Methods Matter: Selecting the best real-world approach for evidence generation. Louise Parmenter, PhD Senior Director, Late Phase Strategic Planning

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1 WHITE PAPER Methods Matter: Selecting the best real-world approach for evidence generation Louise Parmenter, PhD Senior Director, Late Phase Strategic Planning Executive Summary When it comes to improving the quality and consistency of the provision of healthcare, and maximising its value, evidence is vital. In the last few years, several trends have emerged that have increased the demands on companies to deliver stronger and more holistic evidence to support healthcare decision making. At pharmaceutical companies, development costs are increasing while productivity is on the wane and there are greater demands for evidence of safety, efficacy and effectiveness from a range of healthcare system stakeholders. At the national level, governments seek to control expenditure on healthcare and are making reimbursement contingent upon proven outcomes and value. Locally, healthcare providers are being asked to do more with fewer resources and to deliver healthcare interventions based on evidence of value. Finally, patients are demanding more information as well as greater quality and convenience. Every part of the healthcare continuum is under pressure. Over past decades, the lens has widened from an almost singular attention on clinical outcomes to a much broader focus on clinical, economic and humanistic outcomes. Unprecedented access to new data channels, new research designs and improved analytical techniques are together driving a paradigm shift in real-world methodologies. Never has the world of healthcare evidence generation been so rich and complex.

2 Table of Contents Introduction 03 The Methods Debate 03 Selecting the Best Real-World Approach 04 > Ethical Considerations 04 > Methodological Considerations 05 > Practical Considerations 06 Types of Real-World Approach 06 > Pragmatic Designs and Large Simple Trials 07 > Non-randomised de novo Epidemiological Studies: Cohort, Case Control, Patient Registries 07 > Database Epidemiological Studies 08 > Cross-sectional Studies 08 > Systematic Literature Reviews, Network Meta-analysis and Decision Analytic Models 08 > Innovation & Hybrid Research Designs 09 > Direct-to-Patient Studies The Next Frontier 10 Conclusion 10 2

3 Introduction As healthcare decision making becomes more complex, stakeholders are demanding real-world evidence tailored to their own individual needs. Each stakeholder group has a different decision to make and question to ask, and so approaches evidence from a slightly different perspective. For example, regulators looking at drug approval need to know whether a drug is safe and effective, but payers looking at drug coverage want evidence that helps them decide whether a drug should be included on the formulary and what it should cost healthcare providers and patients. Providers interested in the standard of care want to know whether the treatment should be incorporated into clinical practice guidelines, while local decision makers want to know whether a treatment should be available for their specific patient population. Patients want to know whether a treatment will make them feel better and is affordable. Clinically, after establishing that a drug is safe and effective, most of the important questions about drugs are comparative, aiding decision makers on what treatment to choose. Questions concern whether a drug works better, faster or in more patients than an alternative, whether it can be added to other treatment, or whether it conveys additional benefit. Unprecedented access to new data channels, new research designs and improved analytical techniques are together driving a paradigm shift in real-world methodologies. The Methods Debate Methods matter, as sound ethical, scientific and efficient approaches must be used to generate evidence for healthcare decision making. In contrast with the tightly defined research process to bring a drug to market, guidance on epidemiological, comparative effectiveness and safety research methods is more dispersed. Navigating this breadth information to determine which research approach to choose to answer a given question can be hugely challenging. Recently, the European Network of Centres for Pharmacovigilance and Pharmacoepidemiology (ENCePP 1 ) helped to clarify the situation by releasing a Guide on Methodological Standards in Pharmacoepidemiology. The ENCePP Guide provides a structured architecture that includes an overview of internationally acknowledged recommendations, key points from other existing English-language guidelines and standards in pharmacoepidemiology, and directions for learning on study design and methods. Rather than duplicating the text from existing guidelines and textbooks, the researcher is provided with a single overview document and web resource. The International Society for Pharmacoeconomic and Outcomes Research (ISPOR) Prospective Observational Clinical Studies Task Force will soon be reporting good research practices for prospective observational clinical studies that focus on the effectiveness and/or comparative effectiveness of health care interventions. 3

4 Sponsors have been criticised for undertaking studies and trials that have only one comparator, or are too small to yield definitive answers. Experts have questioned whether observational research approaches may be valid to answer comparative effectiveness research questions 2. Some ambitious long term trials such as ALLHAT 3 (The Antihypertensive and Lipid-Lowering Treatment to prevent Heart Attack Trial) have quickly become outdated, being overtaken by rapid changes in clinical practice. ALLHAT is one of the most cited pragmatic trials of all time, providing a salient example of the difficulties inherent in How often do regulators and payers provide Figure 1: How often do regulators and payers provide enough enough information on what study designs will comparative effectiveness information on what study designs will meet their needs? meet their needs? research. In positive contrast to many industry sponsored trials, All of the time the government funded ALLHAT trial studied multiple drugs In most cases In a few cases Never (chlorthalidone, lisinopril, doxazosin, amplodipine), but there were design problems. In addition, meta-analyses and 0% 10% 20% 30% 40% 50% 60% 70% 80% other trials showed different results. 4 In a recent poll of biopharmaceutical professionals responsible for real-world research, the majority responded that regulators and payers provide sufficient study design guidance in only a few cases (Quintiles Poll (n=22) May 2011 see Figure 1). Ethical Clinical Trial Conduct In 2010 the FDA requested that the Institute of Medicine IOM examine when and how to conduct clinical trials ethically to evaluate safety in approved drugs 5. The IOM recommends that the FDA conduct clinical trials only if: > there is not enough existing information to assess these risks; > information cannot be gained through a different kind of study; > the trial is appropriately designed to answer questions about a drug s efficacy and safety; and > the trial minimizes risk to its participants 5. Selecting the Best Real-World Approach Selecting the right approach based on ethical, methodological and practical considerations is critical. Ethical considerations must come first, such as those surrounding randomisation to treatments to evaluate known safety risks for marketed products. Second, the scientific approach must be valid to answer a given research question so that the facts generated have value. Third, the data must be delivered in a timely and cost effective manner in order for the insights gained to be relevant and affordable. Ethical Considerations Ethical considerations include patient rights and safety, data ownership and privacy, and the payment of honoraria to health care providers and patients. Legal requirements impact decisions involving the use of data for real-world research. Typically, database epidemiological studies are performed on aggregated de-identified data sets with randomised trials and non-randomised de novo epidemiological research requiring patient consent. National and local laws must be understood and respected, and for this reason, many sponsors have departments dedicated to interpreting data ownership and privacy legislation. Most sponsors have developed processes to avoid conflict of interest and inappropriate inducements to prescribe. Sponsors have come under considerable scrutiny over the payment of honoraria to healthcare professionals for research on marketed products. 4

5 Methodological Considerations Methodological considerations underpin the concept of research validity which, simply stated, is whether what is being measured is what was intended to be measured. Most commonly, the validity of research methods have been ranked in a linear hierarchy based on the perceived strength of the evidence generated. Systematic reviews and randomised controlled double-blinded clinical trials traditionally sit at the top of this hierarchy, while methods such as non-randomised trials, observational and descriptive studies traditionally appear further down. Expert opinion sits at the bottom of the hierarchy, considered to be the weakest form of evidence. The Concept of Research Validity: Whether what is being measured is what was intended to be measured 6 However, thinking has moved on in recent years. A different model using the analogy of a web was suggested in In this new model, clinical trials are represented by strong strands but the integrity of the web also relies on a variety of supporting strands from a diverse array of study types. One step on from this is the idea of a 360 value appraisal being required for medical decision making. 9 While systematic reviews and explanatory randomised controlled clinical trials will always be central, evidence will be viewed by individual stakeholders from their own, individual perspective and will assess value accordingly. Other methodological considerations include choice of comparator and outcome measurement, duration of patient follow-up, and sample size. Internal and External Validity > Internal validity is the extent to which the findings of the study accurately represent the causal relationship between an intervention and an outcome in the particular circumstances of an investigation. This includes the extent to which a study minimises any systematic or non-random error in the data. 7 > External validity refers to the extent to which the findings obtained from an investigation conducted under certain circumstances can be generalised to other circumstances. To the extent that the circumstances of a particular investigation (e.g., patient characteristics or the manner of delivering the treatment) differ from the circumstances of interest, the external validity of the findings of that investigation may be questioned. 7 It is a well-known fact that many biopharmaceutical products come to market with data on a few thousand patients. Yet, some small but significant treatment differences can only be demonstrated with data on tens of thousands of patients. Large sample sizes are able to show small but significant treatment effects that are not demonstrated with smaller patient populations. Trials conducted as part of the regulatory submission process may not include comparative data with existing standard of care in broad patient populations. Real-world studies seek to assess new treatment interventions against what is normally prescribed to enable relative effectiveness to be measured, including sub-populations that may not normally be assessed in the clinical trial process to bring a drug to market. Data collected on patients over time (i.e., longitudinally) provides insight into the effectiveness of long-term treatment and the changes in treatment practice over time, including the impact of new treatment interventions as well as off-label use. Sponsors should consider whether the outcomes of interest are captured as part of normal clinical practice (e.g., adverse drug reactions) or would require instruments (e.g., validated questionnaires), as well as to consider whether the outcomes (e.g., quality of life) are acceptable to decision makers. 5

6 Many biopharmaceutical products come to market with data on a few thousand patients. Yet, some small but significant treatment differences can only be demonstrated with data on tens of thousands of patients. Practical Considerations Practical considerations include the availability of technology, common reference standards, existing data sources, and patients, as well as the patient demographic. An important practical consideration is fundamental do the technologies exist to facilitate the research process? Technologies include data capture and communication tools (e.g., hand-held devices and web portals). Common data dictionaries are needed in order to combine data sets. Significant time and budget can be saved if the data is already available in health records, claims data or existing research databases. In the case of prospective research, sufficient patients must be available and motivated to participate. Any barriers to patient interface should be understood and allowed for in the research design. These can include factors such as language, mobility and ease with technology. Lastly, is it feasible to conduct the research in the time and budget available? A comparison of ethical, methodological and practical considerations for different real-world research approaches is shown at the end of this paper (Figures 4 & 5). Types of Real-World Approach Figure 2, The spectrum of research approaches, highlights research approaches available to generate evidence for healthcare decision making. Methods fall into the two broad categories of Experimental Research and Observational Research. In experimental research patients are randomised to a treatment and followed longitudinally, whereas in observational research patients are treated per normal clinical practice. Research data can be sourced in two ways. One option is to use data that is already being collected for another purpose, e.g., electronic health records, administrative databases. The other approach is to solicit new data specifically for the purpose of a research study, and is known as de novo data capture. Figure 2: The spectrum of research approaches Controlled Experimental Research Observational Research Real-world Explanatory RCTs Pragmatic RCTs Prospective/Retrospective Studies Randomised Blinded Controlled Trials Pragmatic Designs Including Large Simple Trials Non-Randomised De Novo Epi Studies Cross-Sectional Studies, Audits, Surveys Database Epi Studies Systematic Literature Reviews Decision Analytic Models Network Meta-analysis 6

7 More detail on each real-world research approach is provided below. Pragmatic Designs and Large Simple Trials While explanatory RCTs are generally considered to yield the least biased estimate of the effect of a specific intervention under ideal conditions (e.g., randomised double-blinded, placebo-controlled clinical trials), pragmatic RCTs can render a more representative estimate of the benefits and harms in typical patients. Large simple trials have many features of a pragmatic research approach. Pragmatic RCTs balance internal and external validity and, due to their size, can demonstrate moderate but clinically important treatment effects. + Advantages Disadvantages Large Simple Trials are able to show moderate but worthwhile benefits of treatment against active comparators, and randomisation of patients minimises the potential for bias and confounding increasing the strength of the evidence obtained. They balance internal and external validity to provide a more representative estimate of benefit/harm in typical patients. In addition, patient enrolment is not dependent upon treatment adoption and they can be used to test hypotheses. The design introduces a greater level of bias than with a highly explanatory RCT due to being open-label and therefore exerting less control over patient follow-up and means that it is less likely to demonstrate changes in standard of care over time. As is also the case with observational studies, Large Simple Trials are not placebocontrolled, and therefore the estimation of treatment effect (for both/ all arms) may be overestimated. In addition, research questions may become outdated in areas of rapid change of therapeutic care. Large Simple Trials are expensive compared to prospective observational cohort designs due to the need to provide treatment, and they present possible ethical issues associated with randomisation. Used to answer comparative effectiveness and safety questions, Large Simple Trials are typically community-based, with patients, and have a streamlined design with outcomes strictly limited to the primary research question. The protocol-driven aspects of Large Simple Trials occur entirely at baseline, when patients are randomly assigned to one of two (or more) active treatment arms. The randomisation is a critical element to promote research validity, but patients are subsequently followed per standard of care. Non-randomised de novo Epidemiological Studies: Cohort, Case Control, Patient Registries Non-randomised de novo epidemiological studies include cohort, case-control and patient registries. In cohort studies, patients are allocated to usual treatment and followed prospectively, helping to generate more specific estimates of the baseline risk of a target treatment. Case-control studies can be used to examine risk for uncommon harms and factors that modify risk, by matching people with a disease (cases) with people who do not have the disease (controls). Using Patient registries, observational study methods can be used to collect uniform data to evaluate a specific outcome for a population, allowing estimates of major harms across large populations, detection of rare events and estimates of adherence to treatment. + Advantages Disadvantages Prospective observational studies can examine longer-term outcomes in populations typically excluded from trials and examine risks for uncommon harms and factors that modify risk. They produce more representative data on a range of outcomes, including harms, and can be more cost-effective than randomised trial designs. They also allow assessment of actual use (including off-label) to identify potential new indications. These studies are prone to bias and confounding and in case-control studies can be difficult to match cases. Patient enrolment can also be slowed due to insufficient product usage. 7

8 Database Epidemiological Studies Database epidemiological studies mine existing data repositories for information, including paper and electronic health records and administrative databases. They can be used to determine the feasibility of, and to develop hypotheses for, prospective research designs. Database epidemiological studies can also quantify disease burden, evaluate treatment patterns and compare outcomes for marketed products. In addition, they can be combined with prospective data capture, thereby creating hybrid designs that are more time and cost-efficient than de novo data capture. Examples include: Cohort, Case-control and Data mining. + Advantages Disadvantages Database Epidemiological Studies can be used to assess benefits and harms across an extremely large population and are cost and time efficient compared to prospective, longitudinal research. The underlying information is not collected in a systematic way and it is difficult to interpret missing data. Data abstraction from paper records is resource intensive, complete medical and clinical histories may not be available (e.g., where a patient was referred to the clinic) and administrative databases and electronic health records are not uniformly available across all countries. Privacy issues may also create the need to aggregate data. Cross-sectional Studies Cross-sectional studies can be performed using existing data sources or by soliciting new data. They include audits and surveys, which examine the appropriateness of practice patterns and patient selection, and expert opinion, where individuals or panels are consulted to inform therapeutic guidelines and research questions. Cross-sectional studies can also take place through patient chart review or database analyses. + Advantages Disadvantages Cross-sectional studies can be used to inform more formal research approaches and can be time and cost-efficient. Expert surveys can be prone to personal bias and confounding. The limitations of existing data sources are described above. Systematic Literature Reviews, Network Meta-analysis and Decision Analytic Models These research approaches encompass data obtained from a number of studies and/or trials. Even though the underlying research may not always be real-world in design, they are discussed here for completeness. Systematic reviews pool data from a number of studies to approximate a much larger patient population; qualitative reviews summarise the results of studies, while quantitative reviews integrate the data from independent studies using statistical methods. + Advantages Disadvantages These approaches may allow for estimates of less frequent events and more stable estimates of treatment effect due to larger patient populations. They are also time and cost-efficient. Study heterogeneity may be difficult to assess and manage. The quality of the reporting in the source studies needs to be known in order to understand underlying study methods and assumptions. Network meta-analysis is becoming more accepted assessing relative effectiveness from randomised clinical trials and comparing evidence from randomised and observational research. 8

9 Decision Analytic Models also pool data from trials and other data sources in an effort to clarify the relationships between therapeutic processes, costs and outcomes, in order to highlight the differences between alternative treatments. Figure Do 3: Do you you consider consider your your company company to be to innovative be innovative in real-world in real-world research? research? 50% 40% 30% 20% 10% 0% More innovative than most companies like us About the same as most companies like us Still catching up to most companies like us Innovation & Hybrid Research Designs As the demand for real-world research grows, so does the pressure for innovation. Approximately one third of biopharmaceutical research professionals consider that their company is more innovative than others, leaving two thirds on a level playing field or less innovative than other companies (Quintiles Poll (n=22) May 2011 see Figure 3). Undoubtedly, innovation in methods will underpin success in real-world research. Hybrid research designs bring innovation through the combination of methodological approaches. A combination of data collection from existing data sources together with de novo data capture can produce a hybrid design that capitalises on the speed, cost efficiency and strength of different methods, utilising data already available with de novo data collection to shorten time and cost. For example, existing data can be used to inform the design of a de novo real-world study, such as identifying suitable sites and patients or generating hypotheses that can be tested in a more formal study. Alternatively, if the data present in a database is sufficient, existing data can be used as part of a trial dataset, creating a look back period and effectively extending the overall horizon of patient observation and/or shortening the follow-up period in a long study. Hybrid Design Case Example: The Partnership to Accelerate Clinical Electronic Research (PACeR) project 10 is an example of how real-world research may be revolutionized in the future through a hybrid blend of methods. Launched in New York State, this innovative project will connect hospitals, physicians, some patients homes and pharmaceutical scientists, offering valuable insights into current, real-world medical practices. PACeR is expected to significantly increase the speed, quality and efficacy of clinical studies, helping to provide patients with quicker access to new, life-saving medicines. 9

10 Hybrid Design Case Example (continued): The sponsor, the Healthcare Association of New York State, is working with a broad consortium of academic, biopharmaceutical and technology companies (including Quintiles) and have appointed advisors including the FDA, the Office of the National Coordinator for Health Information Technology, the National Institutes of Health and other federal and state agencies, Clinical Data Interchange Standards Consortium and Health Level Seven. The field of clinical analytics applied to modelling and managing clinical trials should rapidly advance as a result of the growth of large-scale web-accessible patient record systems and clinical repositories such as the one being built in New York State. Phase 1 of the scheme aims to identify barriers to the use of electronic health data for research, such as: insufficient clinical data; inconsistent systems; legal, regulatory and structural constraints; and economic sustainability. Phase 2 will see demonstration projects set up to address barriers and to develop an infrastructure for an efficient clinical research data network. This network will include realworld research applications, large-scale data access for protocol modelling and patient selection services, and wrap around clinical software systems to augment Electronic Medical Record data. When very large studies need to be kept simple for practical reasons, a useful hybrid approach is to conduct sub-studies alongside larger, simple prospective research. In this way, the large study remains simple and the small studies benefit from the efficiencies of running in parallel. One example would be to run sub-studies alongside larger studies in specific geographies to answer local economic questions. A second would be to select specific patient subgroups to answer clinical or humanistic questions that may be related to ethnicity, age or other population factors. With research costs escalating, the demands for such efficiencies have never been so compelling. Direct-to-Patient Studies The Next Frontier Online patient communities such as MediGuard or Patientslikeme that have a consented relationship with large patient pools, could transform current practice for observational research by reaching out to patients directly for study enrolment, without the need for physician sites. While these communities have already demonstrated the ability to collect patient-reported data on clinical, economic, and quality of life endpoints, MediGuard in particular has begun to deliver observational study designs that also include laboratory testing and medical record review in addition to patientreported data. The benefit of this integrated direct-to-patient approach is that it can generate data similar to a traditional, site-based registry in a shorter time frame and at a much lower cost. Conclusion A perfect storm of stakeholder demands, unprecedented data access, new data channels, new technologies, and design and analytic techniques is driving a paradigm shift in real-world research methodologies. With so many contrasting stakeholder needs and opinions, and the significant and evolving choice of research approaches, it has never been more challenging for research professionals to define what is required. Working backwards from the needs of the decision maker, and including ethical, methodological and practical considerations should ensure greater success in forward thinking real-world research approaches. 10

11 Figure 4: Comparison of Ethical and Methodological Aspects of Real-World Research Approaches Considerations Pragmatic Designs: Large Simple Trials Nonrandomised de novo epi studies Cross-sectional studies: audits, surveys Database epi studies Systematic Lit. Review, network meta-analysis & Decision Analytic Models EThICAl Data privacy Pharmaceutical codes Patient consent required METhODOlOGICAl Internally valid Externally valid Measures small but significant differences Need for long term data Humanistic outcomes (PROs) Patient consent required May or may not require patient consent May or may not require patient consent Relevant to underlying research Highly relevant Highly relevant Relevant Some relevance Relevant to underlying research Randomisation promotes stronger internal validity than other observational methods Strongest external validity of experimental methods if optimal design and analysis Excellent approach Strongest internal validity of observational methods if optimal design and analysis Can provide strong external validity if well designed Limited due to lack of randomisation Risk of weak internal validity due to data gaps, bias and confounding Validity depends upon size and completeness of data set but can be high for very localised research No Risk of weak internal validity due to data gaps, bias and confounding Can provide strong external validity due to use of vast data sets Can be used to see rare signals but not confirmatory Dependent upon underlying studies and trials Dependent upon underlying studies and trials Dependent upon homogeneity of underlying studies and trials Yes Yes No Yes Dependent upon underlying studies and trials No (recommend sub-studies) Yes Yes No No 11

12 Figure 5: Comparison of Practical Aspects of Real-World Research Approaches Considerations Pragmatic Designs: Large Simple Trials Nonrandomised de novo epi studies Cross-sectional studies: audits, surveys Database epi studies Systematic Lit. Review, network meta-analysis & Decision Analytic Models PRACTICAl Technology Existing Data Sources Patient Rarity Product Sales Patient Demographic Urgency (Quintiles broad estimates only) Budget (Quintiles ballpark estimates only) Essential for data capture and study management Not necessary but helpful for feasibility Difficult to conduct for rare diseases No dependency upon product sales Patient sub-populations can be targeted Essential for data capture and study management Not necessary but helpful for feasibility Registries are excellent for rare diseases Unable to run product study in countries without product sales but disease registries can be useful Patient sub-populations can be targeted 3 years 4 years dependent upon product real-world usage Broad range up to $100.0 M for a LST Good for rapid data capture and analysis May or may not be necessary Expert opinion critical but audits and surveys may be limited due to disease rarity Unable to run study in countries without product sales but disease studies can be useful Patient sub-populations can be targeted Need for common data dictionaries and data analytics Essential Large databases can help to access clinically relevant numbers of patients Useful for understanding prescribing of all products relevant to disease Patient subpopulations can be identified if included in existing databases Yes, for quantitative research Need high quality existing studies and trials Sufficient trials and studies may be a challenge No dependency upon product sales Patient sub-populations can be targeted if included in original research 1-3 months 1-3 months 1-3 months $ M $0.5 - $2.0 M $0.3 - $2.0 M $0.3 - $1.0 M 12

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