By Jacob Barhak, PhD (https://sites.google.com/site/jacobbarhak/)
The Reference Model for disease progression is used to compare existing models that forecast chronic disease progression. When given a population, the model attempts to predict the number of complications and morbidities such as stroke, MI, or number of deaths that will occur within a given number of years. Given additional local specific information, the model can predict costs and quality of life.
The Reference Model is not a single model; instead it is a league of models that compete amongst themselves to gain the best fit to known clinical trial results. In a sense, this is similar to asking the opinions of multiple specialists about a disease and figuring out who is the best specialist for which conditions. The Reference Model is therefore more than a forecast tool, it is a tool that allows us to gage our understanding of disease progression by comparing multiple models and populations.
The Reference Model for disease progression has rapidly advanced since its first launch in 2012.
The first important advancement is visually showing model and population fitness information in a color coded matrix form. This fitness matrix allows comparing the behavior of multiple models with multiple populations. This is actually the main idea behind the model.
Another important advancement was demonstrating that disease models become outdated and need correction/update for treatment improvement in time. This shows the necessity in a new generations of models that can cope with recent and future changes.
Another advance is demonstrating the uncertainty associated with unknown correlations of biomarkers. This is important since it shows how much model results can be believed considering hidden unknowns/assumptions in the population inputs. After all the inputs for the model are all publicly available data. More specifically, the populations are reconstructed from summary data of clinical trial results that are freely available in the literature. Using publicly available data increases the information base of the model.
The Reference Model is not a perfect solution, there is still a lot of knowledge missing and many assumptions are made. Yet it is a good way to cross reference information to find out pieces of information and assumptions that fit together, and allow competition against known accumulated data to guide our perception. This is preferable in many cases to expert opinion since using the model the opinion can cross referenced and tested against evidence.
High Performance Computing is used to cross reference all this information. The MIcro Simulation Tool (MIST) is free software used to implement the model and run it. MIST can run over the cloud! More information about MIST in my next post. For more information or questions, please contact me at https://sites.google.com/site/jacobbarhak/.
By Caitlin Rothermel, MA, MPH
Recently, Patti Peeples at HealthEconomics.Com interviewed me for a podcast on health economics writing. You can listen to it here (and I hope you will), but after it was over I realized I had a bit more to say about modeling, writing, and transparency.
First off, I really like writing health economics research. When studies are well designed, it is a pleasure to explain researchers’ assumptions, model design, and study outcomes. It is like putting together a very attractive puzzle, one that’s just complex enough to hold your interest all the way through.
That said, some of us are good at building research (that’s probably you, reader), and some of us are good at organizing and presenting it (that’s me). I think that one of the biggest concerns today in health economics communications is creating and presenting genuinely transparent cost models. And of course, developing a transparent model is only the first step in this process – it has to maintain transparency and make sense to a range of readers when it’s summarized in print.
Recently, I read an interesting presentation from a 2011 Drug Information Association workshop. Medina and colleagues surveyed payers (n=12, primarily pharmacy directors) and pharmaceutical manufacturers (n=13) to evaluate the relative usefulness and importance of dossier submissions’ clinical and economic sections. This research focused on Academy of Managed Care Pharmacy dossiers, but its take-home is key for any publication type: “Payers felt that [economic] models were only somewhat transparent, whereas manufacturers believed their models were transparent to most, if not all, health systems.” In addition, payers thought most models were not clear and/or easy enough to use.
It might be fair to argue that part of the problem is that pharmacy directors are not versed in health economics. There may be a genuine need for more education in this area. But this justification isn’t much consolation when your hard work is thrown in the trash because the target audience just doesn’t get it.
I’ve picked up some good tips from listening to experts and payers. Here’s the first: As much as possible, make sure your model design reflects the real world. Transparency doesn’t mean very much if a model’s steps or transitions don’t match the actual course of disease progression or what would be considered standard practice in clinical care. For example, when modeling a chronic disease, think twice about using an approach that assumes patients will remain on the same treatment for decades or even a lifetime. A hallmark of chronic disease is its progressive nature and the fact that treatment is constantly updated.
Don’t torture the data just because you want to present outcomes in a certain way. The harder you have to work to make your point, the more confusing your explanations are likely to become. You’re digging a hole for yourself, and readers don’t necessarily want to follow you down that hole. Think of it this way – most of us are exposed regularly to tweaked budgets of some sort, fudged or failed projections, or estimates once considered accurate and now recognized as horribly off. Educated readers are increasingly savvy to potential distortions, and are likely to overlook monetary details or calculations that don’t make reasonable sense.
Another way to look at your writing is to consider what turns readers off, and do what you can to avoid it. When people become confused by a document’s content, they tend to put it down and not pick it up again. I’ve known people who refused to continue reading a document with the inappropriate use of hyphenation. Here’s something I’ve seen in health economics – not providing recommended information because it is considered intuitively apparent. For example, what is the point in explaining why a model covers only a specific time frame, when this is readily apparent from context? In this case, imagine an in-hospital treatment where all side effects will show up within one year or less. It’s generally best to insert a sentence stating the reason for the decision. Yes, it may seem simplistic, and your intention may be evident to 90% of readers. But what about the other 10% who simply stop in their tracks and don’t go further when they reach a point of non-understanding?
Of course, a consistent barrier to writing up health economics research is having too much to say. It is always going to be challenging to communicate health economics data in existing journal length format. In general, the word count for original research in a medical journal (whether it’s a traditional journal or one focused on health economics) is 3,500 or 4,000 words. A few publications, like Health Economics, allow up to 5,000 words, but they are the exception and not the rule.
For the writer, this means you need to be concise and to the point. If you can’t achieve all of your goals in the space allotted, take advantage of the online appendices that many journals offer (I would especially encourage using appendices to provide information on study assumptions). If this is not possible, you’ll have to work harder to streamline the content you are able to present. Don’t make non-intuitive leaps or leave gaps in your model explanation just because space is tight. If you find yourself in a situation where you run out of room to summarize your model methods or results and are thinking of cutting back, ask yourself honestly if you’ve padded your Introduction and Discussion sections. Remember, good opening and closing arguments for your study’s relevance will only go so far if the reader is not convinced that your research strategy or results are valid.
One way to make sure your document is concise and complete from the first draft on is to outline it first, based on the sections of the document. To get an idea of the word count you are headed towards, take a look at the word count of your outline; mentally add on the amount of additional content that will be needed to fully flesh it out. Stay within the limits you set.
This gives me a chance to make a plug for having a health economics-oriented medical writer help you out, either at the beginning of your project, or at least before final submission. Medical writers are all about attention to detail, looking for gaps, and making sure the content we’ve been given matches the publishers’ specifications. Even if you can’t afford a professional writer, accept that there will typically be gaps in your documents that you will not be able to see. Prior to submitting your document, have someone who you trust review it closely. Choose someone quantitatively oriented who is willing to look at your model from the roots up, and who will let you know if there are points where their understanding falls off.
The work health economists do is complicated and meticulous. If your writing is clear, even non-professionals should be able to review and basically grasp your studies’ goals, implications, and key findings. Bottom line – strive for transparency, clarity, and simplicity. When writing achieves these goals, it not only facilitates the accurate communication of information, it builds trust between stakeholders.
Caitlin Rothermel, MA, MPH is a medical and health economics writer. She lives in Vashon, WA with her family. You can learn more about Caitlin by visiting http://www.MedLitera.com.
Do you wonder what is going on within the job market for pharmaceutical Health Economics and Outcomes Research (HEOR), and Pricing, Reimbursement, Market Access (PRMA) from either a job-seeker’s perspective or a hiring manager’s viewpoint? Do you wonder how best to prepare for a Career Fair? To answer those questions, I interviewed Michael Litten and Scott Kabo of Klein Hersh (KH), a strategic leadership placement organization with a division focused on HEOR, PRMA, and the fast-growing Data Analytics and Informatics areas. Listen to the Podcast here, as part of our HealthEconomics.Com CONNECTED COMMUNITY Podcast Series. This blog posting is a summary of the Podcast.
On January 23, 2014, HealthEconomics.Com is hosting the first ever virtual Career Fair focused on HEOR, PRMA, and Data Analytics in HE-Xpo, a virtual on-line tradeshow and marketplace. Pre-register here for this event, and to be informed about other virtual HEOR-focused educational conferences and events. On January 23rd, between 9am-1pm, log-in from your computer, tablet or smartphone, and see what is in store for your career in 2014! You’ll be able to visit the Exhibit Hall and interact LIVE with exhibitors (pharmaceutical companies, consulting organizations, universities, CROs, and more), as well as visit the Learning Center to get Job Search tips. Spend time in the Lounge to network with others one-on-one and to connect to all those companies via social media! It’s a truly unique experience, and accessible to everyone, whether your are checking in from your home, office, or even in an airplane!
Let’s get started on our interview with Mike and Scott of Klein Hersh Recruiting.
[Patti] At the January 23rd Virtual HEOR Career Fair, we have many companies who are recruiting, including Boehringer Ingelheim, Covance, Evidera, Carrot Pharma, Klein Hersh, University of Florida, Thomas Jefferson University, PRMA, and more. What three pieces of advice do you offer to a Job Seeker as they prepare for a Career Fair?
[KH] In every job search, job seekers should focus on:
2. Due Diligence; and,
3. Creating an agenda with goals to accomplish during the Career Fair.
It is important to know the specific companies that will be present at the Career Fair, and in advance of the event, research the job openings and the individual position description. Know what company and job you will visit first, second, and so on. It is recommended that the candidate prepare a specific script targeted toward each job, explaining why he/she would be a great fit for that advertised position and the company as a whole. Kabo emphasizes that the candidate should be strategic in their efforts during the career fair, and if they aren’t sure what the company is looking for, to be prepared with “qualifying questions” that help elicit specific traits, skills, and experience that the company desires in the person who will fill that role. He suggests conducting due diligence on the company by searching the corporate website, reviewing press releases and announcements, and familiarizing oneself with the product portfolio and strategic focus. “The art of an interview is your ability to ask questions and engage in dialogue,” emphasizes Kabo. Moreover, even at a virtual career fair, all candidates should come to the interview prepared with a list of questions that help him or her understand the company, its culture, the specific role, and the skills necessary to succeed.
[Patti] How should an Employer prepare for a Career Fair?
[KH] Michael Litten notes that some of same advice given to candidates embarking on a Career Fair also applies to employers: namely, Preparation, Preparation, Preparation. Employers should be fully capable of discussing the corporate goals, areas of focus, and uniqueness. They should also be prepared to field common questions that candidates in the field of HEOR would ask about the specific job. Interviewing goes both ways, particularly in high-demand areas like HEOR and Pricing, Reimbursement, and Market Access. All human resources personnel should know what makes their company “sizzle” and be prepared for delivering a pitch that would make someone want to make the leap from their present company to a new place of employment.
[Patti] This is a virtual Career Fair. How should employers and job seekers specifically prepare for this on-line experience?
[KH] In terms of the upcoming Virtual Career Fair on January 23, Litten and Kabo encourage everyone to participate because – as they say – there really are no barriers to one attending in terms of location, travel, cost, etc. This Virtual Career Fair for Health Economics & Outcomes Research is incredibly unique and a fantastic idea. There are, however, a few things both a candidate and an employer should remember about this on-line experience. Litten believes that the virtual experience has some commonalities with a telephone interview. Both the employer and the candidate should be prepared to quickly and efficiently steer the conversation toward a specific role and to be able to say a few words that highlight the skill set needed or that the candidate possesses, up front. Because this is a virtual environment typing this information, so be prepared for this. Also, be ready to migrate through the Exhibit Hall to visit as many companies as possible, and make sure to get individual follow-up information with email addresses so you can establish that relationship outside of the Career Fair.
[Patti] What is the most common mistake you have observed in the interviewing process?
[KH] Litten notes that time delays, including drawn-out scheduling and/or decision-making in the job search process by the hiring company, is the biggest challenge to both the employer and the job seeker. If the process goes stagnant, the candidate will move onto another opportunity. Klein Hersh establishes deep relationships with both the hiring company and the candidates and seeks for transparency on behalf of both parties, in terms of interest in the candidate or interest in taking the position. Occasionally, a candidate may be uncomfortable stating that they are not interested in a position, but ultimately, this honesty works best for all parties involved. Additionally, it is important that the company clearly state if they desire a specific type of skill set. Likewise, the candidate needs to be transparent about what skills they possess and the areas in which they are less comfortable. Another common fallacy is for the candidate to play their cards “close to the vest”. Kabo notes that candidates should clearly communicate their enthusiasm for the position and the company, if it exists. The last thing one wants is for the company to have a debrief after an interview and note, “we really liked the candidate’s technical capabilities, but we just didn’t get the sense they were interested in us!” If you like each other, let it be known!
[Patti] How can a resume stand out in today’s marketplace?
[KH] Klein Hersh does a great deal of counseling on resume structure. The most essential thing to remember is that people are busy, so conciseness is essential. Your entire work career will likely not be reviewed job by job, bullet point by bullet point. Litten strongly suggests including an “Expertise or Overall Summary of Profile” statement at the top of the resume, including relevant skill sets and today’s buzzwords. He also notes that, in today’s environment, it is increasingly common for candidates to hold a series of short-term consulting jobs, often moving from one company to another in short-term contract positions. If this is the case, it is essential to note that these moves were intentional and part of an organized process of consulting, and to note this at the top of the resume. This immediately addresses the concern over the candidate being a “job-hopper”, and instead positions the candidate as someone who has chosen a particular tact to their career focused intentionally on short-term contracting positions. This is acceptable, but an explanation at the top of the resume should indicate this approach.
[Patti] In today’s marketplace, how is loyalty judged? What’s the shortest acceptable time to stay at a company, and what is a good duration in a job or with a company?
[KH] While it is uncommon in today’s marketplace to stay with a company for 25 years and receive that retirement party and a gold watch, it is important for individuals to have reasonable tenure in their positions. Litten suggests that 1 year is the minimum time in a job, and preferably two years. If an individual is with a company for 3-5 years, even better. But, as noted before, if an individual has held a series of multi-month or year-long contract positions, this is acceptable as long as it is explained within the resume.
[Patti] Do you have any closing thoughts on the Job Market for individuals in the healthcare value area?
[KH] This area of HEOR and PRMA, as well as Data Analytics, continues to be a fast-growing area. Make sure you, as a candidate, really know and understand the company where you are applying and be prepared to speak cogently about the organization’s mission and focus, the specific position, and what makes you stand out as that one great candidate for that particular job. From an employer’s perspective, the job market is tight and skilled candidates have many options. Be prepared to really sell the company as well as be able to speak to the commitment to the function of HEOR. Employers should describe the exciting future that the company can provide to the candidate. And lastly, it’s essential to be honest and transparent with each other and with your Executive Recruiter.
[Patti] Thank you to Michael Litten and Scott Kabo, Executive Recruiters with Klein Hersh, for their insights into the HEOR & PRMA Jobs Market. Klein Hersh will be exhibiting at the 1st-ever HEOR Virtual Career Fair in HE-Xpo, sponsored by HealthEconomics.Com, scheduled for Thursday, January 23, 2014, 9am-1pm ET. Pre-register here or, you can register on the day of the event with a few clicks at the same link. Log-on from your computer, tablet or smart-phone. If you are a hiring company and are interested in exhibiting or recruiting at the Career Fair, or participating in future HE-Xpo events, contact Leslie Fine, HE-Xpo Marketing Manager at email@example.com.
If you’ve been following the health economics industry for the past few years, you’ve heard about value-based pricing agreements between payers and the pharmaceutical industry. With value-based approaches, payment is linked directly to real-world treatment effectiveness. In this way, purchasers’ and manufacturers’ economic incentives are aligned based on clear, mutually defined patient outcomes.
Once or twice a year, I look around to see if anything major has changed in this area – Is the chatter on value-based agreements getting louder? Are value-based approaches becoming more common? My take on the current environment is that stakeholders are talking about value-based approaches more than ever, but their actual, successful implementation is stalling.
In a recent blog on payer value messaging strategies, HealthEconomics.com’s Patti Peeples indicated that a shift is clearly underway “towards more flexible risk-sharing and cost management mechanisms from the payer’s viewpoint.” But she proceeds to argue that substantial misperceptions persist regarding what payers actually want from pharma versus what pharma believes payers want. The ultimate problem? A lack of effective communication between the players.
In a 2011 case-based investigation published in Health Affairs, Peter Neumann and colleagues found very few examples of successful risk-sharing agreements, and noted that U.S. stakeholders continue to focus primarily on payment models not connected to performance assessment or data analytics. They: “The principal lesson thus far seems to be that risk sharing for pharmaceuticals is appealing in theory, but hard in practice.” The primary barriers identified? High implementation costs, lack of a data infrastructure, and challenges with outcomes selection.
To be fair, this issue is multifaceted and doesn’t have easy solutions. But I think a way exists to circumvent at least one key obstacle – the data infrastructure gap. According to Neumann et al: “Risk-sharing agreements require high-quality information systems, databases, and operational expertise.” It takes time to put these capacities in place, but there may be a workaround.
But before going any further, it’s important to understand that the way value is measured is undergoing a sea change.
The Old Ways of Measuring Value Are Changing
It could be said that value (treatment outcomes adjusted for treatment costs) is the newest currency in healthcare. But to be useful, value needs to be defined, measured, predicted, and optimized. Thanks in large part to novel study designs and the availability of advanced analytics, our notion of what aspects of value can be measured is evolving rapidly.
A poster presentation from Dinh and colleagues at the 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR) conference outlines some key distinctions between past and future approaches to value strategy development.
The old way is reactive.
It does not consider value until late in the drug development process. This approach:
The new way is proactive.
It applies value prediction in early drug development (for example, by identifying “most valuable” subpopulations using data-based methods). This approach:
Getting Around the Information Technology Barrier
A 2012 Deloitte Center for Healthcare Solutions Issue Brief summarizes the information technology aspect of the value analytics gap as follows: “The availability of valid-real-time data for value metric assessments requires widespread electronic exchange of health information among stakeholders and disparate sources…this extensive network needs to be further built as it is not currently widespread.”
In some cases, the pharmaceutical industry and payers are teaming to overcome these data limitations. A collaboration between AstraZeneca and WellPoint, established in 2011, is using electronic medical records, claims information, and patient survey data to evaluate how currently available drugs affect healthcare costs and patient outcomes, particularly in chronic disease.
There are also some electronically enabled tools that are readily available to assess cost-effectiveness. Retrospective database analyses have the virtue of very large samples, substantial data inputs, and access to “real-world” patients. Interactive models simulate future outcomes and costs associated with specific products, and can be designed to reflect specific payers’ resource utilization patterns.
But these approaches have limitations when assessing the clinical and economic impact of new treatments. First, there’s always missing information – claims records don’t necessarily contain the biomarker and lab data needed to assess treatment efficacy, and interactive models are limited in scope. Also, in every case, someone has to take on the work and expense associated with designing and running new studies or building new models. And of course, if a treatment is not already on the market, time needs to pass before you can obtain useful information from retrospective claims data.
I think there is a way to bridge this gap. The data that players need can be augmented by incorporating accurately simulated data, using a tool that’s available today.
A Way To Look Data From Many Sides
When only limited real-world data are available, or the data doesn’t allow for the answering of specific questions, the judicious integration of simulated data can help payers and pharma work together to build a case.
Simulation models can answer the big “what if” questions that are cost-prohibitive or can’t be evaluated with traditional research. For example, what if you had a new dyslipidemia treatment, but had trouble discerning its efficacy against the background noise created by other cholesterol-lowering medications? Models enable you to run unlimited simulations of patient populations with user-defined characteristics.
The Archimedes Model is a large-scale, clinically realistic model of health and healthcare delivery. It contains detailed patient information obtained from public databases, clinical trials, observational studies, and epidemiologic data; this lets users conduct research on the general U.S. population or subpopulations. The Model also integrates with proprietary data, for example, claims or electronic health records containing biomarkers or clinical practice processes.
ARCHeS, the Model’s intuitive, online platform provides a natural venue for collaboration and value strategy development. The results of a recent Quintiles survey show that payers want more involvement during every stage of drug development. With ARCHeS, manufacturers and payers can sit down together (literally or virtually) at any point to “slice and dice” their customized data. ARCHeS can be used in two ways:
- With ARCHeS Population Explorer, users can model the impact of a new treatment on multiple populations (either the U.S. population or defined subpopulations, or users can upload their own patient data).
- With ARCHeS Trial Designer, users can set up virtual clinical trials for new products, compare the results for accuracy, and then set up new virtual trials to gain additional information—all within a few hours.
This is a new approach, and it could make it possible to examine some big economic and clinical questions early in the game:
- What cost savings are likely to be associated with a treatment, and what is the financial hit if any key assumptions are incorrect?
- What downstream effects, both positive and negative, are likely to be associated with a treatment?
When information like this is available up front, it becomes possible for stakeholders to negotiate based on accurate answers to “what if?” statements. For example, at what price point does an intervention become effective? What downstream effects can be expected to accompany a 9 mm Hg drop in blood pressure? What is the cost-benefit relationship of screening select subpopulations for certain cancers?
The next big experiment I’d like to see in value-based pricing involves the Archimedes Model and ARCHeS. With ARCHeS, you can integrate various types of data, hypothesize, add missing information, and receive answers to the questions that will help determine the specific value of an intervention in a particular population. The tools are there, just waiting to be used – and innovation has never been easier or more straightforward.
Caitlin Rothermel, MA, MPHc is a Northwest Washington-based medical and health economics writer. She has paid close attention to the Archimedes Model for years and gets completely geeked-out happy by Bayesian hierarchical modeling. You can learn more about Caitlin by visiting www.MedLitera.com.
Paid advertorial by Archimedes, Inc.
Maintaining high levels of patient recruitment, adherence and retention is essential for the successful completion of a clinical trial, yet it remains a significant challenge faced by researchers. Poor patient adherence and retention can adversely affect a trial by lengthening timelines, adding cost and risk to the validity of the data and delaying product approval.
There are numerous factors which contribute to the slow process of attracting the patient to consider participating in clinical research studies. These include lack of awareness of the critical role that clinical trials play, limited access to knowledge about clinical trials, lack of understanding about rights, safety and benefits and which are among the most common reasons for low participation. In terms of retention in the trial these include (i) patient-centred factors: such as demographic and psychosocial including beliefs, attitudes and motivation, severity; (ii) therapy-related factors: such as treatment complexity adverse reactions, lack of therapeutic impact; (iii) social and economic factors : including inability to take time off works, lifestyle patterns etc. (iii) Other factors: including clinical trial site location, frequency of clinic visits, clinic staff etc.
Development and integration of patient retention strategies that address the issues of patient perceived benefits, barriers and burden by leveraging both technology and communication is essential for addressing patient dropout. However, traditional thinking would have us believe that an individual’s decision to stay in or drop out of a clinical trial is based on the patient acting rationally.
At DHP Research we are bringing to our clients an approach which to quote Jules Berry “…shines a light on the factors that influence our actions” in an attempt to understand more the subtle and complex mechanisms that influences patient behaviour whether to stay or pullout of a clinical trial. It is to paraphrase Jules Berry, viewing the solutions through the prism of behavioural economics.
What is behavioural economics (BE)?
BE is a multi-factor approach to the understanding of human behaviour that represents a paradigm shift in the traditional thinking that our behaviour is rational. Rather the principles of BE are that our behaviour is to a large extent unconscious, irrational and socially driven. Therefore, for any clinical trial retention strategy to be truly patient-centric it must take account of these influences on patient behaviour throughout the different phases of a clinical trial.
The key characteristics of human behaviour outlined in BE are:
- Personal factors: We don’t like change, we live in the here and now, we are averse to loss, we want a positive and consistent self-image
- Social factors: We are heavily influenced by others
- Local and choice environment: The environment matters, it’s hard work to think and choices are guided by salience of information and mental shortcuts
Lets illustrate how these might apply in a real world setting.
Personal factors: We think short term and avoid loss rather than achieve gain (loss-aversion). In other words we feel loss more keenly than gain. For example, offering an amount of points at the start of an exercise programme which could be exchanged for items or money on programme completion – but would be withdrawn for failure to adhere to aspects of the programme – is likely to be more effective in achieving adherence than accumulating points from zero.
Social factors: In addition to our need to retain a positive self-image, individual decision making is heavily influenced by others – the power of the messenger – rather than the message itself. It is certainly true our behaviour can be be influenced by experts and authority, but the people like us (our peers) have tremendous influence over what we do.
Knowing which groups of people are likely to be be influential is a key question in developing a patient retention strategy and one that only research can address but, patient groups and their communications for example through social media and mobile phone apps could have a significant influence on trial participants to stay in.
Choice environment factors: When it comes to making a decision or choice as humans we don’t like to think too much. We can’t attend to and process all available information. Too much information, too many messages leaves us unable to cope and our system 1 thinking – perceptual, intuitive, influenced by emotion – kicks in to help us make decisions on judgements that come easily to mind.
When it comes to choice things that come to mind easily are considered important and decisions are often influenced by the salience of the information readily available. For example, we are less likely to participate in a clinical trial because we know someone who had a bad experience and withdrew. This is known as as the availability heuristic which results in the individual giving too much emphasis to small probabilities.
As individuals we priorities information that supports our existing beliefs and will filter information that supports those beliefs. Related to this is anchoring and occurs when we are presented with a piece of information we then use as an anchor for all subsequent information. For example, if patients are told that in previous studies that chances of adverse reactions was 8% this will serve as an anchor for the expected adverse reactions in the current study whether this is high or low.
Information such as this can be communicated in different ways and which can have a profound effect on choice For example, at a trial recruitment stage patients are told there’s a one in 25 chance of having an adverse reaction it’s more than likely the majority of patients will think this as extremely risky. However, informing patients the treatment is 96% reliable will more than likely be considered as very safe. Neither of these values is strictly “the truth” but they provide a perspective which is known as framing where the salience of information is created by presenting the more positive side of the problem.
Salience can also effect what we remember which is usually shaped not by the average way we felt about an experience but, rather at the peak and end of the experience. We use a mental shortcut to remember the most salient aspect of the experience. For example, having met interesting people at a party might be the salient thought about the party and will have significant impact on your choice to accept another invitation from the host. For trial participants this would not be the overall experience of participating in the trial to that point that comes to mind, but for example, how they were made to feel the last time they attended the trial site or experienced an adverse reaction.
The effect of salience can be seen in our everyday lives such as the most popular/expensive coffee and tea placed on the front of shelves, chocolate and crisps next to the till.
Ensuring trial participants have a positive experience at the trial site is critical for ongoing patient participation not only for increasing the salience of that positive experience for example through 1 to 1 discussions with patients about their well-being and attitudes and experiences of participating in the trial but, also as an opportunity to reinforce the salience of positive aspects of the trial.
This post has described just some of the key aspects to illustrate how BE can provide an added dimension to understanding behaviour. Through the careful assessment of these behavioural factors using different research techniques we can build a framework that helps us explain how these factors relate to behaviour in different situations. There are however, questions still to be answered such as the importance of each factor. Do the factors work together? How do the factors work at the individual level? Are the factors equally important for different disease groups and trial? Yet despite this and as yet not being able to make accurate predictions, behavioural economics does have the potential to be a real game changer in understanding some of the subtle and complex mechanisms that influences patient participation in clinical trials.
We hope to present more on this topic at the DIA 2014 Annual Meeting, June 15-19, San Diego, CA
For more information or to discuss this blog contact us at firstname.lastname@example.org
Are traditional conferences and trade shows dead? That thought probably has Conference Companies quaking in their well-traveled shoes, while industry execs are drumming on their laptops with glee at the thought of never having to go to another 2-day meeting in a Marriott ever again. But maybe it’s time to re-think conferences and how we share ideas. Let’s explore that for the HEOR and Managed Markets audience for a moment.
There are dozens of conferences held each month. These include major association meetings, like ISPOR, AMCP, DIA, ISPE, etc. as well as for-profit conference companies sponsoring events like CBI, eyeforpharma, IIR, marcusevans, and more. Just look at the conference calendar on HealthEconomics.Com for a peek at the sheer number of events on pricing, reimbursement, ACOs, PROs, and informatics. Many industry watchers bemoan the state of conferences in our field, complaining of low attendance, repetitive presentations by the same individuals hawking their wares, as well as the expense associated with registrations, airline and hotel stays. This doesn’t include the biggest expense, which is time away from the office with limited ROI. In fact, many government organizations in the United States forbid all but the most mission-critical conferences if they are further than a Metro ride away. These restrictions are in addition to an Office of Management and Budget memo promoting efficient agency travel spending, disseminated in May 2012.
Please take a Quick Poll about your company’s recent policies on conference attendance. When answering, consider whether your company has implemented policy changes, levels of approval, per diem spending limits, or recommended limiting travel/attendance in any way.
Of course, live conferences may offer the possibility of networking with individuals outside your own company, and occasionally hearing a nugget that might be the genesis of a big new idea or a re-thinking of an old approach. Nevertheless, forward-thinking executives have been wondering: How can we retain the benefits of conferences and trade shows, while tossing out the negatives?
One word: Virtual.
What does that have to do with HEOR and Managed Markets? One more word: HE-Xpo. HE-Xpo is a virtual conference, trade show and market place, just for the health economics, outcomes research, and managed markets community – worldwide.
Growth of Virtual Conferences & Tradeshows
More about HE-Xpo is below…but first, let’s back up. What exactly is a Virtual Conference and Trade Show and what is happening in this arena? According to a leading market research and technology research company, Market Research Media, the worldwide virtual conference market is forecasted to grow at a compound annual growth rate (CAGR) of 56% between the period of 2013 and 2018. This prediction was contained in their recent research report “Virtual Conference & Trade Show Market Forecast 2013-2018“. The worldwide virtual conference and trade show market was predicted to reach $18.6 Billion over the period 2013 – 2018.
The image below, derived from Market Research Media, shows a Venn diagram of sorts describing Virtual Conferences & Trade Show components.
In a recent Reader Satisfaction Survey by HealthEconomics.Com conducted April 2013, “Virtual Conferences” was the most requested new service by the >200 survey participants. As a result, we answered your call. HealthEconomics.Com has launched a new service, HE-Xpo, a virtual conference, trade show, and market place available 24 hours a day, 7 days a week, 365 days a year. HE-Xpo allows you to feature your company, products, and services in a media-rich, interactive environment that is SEO enhanced and offers powerful lead generation through real-time attendee profiling. HE-Xpo has Exhibit Booths, a Learning Center, an Auditorium, and a Lounge. You can create a compelling user experience by hosting live webinars or streaming a conference live in the Auditorium, uploading unlimited media to your exhibit booth and the HE-XPo Learning Center, including video, audio, images, PowerPoint slides, spreadsheets, PDFs and more, as well as network and share via social media (Twitter, Facebook, LinkedIn, Google+, more) in the Lounge. For a full description and price list, view our HE-XPo Media Pak.
HE-Xpo is going live this month (July 2013), with many HEOR company booths in the Exhibit Center and many items already uploaded into the Learning Center. Several industry associations are considering using HE-Xpo to live stream their annual meetings to expand involvement and access to those individuals in HEOR and Managed Markets who cannot get to the live meeting. Virtual attendance is the next best thing, and it may be even better (from an ROI) than attending in person. Contact Leslie Fine, HE-Xpo Marketing Manager, at email@example.com , to reserve your exhibit booth and get involved in the next big technology for the industry: virtual conferences and tradeshows.
HE-Xpo Grand Opening and Launch Party – September 19, 2013
Mark your calendars for the HE-Xpo Grand Opening & Launch Party, scheduled for September 19, 2013. Our HE-XPo Grand Opening is a day-long event, full of live presentations, exhibitor give-ways, learning opportunities, networking, and powerful lead-generation opportunities. We want you to be present and participate. HE-Xpo gives you all the next generation tools you need to showcase your product or service. We want to help you turn your booth attendees into customers and getting your booth ready and active is a snap. You are literally minutes away from having a live and accessible booth, available 365 days a year, 24 hours a day because it is extremely easy to set up. And, our experts are here to assist you in any way possible so that you can begin to see the benefits from your participation as soon as possible.
We can’t wait to have you join us with this special offer! If you are ready to raise the quality of your digital marketing experience to new heights, please contact Leslie@healtheconomics.com to get started.
Collecting information using a questionnaire to measure patient satisfaction, experience and health is now common practice. There is a variety of health questionnaire types that can be used. Whichever way questionnaires are used and for whatever purpose, the objective is to obtain reliable and valid information on the patient’s experience and reported outcomes. Below are some pointers as to why NHS patient experience surveys can fail in providing useful information.
- Not understanding the big picture: It is essential that the overall objectives of the patient experience survey are defined at the outset (the research question). This will include establishing the purpose of the survey e.g. measuring patient satisfaction, experience and outcomes, clarifying the target population the health questionnaire will be administered to e.g. patient group, disease type, how the information will be collected e.g. paper/pencil, interview, web and how that information will be used e.g. improve patient experience.
- Using inappropriate data collection methodology: It is obvious but, telephone surveys are inappropriate for the hard of hearing and elderly. If you are using a postal survey how reliable is your data source? What is the literacy level of your target population?
- Choosing the wrong question type: Choosing the correct type of question for your health survey will involve making decisions such as whether to use an open or closed question, a ‘don’t know’ response option, rating scales or grids etc. Remember complexity leads to non-response.
- The questions are lengthy and difficult to understand: Remember respondents need to understand what the question is asking to give you the correct information. A well crafted questions needs to be no longer than 20 words, should be written using plain and simple language. The question should ask one question e.g. How would you rate the receptionist’s helpfulness? NOT How would you rate the receptionist’s and doctor’s helpfulness?
- The survey questions are not relevant: Survey questions must be relevant and specific to the target population. When developing a new patient health questionnaire patient input is essential to ensure content validity. If using an existing questionnaire then establish content validity via some focus groups.
- Not pre-testing the questionnaire: Pre-testing the patient questionnaire can highlight any problems with it, including length, understanding, missing questions etc. Pre-tests can be carried out on a small sample of the target population.
- Getting a low response rates: Response rates are critical to the success of a patient experience survey and with some thought and planning can be as high as 70%. This includes, a good introduction letter to the patient explaining the survey and confidentiality, a stamped addressed envelope if a postal survey and reminder letters.
For more information on patient experience questionnaire design download the 7 Tips you need to know for successful questionnaire design
Dr Keith Meadows is founder of DHP Research & Consultancy specialising in the measurement of patient reported outcomes and experience.
A key hurdle facing the entire pharmaceutical industry is non-adherence by patients to medication. This problem is only likely to be surmounted if patients believe that taking medication will lead to immediate benefits through reduction of symptoms, improvement in physiological functioning and quality of life.
The diabetes mellitus (DM) marketplace, for example, is becoming saturated with multiple medications in both the insulin and pre-insulin space, particularly as analogues start to lose their patents. Differentiation in clinical outcomes within classes is often unclear or minimal. This means that differentiation of therapeutic options is likely to focus more on frequency or mode and method of administration, as opposed to statistically significant differences in glucose control, which are clinically relevant.
What is a measurement strategy?
An effective way of establishing the link between the measured outcome, such as the patient’s health status or quality of life following an intervention programme, is the development of a measurement strategy which requires a clear understanding of the disease and the relevant primary outcomes (e.g. reduced hypoglycemia and secondary outcomes e.g. reduced anxiety).
A patient-reported outcomes (PRO) measurement strategy provides a framework to support the selection of an appropriate PRO for a clinical trial through which treatment effectiveness in terms of health status or quality of life for example, can be demonstrated in relation to the desired primary outcomes.
Components of each of the key stages of the strategy are shown below. This strategy makes explicit the expected treatment effects (e.g. primary biomedical endpoint(s)) AND IMPORTANTLY the secondary endpoints such as reduced anxiety, to be measured by the PRO.
A critical aspect of the measurement strategy is selecting the appropriate PRO that captures the benefits of the primary physiological endpoint of treatment i.e. secondary endpoint(s). However, outcome teams are frequently faced with a plethora of potential PROs each purporting to measure – often without a sound theoretical or measurement model – specific health constructs such as health status or quality of life. As a consequence the choice of a PRO is often made according to:
- the instrument having been used in previous studies
- its name appears to be appropriate for the intended use
- The supporting psychometric data looks o.k.
Furthermore, there is the tendency for those conducting clinical trials to treat the more commonly measured health constructs such as quality of life (QoL), health-related quality of life (HRQoL) and health status as interchangeable in the PRO selection process which they are not. Examples of this include the SF-36 and EQ-5D which are frequently referred to as indicators of QoL, but in fact are more indicators of health status, which of course can impact on the individual’s QoL. Health status is a measure of the quality of health yet while there is no universally accepted definition of QoL, there is the general consensus that it is based on the individual’s subjective evaluation of the psychological and social aspects of their life including work, school and family.
As described above, essential to selecting the appropriate PRO is to make explicit the expected treatment effects e.g. primary biomedical endpoint(s) and the resulting secondary endpoint(s) which should be articulated through the endpoint model from which the most appropriate PRO can be selected. For an example of a simple endpoint model see below.
In this simple model, the objective is to reduce recurrent hypoglycemia which – as the primary endpoint – will be a reduction in the various effects of hypoglycemia including sweating, shaking. etc. Then, as a result, an improvement in the the desired secondary endpoint of the patient’s quality of life will occur. Clearly the model requires further expansion to include which elements of QoL are to be measured as well as health status. Having specified the secondary endpoints the appropriate PRO can then be selected. Central however, to selecting the PRO is the PROs conceptual framework.
What is a PRO conceptual framework?
The PROs conceptual framework shows the item content in relation to the specified concepts/domains the instrument is purported to measure. Therefore, if the PRO has been selected to measure aspects of sleep disturbance as a secondary endpoint, then there must be clear conceptual and psychometric evidence that the items of the PRO should relate directly to these specific construct. Below as an example is the conceptual framework of the Diabetes Health Profile -a PRO developed to assess the psychological and behavioural impact of living with diabetes that was derived on the basis of significant patient input and psychometric evidence.
Not all PROs make explicit their conceptual framework, but it’s worth bearing in mind that evidence 0f a conceptual framework is an essential requirement by the FDA for labeling claims
Selecting the most appropriate PRO to provide evidence of treatment effectiveness based on the patient’s perspective is a complex process requiring an explicit measurement strategy. This will include defining the primary endpoints and their relationship(s) with the desired secondary endpoints and linking these with conceptual framework of the selected PRO.
Dr Keith Meadows is Founder and Director of DHP Research and Consultancy Ltd. Keith has extensive experience in the field of patient reported outcome measurement with a particular emphasis on the psychological impact of living with diabetes.