Are randomized trials obsolete or more important than ever in the genomic era?

Large-scale, pragmatic randomized trials in the era of big data, precision medicine and machine learning. Valid and necessary, or outdated and a waste of resources?

During the past decade, more and more large-scale pragmatic clinical trials have been carried out in orthopedic trauma surgery. This trend is fueled by the common belief that the larger the numbers in a trial, the broader the eligibility criteria, and the less strict the regimentation of local treatment standards by protocol, the more trustworthy the findings would be. However, it must also be taken into account that the precision of an outcome measurement does not depend on the sample size alone, but the homogeneity of the studied population. Consequently, a small trial with stringent entry and assessment criteria may offer similarly precise answers as a large trial with less strict entry and assessment criteria because of the basic mathematical correlation between standard deviation and standard error of the mean. There is now a lively and controversial debate about the role of randomized controlled trials (RCT) in an era of stratified medicine driven by the ever increasing understanding and clinical measurability of molecular pathways, making a certain intervention more effective in patients who show a distinct genetic variant. Cluster and pattern recognition by artificial intelligence (AI) and its methodological variety applied to huge datasets and population-based cohorts further propel the spiral of knowledge. Advanced adaptive RCT concepts like enrichment designs, basket and bucket trials, master protocols etc. were developed to combine classic principles of the scientific method with big data, the latter of which have not arrived yet in trauma care. In spite of all biomedical and methodological achievements made, surprisingly such key questions remain unanswered as a) is a certain treatment causally responsible for making a difference in patient-centered outcomes compared to placebo, a control treatment, or the standard of care, b) do the results of a controlled experiment are relevant enough to change clinical practice, and c) under which conditions and assumptions shall we conduct large-scale pragmatic RCTs, focused confirmatory RCTs, or personalized analyses with or without AI support.

Valuation of Health and Nonhealth Outcomes from Next-Generation Sequencing: Approaches, Challenges, and Solutions

BACKGROUND

Next-generation sequencing (NGS) technologies have seen variable adoption in the clinic. This is partly due to a lack of clinical and economic studies, with the latter increasingly challenged to examine patient preferences for health and nonhealth outcomes (e.g., false-positive rate).

OBJECTIVES

To conduct a structured review of studies valuing patients' preference-based utility for NGS outcomes, to highlight identified methodological challenges, and to consider how studies addressed identified challenges.

METHODS

We searched MEDLINE (PubMed), Embase (Ovid), and Web of Science for published studies examining outcomes from health care decisions informed by NGS. We focused our search on direct elicitations of preference-based utility. We reviewed included studies and qualitatively grouped and summarized stated challenges and solutions by theme.

RESULTS

Eleven studies were included. Most of them (n = 6) used discrete choice experiments to value utility. We categorized challenges into four themes: 1) valuing the full range of NGS outcomes, 2) accounting for accuracy and uncertainty surrounding effectiveness, 3) allowing for simultaneous multiple and cascading risks, and 4) incorporating downstream consequences. Studies found strong evidence of utility for NGS information, regardless of health improvement. Investigators addressed challenges by simplifying complex choices, by including health outcomes alongside nonhealth outcomes, and by using multiple elicitation techniques.

CONCLUSIONS

The breadth and complexity of NGS-derived information makes the technology a unique and challenging application for utility valuation. Failing to account for the utility or disutility of NGS-related nonhealth outcomes may lead to overinvestment or underinvestment in NGS, and so there is a need for research addressing unresolved challenges.

Personalized medicine: Genetic risk prediction of drug response

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

Patients' Preferences for Genomic Diagnostic Testing in Chronic Lymphocytic Leukaemia: A Discrete Choice Experiment

BACKGROUND

Genomic information could help to reduce the morbidity effects of inappropriate treatment decisions in many disease areas, in particular cancer. However, evidence of the benefits that patients derive from genomic testing is limited. This study evaluated patient preferences for genomic testing in the context of chronic lymphocytic leukaemia (CLL).

METHODS

We used a discrete choice experiment (DCE) survey to assess the preferences of CLL patients in the UK for genomic testing. The survey presented patients with 16 questions in which they had to choose between two possible test scenarios. Tests in these scenarios were specified in terms of six attributes, including test effectiveness, test reliability and time to receive results.

RESULTS

219 patients completed the survey (response rate 20 %). Both clinical and process-related attributes were valued by respondents. Patients were willing to pay £24 for a 1 % increase in chemotherapy non-responders identified, and £27 to reduce time to receive test results by 1 day. Patients were also willing to wait an extra 29 days for test results if an additional one-third of chemotherapy non-responders could be identified, and would tolerate a genomic test being wrong 8 % of the time to receive this information.

CONCLUSION

CLL patients value the information that could be provided by genomic tests, and prefer combinations of test characteristics that more closely reflect future genomic testing practice than current genetic testing practice. Commissioners will need to carefully consider how genomic testing is operationalised in this context if the benefits of testing are to be realised.

The Application of Genomics in Diabetes: Barriers to Discovery and Implementation

The emerging availability of genomic and electronic health data in large populations is a powerful tool for research that has drawn interest in bringing precision medicine to diabetes. In this article, we discuss the potential application of genomics to the prediction, prevention, and treatment of diabetes, and we use examples from other areas of medicine to illustrate some of the challenges involved in conducting genomics research in human populations and implementing findings in practice. At this time, a major barrier to the application of genomics in diabetes care is the lack of actionable genomic findings. Whether genomic information should be used in clinical practice requires a framework for evaluating the validity and clinical utility of this approach, an improved integration of genomic data into electronic health records, and the clinical decision support and educational resources for clinicians to use these data. Efforts to identify optimal approaches in all of these domains are in progress and may help to bring diabetes into the era of genomic medicine.

[Evidence-based trauma and orthopedic surgery : 20 years after Sackett]

The article "Evidence based medicine: what it is and what it isn't" published in the BMJ in 1996, is regarded as the foundation of the evidence-based medicine (EbM) movement. Approximately 5 years later David L. Sackett, one of the leaders of the movement, requested all experts to voluntarily abandon their position to make way for young researchers and fresh ideas. Since the term was first coined and the establishment of organizations and platforms fostering the idea, EbM has polarized clinicians and scientists around the world. Clinical and methodological developments during recent years have, however, overtaken the original principles of EbM. This review highlights the core concepts of EbM which have remained unchanged and valid for the current practice of trauma and orthopedic surgery and where revision is needed.

The path to personalized vascular therapy - We are closer than we think

The terms "personalized" or "precision" medicine are being used commonly in some branches of medicine but have yet to be widely adopted in vascular surgery. Despite this, personalized vascular therapy occurs on a daily basis in every vascular specialist's office as we strive to make informed recommendations at the individual patient level. The following is a description of several of the areas where advances in personalized vascular care have been achieved, including custom devices, personalized predictions, pharmacogenetics and surgicogenetics.

Big data are coming to psychiatry: a general introduction

Big data are coming to the study of bipolar disorder and all of psychiatry. Data are coming from providers and payers (including EMR, imaging, insurance claims and pharmacy data), from omics (genomic, proteomic, and metabolomic data), and from patients and non-providers (data from smart phone and Internet activities, sensors and monitoring tools). Analysis of the big data will provide unprecedented opportunities for exploration, descriptive observation, hypothesis generation, and prediction, and the results of big data studies will be incorporated into clinical practice. Technical challenges remain in the quality, analysis and management of big data. This paper discusses some of the fundamental opportunities and challenges of big data for psychiatry.

Precision cancer medicine: the future is now, only better

The promise of precision medicine for cancer is already being realized with the recent introduction of many targeted therapies, some with companion diagnostic tests that identify patients most likely to benefit from treatment. The utility of molecular profiling of cancer to identify actionable aberrations has been suggested by several small clinical trials conducted in patients with advanced cancer and by many anecdotes but is yet to be proven in well-designed, prospective, randomized trials. Several trials that will definitively test this strategy are now underway or soon to be launched. Melanoma, a disease once largely untreatable when metastatic, may be a paradigm for understanding how the molecular drivers of a disease can lead to highly effective targeted therapies, as well as for realizing the enormous therapeutic potential of unleashing the immune system against cancer to produce long-term disease control. Looking to the future, advanced omics technologies and computational techniques will enable assessment of not only genomic variants, as performed today, but also of pathway and network aberrations that will greatly facilitate selection of drug combinations likely to benefit specific patients. As our deepening understanding of tumor biology converges with rapid advances in measurement science and technology and computational analysis, we have an enormous opportunity to create a future for precision medicine in oncology that provides for highly specific, minimally toxic, and dramatically effective treatment for each patient.

Clinical evidence inputs to comparative effectiveness research could impact the development of novel treatments

Aim: This study aims to analyze the impacts of a range of clinical evidence generation scenarios associated with comparative effectiveness research (CER) on pharmaceutical innovation. Materials & methods: We used the Global Pharmaceutical Policy Model to project the effect of changes in pharmaceutical producer costs, revenues and timings on drug innovation and health for the age 55+ populations in the USA and Europe through year 2060 using three clinical scenarios. Results: Changes in producer incentives from widespread CER evidence generation and use had varied but often large predicted impacts on simulated outcomes in 2060. Effect on the number of new drug introductions ranged from a 81.1% reduction to a 45.5% increase, and the effect on population-level life expectancy ranged from a 15.6% reduction to a 11.4% increase compared to baseline estimates. Conclusion: The uncertainty surrounding the consequences of increased clinical evidence generation and use on innovation calls for a carefully measured approach to CER implementation, balancing near-term benefits to spending and health with long-term implications for innovation.

Guidance for pharmacogenomic biomarker testing in labels of FDA-approved drugs

PURPOSE

The aim of this study was to compare guidance for genetic testing in US Food and Drug Administration (FDA)-approved drug labels in oncology to those of drugs for other therapeutic areas.

METHODS

We reviewed labels of all the FDA-approved drugs with labels containing pharmacogenomic information. We assessed whether genetic testing was required or recommended before prescription and, if not, the reason for pharmacogenomic labeling.

RESULTS

We included 140 drugs corresponding to 158 drug-biomarker pairs. Overall, 46 (29%) of 158 pairs stated a requirement or recommendation for genetic biomarker testing in the label. This proportion was higher in oncology than in other areas (62 vs. 12%; P < 0.001). For the 112 drug-biomarker pairs (including 20 in oncology) without recommendation or requirement for genetic testing, the main reasons for pharmacogenomic labeling were change in pharmacologic end points (32%) and higher risk of toxicity (30%). For 11 (10%) pairs (including 1 in oncology), a genetic biomarker was mentioned only to inform that it was not relevant. In oncology, the main reasons for pharmacogenomic labeling were higher risk of toxicity (55%) and definition of the mechanism of action (25%).

CONCLUSION

Inclusion of biomarkers in drug labels does not always correspond to required or recommended genetic testing, especially outside oncology.Genet Med 17 9, 733-738.

Precision medicine for prostate cancer

Metastatic castration-resistant prostate cancer remains a lethal disease despite considerable progress in systemic therapy over the past decade. The recent advances in genomic sequencing have improved the molecular classification of prostate cancer. The translation of genomic data into clinically relevant prognostic and predictive biomarkers to guide therapy is still in its infancy and therapies for castration-resistant prostate cancer are still used empirically. We discuss these genomic aberrations in more detail, focusing on androgen receptor signaling, ETS transcription factor gene rearrangements and PTEN loss. The incorporation of this genomic data within early phase clinical trials is evolving and may prove significant in advancing personalized care in prostate cancer.

Evidence synthesis and guideline development in genomic medicine: current status and future prospects

PURPOSE

With the accelerated implementation of genomic medicine, health-care providers will depend heavily on professional guidelines and recommendations. Because genomics affects many diseases across the life span, no single professional group covers the entirety of this rapidly developing field.

METHODS

To pursue a discussion of the minimal elements needed to develop evidence-based guidelines in genomics, the Centers for Disease Control and Prevention and the National Cancer Institute jointly held a workshop to engage representatives from 35 organizations with interest in genomics (13 of which make recommendations). The workshop explored methods used in evidence synthesis and guideline development and initiated a dialogue to compare these methods and to assess whether they are consistent with the Institute of Medicine report "Clinical Practice Guidelines We Can Trust."

RESULTS

The participating organizations that develop guidelines or recommendations all had policies to manage guideline development and group membership, and processes to address conflicts of interests. However, there was wide variation in the reliance on external reviews, regular updating of recommendations, and use of systematic reviews to assess the strength of scientific evidence.

CONCLUSION

Ongoing efforts are required to establish criteria for guideline development in genomic medicine as proposed by the Institute of Medicine.

The ethics of clinical trials

Over the past decades, randomised controlled trials (RCTs) have prevailed over clinical judgement, case reports, and observational studies and became the gold evidential standard in medicine. Furthermore, during the same time frame, RCTs became a crucial part of the regulatory process whereby a new therapeutic can gain access to the drug market. Today, clinical trials are large and tightly regulated enterprises that have to comply with ethical requirements while maintaining high epistemic standards, a balance that becomes increasingly difficult as the research questions become more sophisticated. In this review, the author will discuss some of the most important ethical issues surrounding RCTs, with an eye to the most recent debates and the context of oncological research in particular.

Issues surrounding the health economic evaluation of genomic technologies

AIM

Genomic interventions could enable improved disease stratification and individually tailored therapies. However, they have had a limited impact on clinical practice to date due to a lack of evidence, particularly economic evidence. This is partly because health economists are yet to reach consensus on whether existing methods are sufficient to evaluate genomic technologies. As different approaches may produce conflicting adoption decisions, clarification is urgently required. This article summarizes the methodological issues associated with conducting economic evaluations of genomic interventions.

MATERIALS & METHODS

A structured literature review was conducted to identify references that considered the methodological challenges faced when conducting economic evaluations of genomic interventions.

RESULTS

Methodological challenges related to the analytical approach included the choice of comparator, perspective and timeframe. Challenges in costing centered around the need to collect a broad range of costs, frequently, in a data-limited environment. Measuring outcomes is problematic as standard measures have limited applicability, however, alternative metrics (e.g., personal utility) are underdeveloped and alternative approaches (e.g., cost-benefit analysis) underused. Effectiveness data quality is weak and challenging to incorporate into standard economic analyses, while little is known about patient and clinician behavior in this context. Comprehensive value of information analyses are likely to be helpful.

CONCLUSION

Economic evaluations of genomic technologies present a particular challenge for health economists. New methods may be required to resolve these issues, but the evidence to justify alternative approaches is yet to be produced. This should be the focus of future work in this field.

Multiomics medicine in oncology: assessing effectiveness, cost–effectiveness and future research priorities for the molecularly unique individual

The development of genomic technologies has ushered in the era of pharmacogenomics. However, discoveries and clinical use of targeted therapies are still in their infancy. A focus on monogenic pharmacogenetic traits may contribute to this lack of progress. Variation in drug response is likely a complex paradigm involving not only genomic factors but proteomic, metabolomic and epigenomic influences. The incorporation of these omics elements into pharmaceutical development and clinical decision-making will ultimately require the use of methods to determine clinical and economic value. Current methodologies and guidelines for determining clinical effectiveness and cost–effectiveness may have limited applicability to the increasingly personalized nature of omics treatment strategies. Using examples from oncology, this article argues for the adaptation and tailoring of three existing methods for ensuring development and clinical use of multiomics-guided therapies that are effective, safe and offer value for money.