As gamification grows, we look at five key considerations to employ when using this ever increasing approach to survey design.
A review of the recent Kantar Information is Beautiful Awards that includes key learnings and probing questions.
With the pace of business ever increasing, Jo Keeling looks at how technology can increase insight agility.
By Ross McKinnon and Talia S. Foster, M.S.
Biosimilars may offer lower-cost versions of existing biologic therapies – but a survey of Japan/Asia/Pacific (JAPAC) clinicians finds some confusion regarding differences between original biologics and biosimilars that may affect patients.
Biosimilars Resemble But Are Not Identical To Original Biologics
Generic drugs, which copy original small-molecule drugs, seek to provide exactly the same medicine at a lower price. Biosimilars, unlike generic small molecule drugs, differ from the original. For safe, effective therapy involving biosimilars, patients rely on doctors and pharmacists who may lack understanding of important issues around biosimilars.
Biosimilars may closely resemble their original biologics, but are not identical, because of differences in the way they are manufactured. Biologics are produced in living cells; the cell culture system used to produce a biosimilar, including downstream processing, usually differs from the one used to make the original biologic. Growth media used to feed the cells, and other aspects of their environment, might also vary slightly. These differences can influence a variety of factors, including safety and effectiveness.
Patients who use biologic products often have chronic diseases requiring lifelong therapy, and a longterm, stable response to that therapy. Their doctors and pharmacists serve as advisors and partners in choosing therapy that is likely to work and unlikely to cause harmful side effects. To make these decisions, clinicians must thoroughly understand the issues involved that could undermine safety. In the JAPAC region, biosimilars have become a common part of the treatment portfolio for many chronic diseases. But how well do clinicians understand the issues surrounding biosimilars?
Some Clinicians Lack Sufficient Knowledge Regarding Biosimilars
Not well, according to a survey of 670 physicians and pharmacists across JAPAC countries, including Australia, Japan, China/Hong Kong, New Zealand, Taiwan, Malaysia, South Korea, Vietnam and Singapore. Nearly half of those surveyed were rheumatologists, and the remainder were gastroenterologists, dermatologists or pharmacists. The average respondent had practiced for 18 years, mostly in a public hospital setting. On average, the surveyed physicians wrote about 20-25 prescriptions per month (81 in China), and pharmacists dispensed about 75.
Despite their experience, two-thirds of respondents reported feeling not knowledgeable or somewhat knowledgeable about biosimilars. Fewer than five percent felt very knowledgeable. Even in China, with its high volume and longer history of biosimilar use, only seven percent felt very knowledgeable about biosimilars.
Many clinicians did recognize that biosimilars closely resembled the original biologics on which they are based but were not exact copies, and slightly more than half thought there might be differences in safety and efficacy. When asked to highlight differences, one quarter of respondents cited differences in structure and mechanism of action, and almost as many cited differences in manufacturing processes, or in their cost.
Of concern, 20% thought there was no or virtually no difference between biosimilars and original biologics. Many of these respondents also believed that clinical trials had shown their equivalence.
Physicians were much more likely than pharmacists to believe that safety and efficacy may vary between biosimilars and original biologics (22-25% vs. 8%). Dermatologists were especially likely to cite lack of evidence from clinical trials directly comparing their performance. Pharmacists, on the other hand, were more likely than physicians to emphasize cost differences (22% versus 10-21%).
Regardless of specialty, the great majority of respondents (89%) believed that biosimilars should undergo clinical studies like the original biologics to be licensed for each indication. Often they believed that this was necessary to ensure patient safety and efficacy.
Attitudes Toward Biosimilars Differ by Country
Major differences by country were clear from the survey results. Chinese clinicians were much more likely than their counterparts in other countries to believe that safety and efficacy would differ between a biosimilar and its originator biologic (85%). Indeed, they were more likely to identify safety and efficacy as the principal differences between them (63% in China versus 0-22% in other countries).
In contrast, no respondents from Singapore believed that safety and efficacy were the principal areas of difference, and 80% thought these would be the same. Singapore respondents also were much more likely than clinicians in other countries to believe that biosimilars and their originator biologics were identical or highly similar (50% in Singapore versus 9-32% in other countries).
Many Clinicians Accept Interchangeability but May Not Prescribe a Biosimilar
Given these viewpoints on biosimilars, how were respondents actually practising in relation to biosimilar prescribing or dispensing? Most considered biosimilars and original biologics as interchangeable. This was less true among dermatologists (55%) than other specialists (65-73%). It was also less true among respondents from Hong Kong (48%), Vietnam (53%), China (57%), and Taiwan (59%). Respondents from Singapore (90%), New Zealand (90%), Japan (78%), and Australia (76%) were much more likely to consider biosimilars interchangeable with the original biologic.
With little difference by specialty or country, respondents reported moderate approaches to the prescribing of biosimilars. Fewer than 5% believed they were very likely or very unlikely to prescribe a biosimilar. Most reported being moderately likely to prescribe a biosimilar if available, and to switch a patient from an originator biologic to a biosimilar. Pharmacists were more likely than physicians to switch, as were Chinese respondents compared to those in other countries.
While most respondents were moderately likely to prescribe a biosimilar, only a minority would start new patients on a biosimilar instead of the originator biologic. This did not vary much by specialty or country, with 37% of respondents willing to start a patient on a biosimilar.
Opinions Vary Regarding Naming of Biosimilars
When a physician writes a prescription, or a pharmacist dispenses it, the specified name of the drug may dictate what the patient takes home. While most respondents try to control this process by using both the scientific and brand names of a biologic (including a biosimilar), this practice varies by specialty and country. Pharmacists were somewhat more likely than physicians to use one or the other name rather than both. In Singapore and Malaysia, respondents were more likely to use only the brand name than to use either the scientific name or both names.
Whether the biosimilar should have the same scientific name as its originator biologic was controversial among respondents. Respondents in most countries were divided on this question. In China, respondents were much more likely to view scientific names as being very important (42% vs. 0-21% in other countries), especially for reporting adverse events.
Clinicians Want More Information About Biosimilars
Lower cost may be one of the primary reasons why health systems use biosimilars, so what leads clinicians to prescribe an original biologic instead? Many respondents viewed safety and efficacy as their primary reason for using an original biologic instead of its biosimilar. Information from clinical studies documenting such differences was also considered very important by respondents preferring an original biologic. Clinicians reported a preference for this information to be provided by various professional sources. Their first choice was conferences, followed by peer-reviewed journal articles.
As noted by the clinicians who responded to this survey, the potential for safety issues related to using a biosimilar instead of the original biologic underscores their need to understand the differences. Respondents did understand certain differences; they believed that biosimilars varied in structure and mechanism from originator biologics. They also thought biosimilars should be tested for safety and efficacy in each indication, just like the original biologics. Finally, they considered safety and efficacy as the primary reasons why a clinician would prescribe an original biologic instead of its biosimilar.
Despite this level of understanding, most respondents saw originator biologics and biosimilars as interchangeable. While they viewed the structure of the molecules as different, many did not view the need for different scientific names as particularly important.
Chinese clinicians use biologics much more than their counterparts in other countries, and have a longer history of using biosimilars. Perhaps as a result, they were outliers among respondents across the JAPAC region. They were more likely to switch patients to a biosimilar or start them on one, and less likely to see the need for biosimilars to have their own clinical studies. However, and somewhat in opposition to these views, they were also much more likely to see biosimilars as having different safety and efficacy as originator biologics, and did not view them as interchangeable. Any educational initiatives in China should consider the longer history and differing preferences for the use of these products in this country.
Many patients currently use biologics for long term treatment of chronic diseases, and many new biosimilars are being introduced in different markets. Therefore, the need for physicians and patients to fully understand all relevant safety considerations appears timely. Manufacturers of biologics, including biosimilars, can aid understanding by providing education to clinicians and patients. Continuing education related to biosimilars will promote the optimal use of all biologics in the patients receiving them.
Professor Ross McKinnon
Director and Professor in Cancer Research, Flinders Centre for Innovation in Cancer
Associate Dean Research, School of Medicine, Flinders University
Vice-President, International Pharmaceutical Federation
Talia S. Foster, M.S.
Director of Literature-Based Services, Truven Health Analytics
By Kevin Gray and Koen Pauwels
“In spite of our strong marketing support, sales of our brand are flagging. Why? What should we do?”
“If we launch this new product, what will it do to our bottom line? Will we just cannibalize our flagship brand?”
These are just two examples of questions marketers around the world ask themselves every day. Unfortunately, there are rarely simple answers and organizational politics and other factors, such as the state of the economy, also come into play, further complicating matters. While some marketing researchers seem to take for granted that marketing is now well-embedded in most companies and that the value of marketing research is universally accepted by marketers themselves, even in Western multinationals these assumptions are tenuous. In the words of one of our contacts, a marketer with extensive brand management experience at MNCs, “Marketing is regarded as fluff” even at many large corporations. The perception that the real work is done by production, sales and engineering is very common.
On the whole managers, marketers included, seem unprepared about how to fully leverage either data or analytics in decision-making.1 Many decisions continue to be made based on gut instinct and internal politics, even when sophisticated analytics and Big Data are part of the decision-making process. Though not wishing to resurrect Taylorism2, we feel decisions can be made more scientifically and more effectively through the appropriate use of data and analytics and, more fundamentally, by thinking like a scientist.
Thinking like a scientist isn’t just matrix algebra and programming – these are important tools for some participants in the decision-making process but are means and not ends. Thinking like a scientist is a way of looking at the world that helps us tie disparate data and information together to make better decisions in a timely fashion. One not need not have elaborate statistical skills in order to think scientifically – most scientists have actually had minimal academic coursework in statistics.3
The first steps are to examine our assumptions and, in a nutshell, to do our homework. Here are a few basic questions we would encourage decision makers to ask themselves:
- What decisions do we really have to make? Why do we think these are the decisions we must make?
- How much of what we “know” about our product category is actually mere guesswork? What do we really know about the competition? Is the competition really the competition? Perhaps our definitions of our category (and core consumers) are too narrow.
- Is it time to revisit our SWOT analyses (Strengths/Weaknesses/Opportunities/Threats)?
- What relevant data do we have and how reliable is it? What data can we obtain that might fill in important blanks?
- When do we really have to make our decision? A decision made too slowly is a bad decision, but a bad decision made hastily is not a good one either.
Thinking like a scientist can help us better judge whether a decision will have the desired consequences and can also bring to light choices that we had not considered.
Dashboards under assorted names are now a dime-a-dozen but the utility of many of them is uncertain. KPIs are religiously tracked but many may have no empirical relationship with the bottom line. They are assumed to be connected with sales, market share and profitability, for example, but this assumption might never have been rigorously tested, and some KPIs may only be legacy items with no real business meaning. Chapter 8 of ‘It’s not the size of the data – it’s how you use it’ explains how to connect KPIs to your bottom line, and to drop most so-called KPIs because they are not leading indicators of hard performance.
There are many traps that managers can easily fall prey to when trying to unravel the mysteries of the marketplace:
- Presumed causes may, in fact, be effects. For instance, we may observe a huge spike in our paid search clicks together with a spike in online sales and fully attribute the sales increase to the success of our paid search. However, these customers may have already decided to buy from us thanks to other incentives, and simply use search as a lazy way to get to our site. We usually do not have experimental evidence on which to base our decisions and even experiments are never 100% conclusive.
- There may be important variables we haven’t considered. According to London Business School professor Tim Ambler, there should be a KPI for every likely cause of success or failure. Moreover it is important to cover the main stakeholders.
- We frequently cross tab or plot variables two at a time but this does not account for factors that might mediate or moderate their relationship, which may really be weaker or stronger than the tabulation or graph suggests. The classic – macabre – example is that psychiatrist visits increase people’s suicide risk – this relation holds up, but switches from positive to negative once the third variable (depression) is accounted for.
- In time-series data there are often lagged relationships among variables and one that might seem irrelevant may actually have a long-term impact on sales or some other key measure. This effect could be large or small, beneficial or harmful. For instance, increases in brand consideration and liking often lead to long-term brand benefits, even after the competition reacts.
- There may be an genuine relationship between a marketing input and sales but we may not spot it because the relationship between the two is non-linear or obscured by other variables that we have not measured or modelled.
- We may be confusing a fluke with a trend. The more we seek, the more we will find. We should look at the overall patterns in the data and not just focus on one or two variables.
- Last but not least, be wary of confirmation bias – it’s quite natural to search for, interpret or recall information in a way that confirms our beliefs!
Unless the variables that are truly relevant are statistically identified and tied together, dashboards and other decision support tools may be misleading or at best a waste of money. In extreme cases, we would be better off tracking random numbers generated by a spreadsheet; this certainly would be faster and incur little cost!4 (It is important to recognize, though, that statistical models are simplified representations of reality, not actual reality, and that math can never entirely replace the gut in management decisions.)
Humans are strongly inclined to think dichotomously (e.g., something is either good or bad) even though thinking in terms of conditional probabilities is usually a better reflection of the way the world works.5 We should also be frank and admit that data and analytics are often used to support decisions that have already been made and that we especially love numbers match our view of the world! Furthermore, it’s often quite easy to put forth a seemly good “explanation” about why something has happened after the fact but being able to actually predict future events is another matter all together.
Data and analytics have been hyped to the point where many of us are getting sick and tired of hearing about them, and there is also a lot of disagreement about what they mean. In reality, we feel they are still greatly underutilized by managers. This is very unfortunate since we are now at a point in time in which many organizations now have more data and better analytic tools than ever to enhance decision making. However, we should stress that it’s the thought process that’s most important and, by following some of the guidelines we’ve outlined, managers can make better decisions even with limited data and mathematical tools. It’s truly the thought that counts.
1 In the past many marketing research agencies were mainly field and tab companies, often with an Operations department headed by a “Programmer/Statistician.” This person was in charge of fieldwork and data tabulations. Perhaps because of this, marketing researchers to this day often think of analytics as cross tabs or programming. It’s also conflated with ‘Big Data’.
3 See Statistics Done Wrong (Reinhart), for example.
4 Structural Equation Modelling and Time Series Analysis, while highly technical, offer very useful conceptual frameworks for thinking about these issues. The Halo Effect (Rosenzweig), The Improbability Principle (Hand), Risk Assessment and Decision Analysis (Fenton and Neil) and It’s Not The Size Of The Data – It’s How You Use It (Pauwels) are four books that also address these concerns.
5 Instead of “Will this work?”, for instance, “If we assume A, B and C, what is the likelihood of D?” may be the more useful question in many circumstances.
Kevin Gray is president of Cannon Gray, a marketing science and analytics consultancy. Koen Pauwels is professor of marketing at Ozyegin University, Istanbul.