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  • The Contribution of Wearables Towards Healthcare Evidence Generation

    The Contribution of Wearables Towards Healthcare Evidence Generation

    The health and fitness wearable market is on the rise since the last few years. Analysts expect that by 2020, almost half a billion smart wearable devices will have been sold. However, despite the fast-growing market, only 10% of those consumers are using the product daily. This is an opportunity for innovative life science companies to tap into the market and create value-added services for consumers. Demand for wearables which include wristbands, smart garments, chest straps, sports watches and other health monitors is being driven by consumer fascination in quantifying personal health metrics, but it also opens up a world of opportunities to the wider healthcare and pharmaceutical industries. Pharma wants to take wearables beyond fitness trackers to add value through disease diagnosis and monitoring solutions in the form of medical-grade wearables, while also generating evidence in the form of real-world data. 

    In line with the evolution of patient-centricity, wearables have enabled us to become personal data creators, with constant streams of fitness and medical information being generated of our own volition. The question is can this be harnessed by the healthcare industry to drive efficiencies and enhance products and healthcare services in the long term? Apple thinks so. It launched the Apple CareKit this year: software that makes it easier for individuals to keep track of care plans and monitor symptoms and medication, with the ability to share that information with doctors or family. It delivers the promise of empowering the patient and personalizing their care patient centricity at its very best.  Apple also offers a ResearchKit, providing a software framework for apps that enables medical researchers to gather robust and meaningful data. Interestingly in July 2016, GlaxoSmithKline launched a rheumatoid arthritis study, called PARADE, and an iPhone app using Apple’s ResearchKit, demonstrating the first time a drug maker has used the open source software framework to conduct clinical research.

    Wearables are being put to use wherever we look, including in the NHS, which has endorsed wearable use in its own trials. In partnership with Diabetes UK and Hewlett Packard it is deploying mobile health self-management tools (wearable sensors and software) for people with Type 1 and Type 2 diabetes to self-manage their condition. The devices feed data back via the internet to allow more timely intervention from peers, healthcare professionals, carers and social networks, should their help be required.

    Evidence to date suggests that wearable technology is plugging a gap in our knowledge by way of collecting real-world data directly from patients. Hospitals are deploying wireless monitoring so that patient’s vital signs are automatically and continuously fed back to clinicians. Furthermore, sensors are now available that automatically measure and store glucose readings removing the need for finger pricking. But can it really help us as market researchers to better understand the patient journey- is the question.

    Results from a recent survey of HCPs and pharma professionals, which was presented at the BHBIA Conference (May 2016), shows that an already quite apparent use of wearable devices especially amongst HCPs. Two thirds currently make use of biometric data for personal reasons outside of work, which is found to be surprisingly high. But when the use of standard health applications on mobile devices is factored in, this may help to explain things. Over 40% of HCPs use biometric data for work-related purposes, but this is much lower for pharma. Again this seems high, but perhaps this supports increasing examples of patients bringing in their data on their devices to help inform consultations.

    The question is how pharma can generate evidence from wearables? Biometrics collected from wearables could potentially offer an exciting future for qualitative research – to determine what is actually happening instead of what patients tell us is happening. For example, while carrying out a qualitative interview with a patient and discussing mobility, data from their wearable device could help verify whether the patient has been as mobile as they say they have been.   From a quantitative perspective, biometric data from wearables could feed into online segmentation studies. For example, does segment A exhibit a higher heart rate and greater sleep disturbance? In this instance biometric data could be collected to help facilitate the analysis process and test additional hypothesis that at present are very difficult to test. And of course, there is the potential for biometric data to be collected alongside our on-going tracking studies – to monitor how important metrics are changing over time and to identify periods in the day/ week/ month where patients exhibit peaks and troughs.

    It seems the application for data derived from wearables has increasingly more value in the healthcare research process as the technology improves and becomes more reliable. Their potential lies in their inconspicuousness – we forget we’re wearing them, and they provide access to an un-simulated world of responses to everyday events to either back up or refute what we think we know already.

    There could be a very bright future for wearables at the centre of healthcare research, but there are many issues to address first. The accuracy of the data from wearables must be assessed. Not to mention the issues surrounding use; at the very least does the patient own a device and have they worn it at all times without tampering or hindrance? There are ethical issues around the data, gaining approval and ownership of the information generated. And of course, if there’s a problem in the data, should it be reported or not? There are still many unanswered questions.

    Wearables are unlikely to ever entirely supersede traditional market research, but it’s clear their use for wider healthcare research purposes is on the increase. They currently provide a valuable tool to use in conjunction with more traditional methods of research by offering context, putting clinical data into a more relevant light and further personalizing the patient journey. Could the collection of biometric data provide researchers with a new, previously inaccessible, form of insight that may help in further understanding the patient experience? Only time will tell.

    Become A Certified HEOR Professional – Enrol yourself here!

    MarksMan Healthcare

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  • How RWE can Impact Clinical Trial Design and Help in Decision Making?

    How RWE can Impact Clinical Trial Design and Help in Decision Making?

    Real-world evidence (RWE) research is gaining significant importance in biopharmaceutical product development as well as its commercialization. The increasing need of the industry to seek broader information about the safety and effectiveness in the real-world setting, which typically impacts the ensuing reimbursement and utilization of new products, is determined by regulators, public and private payers, and prescribers – in order to understand the impact of a new product in a such a setting. As a result, RWE is now included earlier in the research and development phase. (1)

    Real-world evidence or real world data (RWD) is nothing but the information collected under normal day-to-day circumstances that is found outside of a randomized clinical trial. This data is considered to be RWE when it is looked at from and analysed within the context of what is being measured. Ultimately, RWE can be used to evaluate treatment effectiveness in daily settings to guide clinical decision-making and answer scientific questions. (2)

    Today, healthcare industry is rapidly expanding with varied data sources, such as electronic health records (EHRs), insurance claims data, patient registries, surveys, medical devices, imaging, genomics, etc. that capture enormous amounts of patient health and medical information. These data reflect patient’s routine valuable health information in the context of real clinical practice. This evidence from real-world setting can be used to study different aspects, such as epidemiology and burden of a disease, co-morbidities, treatment patterns, adherence, and outcomes of different treatments. Therefore, RWE can be used to design clinical studies, inform hypotheses, thereby improving the probability of approval and successful treatment take-off. These applications not only facilitate compressed clinical trial timelines and consequent cost-savings, but they can also serve as a powerful complement to evidence gathered from randomized control trials (RCTs), which is a gold standard for assessing biopharmaceutical drug safety and efficacy among researchers. (3)

    Real-world evidence can significantly impact clinical study design. Here’s how: An effective trial design normally begins with creating a hypothesis and defining the patient cohort. This process requires the most extensive research and analysis, which is why it is a lengthy and iterative one, requiring many sequences of refinement. Real-world evidence can be optimized to test the hypotheses across diverse datasets quickly. With RWD, which consists of multiple and expansive datasets, specific insights to indication and severity of interest (e.g. rheumatoid arthritis, stage 4 chronic kidney disease, lymphoma, etc.) can be achieved in order to recognize clinical phenotypes, outcomes, unmet needs, and much more. Real-world evidence can provide answers pertaining to clinical gaps and the consequent findings can then be used to strengthen a hypothesis and further to design and tune the RCTs to all possible unmet needs. (3)

    This is because, conventional primary tools used to generate a hypothesis are limited to 1) existing published literature from previous studies conducted in the patient population of interest or 2) expensive and lengthy primary chart studies. These methods fail to provide extensive information, particularly regarding rare diseases and less common disease subsets. Advances in RWE technologies, therefore, in these instances, can provide researchers the ability to quickly test hypotheses to assess clinical relevance along with care and treatment pathways of the desired cohort. (3)

    In case of regulatory applications of RWE, various large data initiatives are developing standardized and consistent concepts across multiple data types and sources, in addition to bringing forward the importance of RWE in addressing concerns, such as medical product safety, patient-centred outcomes (PROs), and the value of new technologies and care delivery programs. There are many ways in which RWE can be harnessed to improve regulatory decision-making, such as to support the change of label change for new dosing administration or facilitate post-marketing safety surveillance. Furthermore, some applications can also apply RWE in historical controls or the progress and regulatory appraisal of products intended to treat rare disease populations. These examples have been fairly well characterised and followed by US FDA and industry stakeholders in recent years. (4)

    In our view, RWE studies are useful complements to RCTs, since they reproduce the routine utility of drugs, devices and other products, providing a more wide-ranging view of patient response to medications, improved assessment of disease patterns, additional information on safety as well as economic analyses. Moreover, since RWE provides the actual care that patients receive in clinics, which is not limited by a strict inclusion and exclusion criteria; it generates long term efficacy and safety data as well as economic assessment – all in a real-world setting. Additionally, it allows for a comparison between multiple interventions. (5) It can also facilitate well-informed healthcare and policy decisions. Healthcare industry, therefore, needs to keep up with new developments in RWE, data sources, analytical techniques, and study methodologies to ensure competitiveness as well as maximum ROI on new products. (2)

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    1. Cziraky M, Pollock M. Real world evidence studies. October, 2015. 
    2. Jadhav S. Using real world data to enhance clinical trials. January, 2017.
    3. Ahmed R, Rusli E. Using Real-World Evidence to Optimize Clinical Trials- Improving Trial Design, Patient Recruitment, and Data Analysis. Shyft Analytics. 
    4. Incorporating Real-World Evidence in Regulatory Decision-Making: A Pragmatic Approach to Randomization in the Clinical Setting. Margolis Center for Health Policy- Duke University. 
    5. Mahajan R. Real world data: Additional source for making clinical decisions. International Journal of Applied and Basic Medical Research 2015; 5(2):82.

    MarksMan Healthcare

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  • Tapping Payers’ Data to Document the Effect of a New Therapy in the Real World

    Tapping Payers’ Data to Document the Effect of a New Therapy in the Real World

    Traditionally, the pharmaceutical industry has always been dependent upon the ‘push’ strategy for successful market access for products. The drug approval process, involving submission of data on efficacy, safety, and tolerability to the regulatory agencies, used to be simple; which ended with the drug being marketed to the targeted physicians and dispensed by pharmacies post approval. Thus, this whole process involved a limited set of stakeholders, viz. physicians, regulatory agencies, and pharmacies. Conversely, over the years, the market access landscape has evolved primarily due to two factors: (1)

    1. Rising healthcare costs owing to an increasing prevalence of chronic diseases, growing geriatric population, and higher prices of new therapies
    2. Competitive pricing and reimbursement environment

    This has further led to the emergence of a new and diverse set of stakeholders over the years, i.e. the ‘Payer’(s), increasing the complexity of drug access to the market in general, and to patients in particular. Payer exercises the greatest degree of control over pricing and reimbursement for any new drug, and will continue to dominate the scenario to ensure successful market access. (2,3)

    Pharmaceutical advancements are increasingly conflicting as countries attempt to accommodate healthcare costs via different tools. New criteria for recognizing unique drugs and differences among those within the same therapeutic area or concerning the same molecule are being introduced, even though ‘price’ remains the main driver. (4) There is a surge of criticism towards the increasing prices of drugs that adds growing pressure on pharma companies and manufactures to limit future price increases, and eventually on payers to be more cost-effective in their approach to setting budgets and managing costs. (5) Global pharma operations need to keep up with the pace of these changes to approach pharma tendering as a strategy that spans pricing and commercialization.

    In order to document the effect of a new therapy in the real world, pharma companies are trying to justify prices by tapping payer’s data. Payers encourage pharma to collect post-launch evidence of product performance in the real world, thus turning it in pharma’s favor. This can help verify a price agreement or even clarify uncertainties about the clinical and/or safety outcomes outlined at registration. (6)

    The successful market access will involve collaborative team work between sales and marketing departments. The strategy itself should be well equipped to respond to market evolution and also, to accommodate all known interactions. There is no ‘one-size-fits-all’ solution. The challenges in the market will constantly vary as per the product, therapy area and the setting in which the treatment will be used. (1,6)

    Payers are increasingly focusing on “real-world” outcomes to form their decisions, encouraging new policies to be formed, in order to assimilate evidence from different sources. These policies prioritize the evidence that goes beyond information collected during clinical development in randomized controlled trials (RCTs), required by regulatory authorities for marketing approval. ‘Administrative data’- that normally use retrospective or real-time patient data – are an example of the real world data sources, as they are collected primarily for reimbursement, but contain some clinical diagnosis and procedure use with detailed information on charges. Retrospective analyses (longitudinal and cross-sectional) of clinical and economic outcomes at patient, group, or population levels can be performed with the help of claims databases. Such analyses can be performed in short time and at low costs. (7)

    In conclusion, payer’s data from real-world such as claims data can most certainly impact the sound coverage, payment, and reimbursement decisions. It is critical that payers recognize – a) the benefits, limitations, and methodological challenges in using these data, and b) the need to carefully consider the costs and benefits of different forms of data collection in different situations.

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    1. Kumar A, et al. Pharmaceutical market access in emerging markets: concepts, components, and future. Journal of Market Access & Health Policy 2014; 2:10.3402/jmahp.v2.25302.
    2. McClearn C, et al. Big pharma’s market access mission. Deloitte University Press; 2013. 
    3. Arx RV, et al. Leveraging success factors for market access in the life sciences industry. Capgemini Consulting and Cegedim dendrite; 2009.
    4. Skinner JS. The costly paradox of healthcare technology. September, 2013. 
    5. Pharmaceutical pricing and market access 2017. 
    6. Wechsier J. Measuring the value of prescription drugs. Pharmaceutical Executive 2017; 37(5).
    7. Garrison LP Jr. Using real-world data for coverage and payment decisions: The ISPOR Real-World Data Task Force Report. Value Health 2007; 10(5):326-225.

    MarksMan Healthcare

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  • Importance of Tapping Payer’s Data to Document the Effect of a New Therapy

    Importance of Tapping Payer’s Data to Document the Effect of a New Therapy

    Traditionally, the pharmaceutical industry has always been dependent upon the ‘push’ strategy for successful market access for products. The drug approval process, involving submission of data on efficacy, safety, and tolerability to the regulatory agencies, used to be simple; which ended with the drug being marketed to the targeted physicians and dispensed by pharmacies post approval. Thus, this whole process involved a limited set of stakeholders, viz. physicians, regulatory agencies, and pharmacies. Conversely, over the years, the market access landscape has evolved primarily due to two factors: (1)

    1. Rising healthcare costs owing to an increasing prevalence of chronic diseases, growing geriatric population, and higher prices of new therapies
    2. Competitive pricing and reimbursement environment

    This has further led to the emergence of a new and diverse set of stakeholders over the years, i.e. the ‘Payer’(s), increasing the complexity of drug access to the market in general, and to patients in particular. Payer exercises the greatest degree of control over pricing and reimbursement for any new drug, and will continue to dominate the market access scenario to ensure successful market access. (2,3)

    Pharmaceutical advancements are increasingly conflicting as countries attempt to accommodate healthcare costs via different tools. New criteria for recognizing unique drugs and differences among those within the same therapeutic area or concerning the same molecule are being introduced, even though ‘price’ remains the main driver. (4) There is a surge of criticism towards the increasing prices of drugs that adds growing pressure on pharma companies and manufactures to limit future price increases, and eventually on payers to be more cost-effective in their approach to setting budgets and managing costs. (5) Global pharma operations need to keep up with the pace of these changes to approach pharma tendering as a strategy that spans pricing and commercialization.

    In order to document the effect of a new therapy in the real world, pharma companies are trying to justify prices by tapping payer’s data. Payers encourage pharma to collect post-launch evidence of product performance in the real world, thus turning it in pharma’s favor. This can help verify a price agreement or even clarify uncertainties about the clinical and/or safety outcomes outlined at registration. (6)

    The successful market access will involve collaborative team work between sales and marketing departments. The strategy itself should be well equipped to respond to market evolution and also, to accommodate all known interactions. There is no ‘one-size-fits-all’ solution. The challenges in the market will constantly vary as per the product, therapy area and the setting in which the treatment will be used.i,vi

    Payers are increasingly focusing on “real-world” outcomes to form their decisions, encouraging new policies to be formed, in order to assimilate evidence from different sources. These policies prioritize the evidence that goes beyond information collected during clinical development in randomized controlled trials (RCTs), required by regulatory authorities for marketing approval. ‘Administrative data’- that normally use retrospective or real-time patient data – are an example of the real world data sources, as they are collected primarily for reimbursement, but contain some clinical diagnosis and procedure use with detailed information on charges. Retrospective analyses (longitudinal and cross-sectional) of clinical and economic outcomes at patient, group, or population levels can be performed with the help of claims databases. Such analyses can be performed in short time and at low costs. (7)

    In conclusion, payer data from real-world such as claims data can most certainly impact the sound coverage, payment, and reimbursement decisions. It is critical that payers recognize – a) the benefits, limitations, and methodological challenges in using these data, and b) the need to carefully consider the costs and benefits of different forms of data collection in different situations.

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    1. Kumar A, et al. Pharmaceutical market access in emerging markets: concepts, components, and future. Journal of Market Access & Health Policy 2014; 2:10.3402/jmahp.v2.25302.
    2. McClearn C, et al. Big pharma’s market access mission. Deloitte University Press; 2013.
    3. Arx RV, et al. Leveraging success factors for market access in the life sciences industry. Capgemini Consulting and Cegedim dendrite; 2009.
    4. Skinner JS. The costly paradox of healthcare technology. September, 2013. 
    5. Pharmaceutical pricing and market access 2017.
    6. Wechsier J. Measuring the value of prescription drugs. Pharmaceutical Executive 2017; 37(5).
    7. Garrison LP Jr. Using real-world data for coverage and payment decisions: The ISPOR Real-World Data Task Force Report. Value Health 2007; 10(5):326-225.

    MarksMan Healthcare

    , , , , , , , ,

  • Whats is the Reality and Future of Real World Evidence?

    Whats is the Reality and Future of Real World Evidence?

    The term real-world evidence (RWE) commonly applies to data generated in non-randomized clinical trials (RCTs) in a healthcare setting, which sometimes also covers patient socio-economic data and environmental data. Irrespective of the definition, RWE offers significant advantages over RCTs that have well documented limitations, despite being recognized as the gold standard for evidence-based medicine. Today, in the era of value-driven healthcare resource management, RCTs designed for regulatory approval cannot deliver all the evidence needed to support payer coverage decisions. Here, RWE plays a role of complementing RCT data in a way of measuring effectiveness, providing larger and more representative sample sizes over longer periods of time, and recording long-term patient behaviors like adherence. (1)

    Nonetheless, RWE has been in routine use for a while in the EU, particularly for products already approved for safety monitoring and drug utilization. Electronic health records (EHR)/ medical records (EMR), national repositories of curated claims data, clinical registries, wearable technologies and apps- all facilitate the ability to collect real-world data. Easier data capture contributes to the increasing interest in the use of RWE by payers and decision-makers. Increase in health technology assessment (HTA) applications worldwide significantly highlights the health economic and outcome research (HEOR) analysis, with critical inputs from RWE. Moreover, methods for establishing value-based healthcare benefit design will demand more RWE. Also, payers need to evaluate RWE outcomes in order to establish pricing and reimbursement criteria. (1)

    The fundamental aspect of healthcare involves the core ability to access data, describing the current standard of care, along with gaps and deficiencies in the care model, and social or patient-reported data, i.e. patient reported outcomes (PRO). This is where real-world data (RWD) originates – countless sources linked together to provide a view of a patient’s health history that can be acted upon using insights from advanced analytics. (2) However, as numerous data sources are growing at an extremely fast rate, it’s a challenge to collate, analyze and interpret this level of data generation. Data quality and data integrations can be extremely demanding and shouldn’t be underestimated.

    Collaborative initiatives are in place to sustain the development and utilization of RWEs in the West. Although, the uptake of RWE in clinical and therapeutic guidelines is slow, there is an increasing trend in the use of healthcare system data for informed clinical practice. To meet this demand, companies need to work together to enable or improve data access, undertake translational and relevant research and establish sources of reliable evidence. Furthermore, RWE is now becoming the foundation for new pricing strategies that can more explicitly provide therapeutic value to patients and health outcomes benefits to health systems and risk-bearers. RWE also provides a new basis for establishing business-to-business relationships or for having leadership conversations between large risk-bearing health systems and increasingly analytic-centric health insurers and payers. (1,2)

    It’s believed that, RCT data streamlining can be easier if RWE is appropriately adopted by sponsors of new drugs and devices and regulatory agencies. RWE generates new forms of evidence that many decision makers are thinking to consider alongside traditional RCT evidence. Certainly, prospective trials analyzing RWD together with classic RCT data are complementing to RCTs. (2)

    As sources of RWE become available to a greater extent, RWE is being incorporated more often into the necessary data packages for reimbursement, along with a growing desire from regulatory and HTA bodies. However, hindrances in terms of data quality and the integration of disparate sources of data will provide a challenge in the near future. Furthermore, pharmaceutical companies must keep up with the pace of advances in RWE through their own investment and partnerships in RWD sources and respective analytics services. They should be reshaping their necessary skill sets to best address the value, market, and financial opportunities that RWE will offer them.

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    1. The reality of real-world evidence. PMLive. January 2017.
    2. Elton J. The reality behind real-world data and real-world evidence. August 2015.

    MarksMan Healthcare

  • How to Elicit Expert Opinion to Understand Missing Health Outcomes?

    How to Elicit Expert Opinion to Understand Missing Health Outcomes?

    Missing data are a big concern in any research project and are often unavoidable in spite of investigators’ best efforts. Missing outcomes have two effects: reduced precision and power, and bias. Also, the loss of precision is inevitable, except the possible use of the available data; e.g. to be sure not to exclude from the analysis individuals who dropped out before the end of the study but who nevertheless reported intermediate values of the outcome. However, the statistician can aim to reduce bias through suitable choice of an analysis. (1)

    Randomized controlled trials (RCTs) typically have missing outcome data for some participants. Patient-reported outcomes (PROs) such as health-related quality of life (QoL) are mostly prone to missing data due to patients failing to complete follow-up questionnaires. Assumptions are often applied in case of statistical analyses for missing data to explicitly specify the values of the missing data: e.g. missing values being failures, as in smoking cessation trials. Other assumptions are inherent statements about the similarity of distributions, such as ‘last observation carried forward’. (1,2)

    In the primary trial analysis, an approach is often proposed which is valid under plausible assumptions for studies with the missing data. Instead of assuming that the data are ‘missing completely at random’ (MCAR), the primary analysis should suppose them to be ‘missing at random’ (MAR), i.e. the probability of missing data does not depend on the patient’s outcome, after conditioning on the observed variables (e.g. the patients’ baseline characteristics). However, the MAR assumption is unlikely to be used in many settings; for example, patients in relatively poor health are less likely to complete the requisite questionnaires, thus making the outcome data ‘missing not at random’ (MNAR). (2)

    The US National Research Council (NRC) report on missing data in clinical trials advocates sensitivity analyses for recognizing the data to be MNAR, in accordance with general methodological guidance for dealing with missing data and previous specific advice for intention-to-treat (ITT) analysis in RCTs. (3) On the other hand, systematic reviews show that in practice RCTs do not handle missing data appropriately. (4) Sensitivity analysis can be approached with either statistical modeling of parameters that represent outcome differences between individuals with complete versus missing data and/or exploring varying inferences with respect to the ‘sensitivity parameters’ assuming specific values. (5) The final output, i.e. results and conclusion, can then be compared over a reasonable range of values, possibly including a ‘tipping-point’ when results change. However, this approach does have a set of shortcomings. (2)

    An alternative is to allow experts to quantify their views. This is not only more intuitive and attractive for them, but it also considers a fully Bayesian approach and properly captures and reflects expert opinion (and associated uncertainty) about the missing data in the subsequent estimate of the treatment effect and its credible interval. This is particularly useful for those needing a quantitative summary of the trial, such as systematic reviewers, decision makers and health providers, as it provides a quantitative synopsis of interpretation of results by experts, given the missing data. When reviewing the study, experts will implicitly ‘fill in’ the gaps created by the missing data to arrive at their conclusions. The proposed elicitation approach, together with a Bayesian analysis, allows the study to comprehensibly quantify the impact of incorporating expert knowledge through to the estimates of treatment effectiveness.(1,2)

    Sensitivity analyses using Bayesian approach require practical tools for easier expert elicitation, and recent research focuses on elicitation approaches within group meetings. Group level elicitation has benefits for training and clarification and facilitates behavioral aggregation for achieving consensus. (6) However, because of the ‘feedback’ loop, these approaches are costly in both money and time. Thus, in many RCTs, it may not be viable to elicit opinion from a sufficient number and range of experts. Easier uptake of recommended approaches for sensitivity analysis for missing data within RCTs requires more accessible, practical tools for eliciting and synthesizing expert opinion to be developed and exemplified. (2)

    Using open source software like face-to-face or online ones to elicit beliefs from reasonably large number of experts without imposing an undue burden is one option that has been recently suggested. With this tool, the elicited views can be converted into informative priors for the sensitivity parameters in a pattern-mixture model which will allow for correlation in the elicited values across the trial arms. After this, the trial data can be re-evaluated under different MNAR assumptions to explore the robustness of the results. These methods, along with the expected level of loss to follow-up, could provide an improved estimate of the probable impact of missing data on the trial’s results. Therefore, this approach can significantly help improve trial design, so that the study results are more robust to anticipated levels of missing data.

    Become an Certified HEOR Professional – Enrol yourself here!

    References

    1. Jackson D, White IR, Leese M. How much can we learn about missing data?: an exploration of a clinical trial in psychiatry. Journal of the Royal Statistical Society Series A, (Statistics in Society). 2010; 173(3):593-612.
    2. Mason AJ, Gomes M, Grieve R, et al. Development of a practical approach to expert elicitation for randomized controlled trials with missing health outcomes: Application to the IMPROVE trial. Clinical Trials 2017; 14(4):357–367.
    3. Little RJ, D’Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med 2012; 367(14):1355–1360.
    4. Bell ML, Fiero M, Horton NJ, et al. Handling missing data in RCTs; a review of the top medical journals. BMC Med Res Methodol 2014; 14:118.
    5. Little RJA. A class of pattern-mixture models for normal incomplete data. Biometrika 1994; 81(3):471–483.
    6. O’Hagan A, Buck CE, Daneshkhah A, et al. Uncertain judgments: eliciting experts’ probabilities. 1st ed. Hoboken, NJ: John Wiley & Sons, 2006.

    MarksMan Healthcare

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  • Excerpts from the Workshop – “R WE Ready?” – “Real World Evidence” Methods!

    Excerpts from the Workshop – “R WE Ready?” – “Real World Evidence” Methods!

    Workshop title – “R WE Ready?” – “Real World Evidence” Methods!

    Organisers – Co-organised by Organisation of Pharmaceutical Producers of India (OPPI) and ISPOR India-Mumbai Chapter
    Objectives – To enhance skill set of the professionals of the medical affairs community in area of real world evidence generation to create patient value
    Date – June 23, 2017 with ISPOR India-Mumbai Chapter
    Venue – Imperial Hall, Hotel St. Regis, Lower Parel, Mumbai

    Workshop Agenda

    • Real world evidence: what it is, how it is important for medical department, developing a standard framework to help generate RWE
    • Health economics and outcomes research studies and its methodology; how HEOR studies can help in clinical decision making and shaping policy
    • Regulatory aspects: Current regulations governing RWE generation in India and regulated countries.

    Mr. Sanjeev Navangul
    Keynote speaker, Vice President, OPPI; Chair, Medical & Regulatory Work Group; Managing Director, Janseen India, J&J

    “Every drug that is discovered and launched in the market is a promise based on the evidence obtained from a structured clinical trial program”

    Highlights:

    • Effort to deliver the real world evidence (RWE) is far more crucial than clinical trials
    • More the real world evidence, more closer we go to the promise we make for better healthcare delivery
    • Health Economics is a much more important topic for countries like India that are self-pay (out of pocket) markets than countries where reimbursed markets exist

    Synopsis:
    Mr. Navangul welcomed the audience while expressing his views on the importance of generation of real world data and the healthcare professionals (HCPs) must constantly strive to deliver the real world data, dealing with the ‘real’ patients, since the clinical trial data may not suffice in the real world clinical practice. Also, in India, where the healthcare services are paid for out-of-pocket, incorporating health economics is the need of the hour, which will justify the ‘value’ of service and not just the price.

    Dr. Suresh Menon
    Keynote speaker, Co-Chair, Medical & Regulatory Work Group, OPPI; Chief Scientific Officer, Novartis

    “Huge variability in the label approvals and the clinician’s use (prescriptions) in real world practice”

    Highlights:

    • His own experience with the real-world evidence data from a global study by Novartis
    • Difference between RCTs and real world evidence

    Synopsis:
    Dr. Menon set the context for the session by giving an example of a global pivotal study and how they experienced the variability in the approved label and the real world data. He also opined on the differences between RCTs and RWE and importance of meaningful RWE.

    Mr. Desmond Lim (Special Invitee)
    Business Lead, Connected Health & Innovation, Asia-Pacific, Quintiles IMS, Singapore

    “Digitization of patient data for easy healthcare delivery”

    Highlights:

    • Some insights on problems faced by patients in clinical trials
    • Ways to regulate clinical processes
    • An app for sites and patients in order for them to enter their own data and accelerate easy access and data collection- This is not quite prevalent in Asia

    Synopsis:
    Mr. Lim stressed on the importance of digitization of patient data by means of patient/user-friendly apps. Such apps are the future of healthcare and they’re expected to encourage electronic data collection, thereby facilitating easy healthcare delivery by deploying simple technologies. This use of technology can assure data reliability and quality, save time and also be cost-effective in the long run.

    Dr. Viraj Suvarna [Chairperson: Real world evidence methods]
    Medical Director, Boehringer Ingelheim

    “Need of professionals (with both personal and professional traits) and ‘heart’ workers who’ll put their heart in their work”

    Highlights:

    • Competitive collaboration to facilitate RWE studies
    • Hybrid studies

    Synopsis:
    Dr. Suvarna headed the first session and while he started the session, he spoke about the collaborative studies where the doctors will perform RWE studies along with RCTs to know the patient responses/ profiles. He used the term ‘hybrid studies’ or ‘large simple trials’ which follow after efficacy, effectiveness, phase IIB stages of a clinical trial and incorporate real world data.

    Ms. Richa Goyal
    Senior Manager, Real World Insights (RWI), Quintiles IMS

    “Big data is getting bigger and bigger!”

    Highlights:

    • Scope and applications and source of RWE
    • What is RWE?
    • RCT+RWE: Strong evidence
    • Evidence platforms

    Synopsis:
    Ms. Goyal started the session by giving a brief of scope, sources and applications of RWE. It’s a well known fact that RCTs are the gold standard for evidence. Ms. Goyal opined that, when RCTs and RWEs are done together, they can strengthen the evidence. “Data is the new oil”; meaning, the vast and humongous healthcare data is still being discovered and if it is channelized and harnessed properly, it can entirely change the face of all the associated stakeholders. Furthermore, she also spoke about various RWE data sources which may include HCPs, electronic medical records (EMRs), registries, pharmacies, social media, lab data, and so on. Companies can collaboratively work on channelizing and harnessing the available data and put it to maximum use by using analytical tools, publications, patient analytics, consultations, KOL engagement or own in-built databases. She further mentioned that RWE cuts through all stages of clinical drug development till drug delivery and it can be analyzed in parallel with the clinical trial. In RWE, payers and providers work together to generate substantial evidence beneficial for the patients. Ms. Goyal further pointed out the key challenges for conducting RWE studies, viz. no set guidelines, no transparency or reproducibility and limitations of bias. She also explained the 3 A’s of the drug for patients: Approvals, Accessibility, and Affordability. To add, not only primary but secondary data are also important in RWE, viz. assumptions, modeling studies, budget impact analysis, systematic reviews and meta-analyses, HTAs and so on. In the end, Ms. Goyal also threw some light on the position of RWE in India along with the current challenges and probable solutions to overcome those challenges.

    Ms. Amita Bhave
    Head- Regulatory Affairs Global Drug Development, Novartis

    “RWE is important in regulatory decision making”

    Highlights:

    • RWE guidelines and regulatory scenario around RWE research in regulated countries
    • Current position of RWE in the western regulated markets
    • Importance of RWE in decision making

    Synopsis:
    Ms. Bhave shared some insights on the status of RWE in the regulated countries, e.g. US (USFDA), Europe (EMA). These countries have some regulations in place for the evidence from real world (E.g. PDUFA- Prescription drug user-free drug act by USFDA for RWE activities, 6th version of this act to be enacted in October, 2017). In the near future, USFDA will be conducting pilot studies and publishing guidance on how RWE can prove the safety and effectiveness in regulatory submissions or can contribute in the decision making processes. Additionally, Cures Act is in place which will be drafting a framework for RWE activities. Furthermore, USFDA will be conducting RWE workshops that would discuss the role of RWE in decision making along with the ways to overcome challenges faced by RWE activities as of now and bridging the gaps in data collection. EMA also conducted two workshops recently viz. patient registry workshop and regulatory data workshop – both focusing on the post-authorization area. EMA also describes a lot of adaptive pathways in regards with the RWE data. Ms Bhave also highlighted the outcomes of the Big Data Workshop by EMA and possibilities of regulatory tools for analysis of RWE data in order to incorporate it in the decision making, thereby promoting the use of RWE data. Although, very little data available on the RWE regulations in UK or Japan and challenges still exist with respect to its use in regulatory approvals or decision making processes.

    Dr. C S Pramesh
    Chief, Thoracic Surgery Professor, Dep’t of Surgical Oncology, Tata Memorial Center

    “Population based research has great potential to reduce mortality in cancer or for that matter, any disease”

    Highlights:

    • How RWE can be used in decision making processes
    • “Getting the right treatment to the right patient at the right time”

    Synopsis:
    Dr. Pramesh, who is an eminent name in the current cancer research in Mumbai, opined on some advantages as well as disadvantages of RCTs. Reasons being, 1) RCTs are not generalizable as they have eligibility-inclusion/exclusion criteria; 2) they are not large enough to identify everything to be identified about a new treatment that is going to be launched in the market; 3) RCTs lack external validity; 4) They ignore variability in care delivery. He further shared the initiatives implemented by Tata Memorial Center (TMC) like, 1) National Tumor Board that fosters anonymized data sharing on a secure web-based platform in order to reach out to cancer patients and providing easy care; or 2) Navya which is a service for cancer patients to obtain second opinion. Dr Pramesh even shared some case studies where RWE has been implemented in the research that has shown noticeable results. He also opined that about 2/3rd of the patient population may not benefit out of the treatment which it is actually labeled for in the real world practice.

    Dr. Jitender Sharma
    Founder and CEO, Andhra MedTech Zone (AMTZ)

    Highlights:

    • RWE in addressing the efficacy and effectiveness gap
    • Use of HTA and related tools

    Synopsis:
    Dr. Jitendar Sharma addressed the importance of promoting RWE in India by way of looking after the efficacy and effectiveness gap. He suggested ways of doing so with the help of HTA and budget impact analysis. He further opined that evidence can be too logical and one must go beyond the logic and prove the efficacy or feasibility of a drug/treatment for the patients, which is the general concept behind ‘value-based medicine’.

    Panel Discussion – I

    The panel discussed at length about the need for RWE in the Indian scenario and the challenges that could be faced by while implementing it. The panel unanimously opined that data sources could be a big challenge; therefore data collection should be done in an organized manner. In case of execution, the challenges exist with regards to the lack of awareness about designing a RWE protocol among the industry professionals. Value-based medicine is a new term and it needs to be promoted. At the same time, affordability of the treatment is necessary. While the external regulatory environment is quite strong, the same needs to be available in India to adapt to the RWE world and also to bring the RWE into practice. To add to it, a structured framework and capability building are also need to be in place in order to overcome challenges. More and more companies are considering patient support programs or disease/wellness management program to gain data directly from patients in order to analyze it and use it as evidence. Furthermore, lack of Indian data can be attributed to the lack of logistics and to tackle this, safe data sharing on a secure platform needs to be promoted along with tools that will be comprehensible for patients. Also, equipping the HCPs to use the language that patients would understand will also be helpful.

    Ms. Suneela Thatte [Chairperson: Health Economics and Outcomes Research]
    Co-Chair, Medical & Regulatory Work Group, OPPI; Vice President, Global Operations, Quintiles IMS, India

    “Similarity between RCTs and RWE”

    Highlights:

    • Supporting the drug to be launched in the market having RCT data with RWE, making it available to the end-user

    Synopsis:
    Ms. Thatte, while setting off the context for session 2, stressed on the importance of merging RWE data along with that of RCT in the drug development process itself. This will make the drug to strongly hold the place in market, along with the data from real world patients (beyond the eligibility or inclusion/exclusion criteria of an RCT).

    Mr. Mahendra Rai
    President-Elect, ISPOR Mumbai; Delivery-head, Market Access, HEOR, PRMA, RWE), Tata Consultancy Services

    “The guidelines for RWE are in place and being reviewed’”

    Highlights:

    • An overview of health economics and outcomes research
    • Introduction to healthcare costs and analysis
    • HTA in India

    Synopsis:
    Mr. Rai introduced the audience to the concepts of health economics, healthcare costs and healthcare perspectives that are a part of health economic evaluations. The costs and perspectives form an important part of any economic evaluation.

    Dr. Amit Dang
    President- ISPOR Mumbai; Founder and CEO, MarksMan Healthcare Solutions LLP

    “Though patient involvement is necessary today, ‘over-empowerment’ of patients can be harmful for them”

    Highlights:

    • Comparative effectiveness research (CER) – Methods and applications
    • An Indian perspective towards CER at a very nascent stage

    Synopsis:
    Dr. Dang stressed on the importance of patient centeredness and also threw some light on patient preference studies/patient centric clinical trials/patient advocacy. This kind of patient involvement is the future of Indian clinical trials, where patients will be involved right from the protocol design stage. He further added that there needs to be a constant flow of information from physicians. CER compares the effectiveness of two or more drugs or medical devices/services; however, it involves patient centric healthcare. HCPs need to keep up with the pace and keep themselves updates because today’s patients can easily access the internet and vast information available online. Dr Dang further explained about the stakeholders (patients, payer, provider, government and pharma industry) and their role in the RWE area. He also explained the steps involved in the CER, viz. gap identification, evidence generation by means of pragmatic studies (more observational studies), and evidence synthesis with the help of systematic literature reviews and meta-analyses. Furthermore, Dr Dang discussed about the impact of CER on RWE.

    Panel Discussion – II

    The panel discussed about ways to adopt for the development of RWE framework. The panel members unanimously agreed upon the need for involving the regulatory bodies and government to ease out the process of using RWE studies as constructive evidence. More epidemiological studies are required to generate RWE data. Monitoring the recent trends during data collection is also a necessary step in order to utilize those during drug development phase. The panel also discussed about the role of academia in development of RWE framework. Finally, a couple of important suggestions came up during the panel discussion that could possibly form some sort of a basis for the RWE framework. One option is to build a narrative to submit to the government in order to supplement the evidence generated from RCTs with the RWE data. Second approach is to make policy for rare diseases, like the one launched for cancer; which will definitely be of some help in using the real world data in the clinical setting.

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  • Adaptive Licensing and Real World Evidence (RWE)

    Adaptive Licensing and Real World Evidence (RWE)

    We all want safe and effective medicines to reach patients as soon as possible, but as we know, drug development, market authorization and payer assessment are all slow sections of a long and drawn out journey for a drug. But what if patients could have access to medicines not just months earlier, but potentially 8 years earlier? This is exactly what the European Medicines Agency (EMA) have in mind, as they lead a broad and diverse group of key stakeholders towards a root-and-branch upheaval of current practice. Adaptive Licensing (AL) (earlier known as adaptive pathways; AP), an ambitious and evolving new initiative which incorporates Real World Evidence (RWE): clinical data collected outside of a conventional randomized controlled trial. AL reforms the existing regulatory approach.

    In March 2014 EMA launched a pilot project to explore the adaptive pathways approach, a scientific concept of medicines development and data generation intended for medicines that address patients’ unmet medical needs. AL seeks to balance timely access for patients who are likely to benefit most from the medicine with the need to provide adequate evolving information on the benefits and risks of the medicine itself. AL is not a new route of approval for medicines. It makes use of existing approval tools, in particular conditional marketing authorization, which has been in operation in the European Union (EU) since 2006. It also builds on the experience gained with strengthened post-marketing monitoring tools introduced by the 2012 pharmacovigilance legislation (e.g., post-authorization studies and patient registries). The adaptive pathways concept is not meant to be applicable to all medicines, but only to medicines that are likely to offer help for a patient population with an unmet medical need, and where the criteria for adaptive pathways apply.

    Notwithstanding the classic randomized controlled clinical trials (RCTs) are Gold Standard for the regulatory approval of new technologies, their inherent generation of efficacy and safety data, are not always utilizable in the daily context. Items such as ‘homogenous populations without other diseases than the one explored in the study’, ‘placebo comparator, not the standard treatment or other active comparator’, and ‘high adherence,’ are just to nominate some points, which are far from the regular use of a medication on the part of patients and healthcare professionals. Even though currently, in parallel with clinical studies, collection programs of observational information are more and more generated, the available evidence is limited and onerous in case of necessity of large volumes; at least by means of clinical studies. This can be overcome with the help of real-world data.

    RWE refers to the planned and systematic recollection of the data generated outside the clinical studies. Adaptive approaches link decision making to an evolving evidence base, parts of which are frequently seen as being derived from analyses of observational data gathered from sources such as electronic medical records, registries or administrative databases. Acceptance of such evidence is an important issue- regulatory authorities and payers are currently prepared to accept observational data to support manufacturers’ efficacy/effectiveness claims only in limited circumstances. In the pilot project, the concept of RWE was expressly intended as wide ranging, encompassing different types of observational research that may be utilized to supplement randomized clinical trials. This was to encourage the submission of different approaches, not all of which could be foreseen at the conceptual stage, with the intent to highlight possibilities, needs and maximize the learning potential.

    RWE data collection within AL has the potential to improve our understanding of disease processes, epidemiological factors, and difficult issues such as adherence, which will in turn allow RCTs to become more efficient. Additionally, for many subpopulations, the life span approach to licensing and coverage and learning from real-world experience as advocated by adaptive pathways will become the only viable access route to new treatments in future. Additionally, in-depth knowledge of the natural history of diseases, existing baseline data, as well as other epidemiology aspects gleaned from existing databases or emerging large data networks and reanalysis of past trials helps to make RCTs more efficient and identify surrogate endpoints, and may increasingly obviate the need for concurrent control groups, e.g., in rare diseases. This knowledge and data can also be leveraged for the post-initial licensing evidence generation foreseen under AL, by providing a reference point against which the real-world performance of a treatment can be assessed.

    Further important steps towards enabling AL are currently being taken. Regulators have just begun to explicitly address and communicate “uncertainty” in their templates for benefit–risk assessment. A growing number of regulators and payer (or HTA) organizations involve patients in their decision-making processes. This can be considered as a pertinent analogy for the history of bringing new drugs to market.

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