrutinib, zilucoplan and nebulised heparin. For each candidate a total of 60 patients will be recruited in Stage 1. If Stage 1 provides evidence of efficacy and acceptable safety the candidate will enter Stage 2 where a total of approximately 126 patients will be recruited into each study arm sub-protocol. Enrollees and outcomes will not be shared across the Stages; the endpoint, analysis and sample size for Stage 2 may be adjusted based on evidence from Stage 1. Additional arms may be added as new potential candidate agents are identified via candidate agent specific sub-protocols. PARTICIPANTS: The study will include hospitalised adult patients (≥18 years) with confirmed SARS-CoV-2 infection, the virus that causes COVID-19, that clinically meet Grades 3 (hospitalised - mild disease, no oxygen therapy), Grades 4 (hospitalised, oxygen by mask or nasal prongs) and 5 (hospitalised, non-invasive ventilation or high flow oxygen) of the WHO Working Group on the Clinical Characteristics of COVID-19 9-point category ordinal scale. Participants will be recruited from England, Northern Ireland, Wales and Scotland. INTERVENTION AND COMPARATOR: Comparator is current standard of care (SoC) for the treatment of COVID-19. Current candidate experimental arms include bemcentinib, MEDI3506, acalabrutinib, zilucoplan and nebulised heparin with others to be added over time. Bemcentinib could potentially reduce viral infection and blocks SARS-CoV-2 spike protein; MEDI3506 is a clinic-ready anti-IL-33 monoclonal antibody with the potential to treat respiratory failure caused by COVID; acalabrutinib is a BTK inhibitor which is anti-viral and anti-inflammatory; zilucoplan is a complement C5 inhibitor which may block the severe inflammatory response in COVID-19 and; nebulised heparin has been shown to bind with the spike protein. ACCORD is linked with the UK national COVID therapeutics task force to help prioritise candidate agents. MAIN OUTCOMES: Time to sustained clinical improvement of at least 2 points (from randomisation) on the WHO 9-point category ordinal scale, live discharge from the hospital, or considered fit for discharge (a score of 0, 1, or 2 on the ordinal scale), whichever comes first, by Day 29 (this will also define the "responder" for the response rate analyses). RANDOMISATION: An electronic randomization will be performed by Cenduit using Interactive Response Technology (IRT). Randomisation will be stratified by baseline severity grade. Randomisation will proceed with an equal allocation to each arm and a contemporaneous SoC arm (e.g. 1:1 if control and 1 experimental arm; 1:1:1 if two experimental candidate arms etc) but will be reviewed as the trial progresses and may be changed to 2:1 in favour of the candidate agents. BLINDING (MASKING): The trial is open label and no blinding is currently planned in the study. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): This will be in the order of 60 patients per candidate agent for Stage 1, and 126 patients for Stage 2. However, sample size re-estimation may be considered after Stage 1. It is estimated that up to 1800 patients will participate in the overall study. TRIAL STATUS: Master protocol version ACCORD-2-001 - Master Protocol (Amendment 1) 22nd April 2020, the trial has full regulatory approval and recruitment is ongoing in the bemcentinib (first patient recruited 6/5/2020), MEDI3506 (first patient recruited 19/5/2020), acalabrutinib (first patient recruited 20/5/2020) and zilucoplan (first patient recruited 19/5/2020) candidates (and SoC). The recruitment dates of each arm will vary between candidate agents as they are added or dropped from the trial, but will have recruited and reported within a year. TRIAL REGISTRATION: EudraCT 2020-001736-95 , registered 28th April 2020. FULL PROTOCOL: The full protocol (Master Protocol with each of the candidate sub-protocols) is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

Life Cycle Modeling and Simulation: Current Practice and Future Impact on Health Care Innovation and Delivery.

Modeling and simulation is poised to transform drug development across the entire life cycle from discovery to commercialization. For the biopharmaceutical industry, this transformation will enable knowledge-based decision making and foster new collaborative ways of working that will translate into more high-value treatments and increased development efficiencies. In the health care arena, where value for money is paramount, modeling and simulation will inform future health care planning and practice.

Rheumatoid Arthritis Disease Progression Modeling.

Time progression models provide a significant advantage in developing clinical trials and can also be used to elicit comparisons among therapeutic agents. The authors performed a meta-analysis to construct a time progression model for rheumatoid arthritis (RA), an area of significant interest for pharmaceutical development, using the ACR20 end point. Compounds studied were chiefly monoclonal antibodies that were used in conjunction with methotrexate. The study shows that an exponential time response model adequately fits the data. From the modeling, a distribution of effects for biological RA therapies can be provided.

Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.

The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.

Repetitive sequence variation and dynamics in the ribosomal DNA array of Saccharomyces cerevisiae as revealed by whole-genome resequencing.

Ribosomal DNA (rDNA) plays a key role in ribosome biogenesis, encoding genes for the structural RNA components of this important cellular organelle. These genes are vital for efficient functioning of the cellular protein synthesis machinery and as such are highly conserved and normally present in high copy numbers. In the baker's yeast Saccharomyces cerevisiae, there are more than 100 rDNA repeats located at a single locus on chromosome XII. Stability and sequence homogeneity of the rDNA array is essential for function, and this is achieved primarily by the mechanism of gene conversion. Detecting variation within these arrays is extremely problematic due to their large size and repetitive structure. In an attempt to address this, we have analyzed over 35 Mbp of rDNA sequence obtained from whole-genome shotgun sequencing (WGSS) of 34 strains of S. cerevisiae. Contrary to expectation, we find significant rDNA sequence variation exists within individual genomes. Many of the detected polymorphisms are not fully resolved. For this type of sequence variation, we introduce the term partial single nucleotide polymorphism, or pSNP. Comparative analysis of the complete data set reveals that different S. cerevisiae genomes possess different patterns of rDNA polymorphism, with much of the variation located within the rapidly evolving nontranscribed intergenic spacer (IGS) region. Furthermore, we find that strains known to have either structured or mosaic/hybrid genomes can be distinguished from one another based on rDNA pSNP number, indicating that pSNP dynamics may provide a reliable new measure of genome origin and stability.

SOX2 is an amplified lineage-survival oncogene in lung and esophageal squamous cell carcinomas.

Lineage-survival oncogenes are activated by somatic DNA alterations in cancers arising from the cell lineages in which these genes play a role in normal development. Here we show that a peak of genomic amplification on chromosome 3q26.33 found in squamous cell carcinomas (SCCs) of the lung and esophagus contains the transcription factor gene SOX2, which is mutated in hereditary human esophageal malformations, is necessary for normal esophageal squamous development, promotes differentiation and proliferation of basal tracheal cells and cooperates in induction of pluripotent stem cells. SOX2 expression is required for proliferation and anchorage-independent growth of lung and esophageal cell lines, as shown by RNA interference experiments. Furthermore, ectopic expression of SOX2 here cooperated with FOXE1 or FGFR2 to transform immortalized tracheobronchial epithelial cells. SOX2-driven tumors show expression of markers of both squamous differentiation and pluripotency. These characteristics identify SOX2 as a lineage-survival oncogene in lung and esophageal SCC.