Matthew Reaney

Life is defined by our achievements and failures. Making goals and striving to reach them are how we categorize our day-to-day lives. This is true in our careers, our finances, our relationships, and also our health. Particularly for those living with a chronic condition, health goals must be agreed, set, and rigorously maintained. In some cases, the goals are defined by clinical parameters; for example, in diabetes, where control of blood glucose to within-recommended targets is the goal for most people. Achieving this goal helps to reduce the likelihood of micro- and macrovascular complications. In other cases, the goals are defined by our behavior; a month of no alcohol consumption among people taking blood-thinning therapy, for example. In line with the principles of behavioral and developmental psychology, it is important to reward successful outcomes where goals have been met. These rewards can be intrinsic or external. However, if we treat the outcomes in a simple binary fashion, where they are met or not, we miss important and rich information on those goals. 

	In order to better understand goal attainment, data is commonly collected using continuous interval data, so as a researcher can measure the achievements of an individual in terms of their status change (and the magnitude thereof), rather than simply whether a pre-defined goal was achieved. This measurement strategy is the cornerstone of in-vivo clinical research for the development of pharmacotherapy, and is the basis on which pharmacotherapeutic medications are developed, licenced, regulated, and made available within the healthcare system. Specifically, pivotal trials aim to answer two questions: (1) “did the experimental therapy achieve clinical benefit in patients who enrolled in the study”, and if so; (2) “is the benefit greater with the experimental therapy than with standard of care?”

	The answer to this question can, at its simplest, be achieved by the analysis of data at two timepoints; baseline (prior to initiation of therapy) and one additional timepoint, some days, weeks or months later, when the pharmacokinetics and

 pharmacodynamics of the drug indicate it should have reached peak efficacy. In reality, longer-term trials are usually carried out to ensure maintenance of an effect over time, with data collection visits occurring at regular intervals (normally one to six months apart). In Figure 1 at right, a graph shows the effect of a hypothetical experimental medication (medication A) compared to two active comparator medications (medications B and C) on physical functioning in rheumatoid arthritis. Assessment of physical functioning is made using a well-established patient-reported outcome (PRO) questionnaire, the SF-36.

	Items on this questionnaire are measured and scored using interval data, and lower scores are indicative of less impairment. In this trial, all three medications are efficacious, in that they have improved physical functioning over the three months of the study. They have done so to a “similar” (non-inferior) degree. In response to the two questions above, one must conclude based on the data presented in Figure 1, that (1) yes, there was a significant improvement in physical functioning from baseline to trial endpoint (3 months) with the experimental medication, but (2) no, the benefit was not greater than with standard of care (using traditional inferential statistical analysis).

	However, the apparent similarities in the impact of these medications on physical functioning may be a function of the simplicity of the study design; more specifically, 

the infrequent measurement of a concept which may change frequently may be masking important differences between the medications. Imagine that, in fact, the medications behaved as per Figure 2 at left, captured in the same trial using twice-weekly diary data rather than just once at the end of the trial. This shows clear differences between the medications. Although our answer to the first question (“did the experimental therapy achieve clinical benefit in patients who enrolled in the study?”) remains yes, the answer to the second question (“is the benefit greater than with standard of care?”) is now not so clear. Utilizing area-under-the-curve analyses (in addition to change-from-baseline analyses) would suggest medication A differs significantly to medication B, but not to medication C. However, the data now provides a basis for hypothesizing that these three drugs are fundamentally different. The differences are in the patient experience.

		A person who is cautious of doing too much, gets anxious about changes, but desires increased physical functioning would benefit most from medication A.

		A person who was physically active prior to the rapid development of rheumatoid arthritis will want to achieve maximal therapeutic benefit quickly so as to return as much physical functioning as possible as early as possible. For this person, medication B is the right choice. 

		A person who needs to engage in peak physical performance but is able to rest for some days afterwards would benefit from medication C (assuming that the peaks and troughs are predictable).

	It is too simplistic to suggest that more data points are more informative. Rather, the frequency of data capture must relate to the dependent variable (outcome) of interest, and be captured in a manner that reduces measurement error. For example, daily collection of “quality-of-life” data may decrease the variability in the measurement owing to participant burden and generalizations by participants when completing the scales. This would reduce the sensitivity of the assessment to identify a meaningful change from baseline within and between cohorts.

	On the other hand, researchers have shown reduced error variance by collecting episodic data in trials of overactive bladder, largely as a function of reduced recall bias and heterogeneity within participants over the course of a trial. The timing of assessment is also key; while random momentary assessment may be appropriate and beneficial for capturing “in-the-moment” fatigue in multiple sclerosis (which is known to follow a diurnal pattern), it is appropriate to standardize collection of pain and stiffness data once during a 24-hour period among rheumatoid arthritis patients. 

	Data collected on an appropriate assessment schedule gives power not only to answer the two questions regularly posed in pivotal trials, but also to explore individual responders to therapy. Figure 3 below provides population-level data from a trial of a hypothetical medication for insomnia.

	Although there is no comparator in this trial, we are able to conclude that there was a significant improvement in sleep time from baseline to trial endpoint (36 weeks) with 

the medication. Figure 4 at left shows data from four participants in the trial with very different experiences. The frequency of data capture allows us to define these as responders (for whom the medication worked) and non-responders. Participant 1, 2, and 4 could all be classified as responders, but for different reasons: 

		When participant 1 sleeps, (s)he sleeps a similar number of minutes/hours per night following initiation of medication as before medication. However, (s)he sleeps more frequently (every night) following initiation, opposed to before initiation (57% of nights).

		Participant 2 sleeps longer since initiation of medication, and has less variability in total time spent sleeping per night.

		Participant 4 has been afforded a consistent addition of one hour sleep per night since initiation of the medication.

		These individual differences in medication action and patient experience do not show on a plot of the average effect (See Figure 3).  

	The temptation in clinical research is to collect some data on a more frequent basis than others. A good example of this is treatment satisfaction, an endpoint utilized in clinical trials as a partial proxy for adherence (anticipated medication-taking behavior outside of the trial environment), but collected less frequently (in general) than clinical data. Satisfaction is a latent variable likely to be a function of expectations and experience of efficacy (positive clinical benefit) and adverse effects of medication (negative side-effects). Some combination of these will contribute to a person’s stated satisfaction with 

treatment, which can change day-to-day, in line with the perceived benefits and detriments of treatment on that day. Regular collection of all three types of data (efficacy, treatment satisfaction, side-effects) allows the researchers to overlay the observed data and understand the main contributing factors to whether high levels of satisfaction are achieved within-and between-patients. 

	As the medical community moves toward individualized and tailored therapeutics, and pharmaceutical companies are increasingly opting into risk-sharing agreements with payers, it is important to understand the patient journey with a medication, as well as the goal achievement of that medication. Collecting this data in pharmacotherapy development programs allows an understanding of the true impact of the medication in that context. This allows for more targeted prescribing, but also for appropriate expectation setting by treating physicians in clinical practice; if the patient journey does not match expectations, then adherence is likely to be poor. In addition to the use of frequent data collection to understand and prescribe medication optimally, there may be other hypotheses that can be generated from the data which can prompt future research for all stakeholders. For example:

 Is there any long-term (>1 year) clinical benefit to medication C over medication A and B (See Figure 2)? The rapid achievement of a goal and maintenance of that goal may predict future outcomes more than simply achieving the goal at three months. Key audience: regulators, payers, healthcare professionals.

 The “positive feedback loop” is a very important psychological construct in medication taking. The best predictor of positive outcomes is good response and, therefore, one could hypothesize that if patients see good outcome early (like with medication B in Figure 2), they are more likely to put up with side-effects and adhere (and, therefore, persist) better. However, what about medications A and C? How do they compare on treatment satisfaction and adherence variables? Key audience: payers, healthcare professionals, patients.

 Can a timepoint be established on Figure 2 at which an “unlikely responder” can be identified with reasonable specificity and sensitivity? Key audience: payers, healthcare professionals, patients.

	Answering these questions, and other clinically relevant questions, requires routine and regular collection of outcome data according to a schedule which allows for maximum understanding of the pharmacotherapy under investigation. In an era of “real-world evidence”, this data can support healthcare tools to enhance medication-taking behavior and clinical communication at appropriate and relevant timepoints, potentially increasing both the quality of life of patients and quality of care they receive.

, M.Sc., was Sr. Scientific Consultant, ERT, at time of research; 

, PhD, is Chief Scientific & Quality Officer, ERT

Articles

Measuring clinically relevant response requires routine and regular collection of outcomes data.

Therapeutic Success or Failure: A Journey, Not Just a Destination

	Patient-reported outcome (PRO) questionnaires can be used to collect subjective clinical and psychosocial outcome data directly from patients in clinical trials, in order to describe their perception of a disease and its treatment(s), without the interpretation of responses by a physician or others. They are the only systematic quantitative way to ask  patients directly about how they feel or function. 

	In looking at the newer classes of drugs for type 2 diabetes mellitus (T2DM), such as DPP-4 inhibitors, GLP-1 receptor analogues, novel insulins, and SGLT-2 inhibitors, PRO questionnaires have been used to

 inform endpoints in most of the pivotal programs. As with the concept of blood glucose, a PRO can contain many related but distinct concepts, including symptoms of diabetes, side-effects of treatment, health-related quality of life, psychological well-being, and treatment satisfaction. Each concept captures a different aspect of a person’s experiences, thoughts, and feelings about T2DM and/or its treatment. All of these have been measured as endpoints across the pivotal trials of the newer classes of T2DM drugs. 

	This PRO data has the potential to offer insights into the subjective experience of therapy and provide an informed perspective of the associated patient-perceived benefit-to-risk ratio of the medication. Historically, PRO data has not been held in high esteem in T2DM, partly due to the traditional glucose-centric model of care, and partly due to variable scientific credibility in their measurement and lack of clarity in their interpretation.

	However, in recent years the science of PROs has progressed considerably, with U.S. and European regulators releasing guidance clarifying their expectations of the scientific rigor that should be applied to the development and selection of PRO endpoints. A recent PRO extension to the CONSORT statement highlights that well-developed PRO instruments can provide reliable and valid data in a manner which is meaningful to clinicians and patients alike. Despite this, the only PRO data to be reflected in product labelling for any anti-hyperglycemic medication for T2DM is blood glucose (based on patient self-recording of objective data). That was, until the 2014 European approval of Trulicity (dulaglutide).

	In the efficacy and safety section of the summary of product characteristics for Trulicity, the European Medicines Agency (EMA) included the following text: “Dulaglutide significantly improved total treatment satisfaction compared to exenatide twice daily. In addition, there was significantly lower perceived frequency of hyperglycaemia and hypoglycaemia compared to exenatide twice daily.” 

	Demonstrating improvements from baseline in treatment satisfaction with Trulicity, and showing that this improvement was greater with Trulicity when compared to exenatide twice daily, allows the developer of Trulicity (Eli Lilly & Co.) to summarize with one metric the patient’s appraisal of his or her experience with treatment (i.e., both processes and outcomes), their perception of the relative balance between positive clinical outcomes and negative side-effects, and their perceived benefit of improved clinical efficacy and reduced injection frequency versus an efficacious comparator. Perhaps most importantly for translational researchers, the data is important for clinical feasibility. 

	Such PRO data, taken together with clinical efficacy and safety data, can assist HCPs in providing holistic care in T2DM that is in line with patient preferences, needs, and values per the principles of evidence-based medicine. The inclusion of PRO data in the product label demonstrates a regulatory acknowledgement of the relevance of the patient perspective as it pertains to the saturated market of antihyperglycemic medication for T2DM, and gives HCPs confidence in the scientific credibility of the PRO endpoint.

Combining PRO data with clinical feasibility data can make a difference in diabetes care.

Patient’s Voice Finally Being Heard in Diabetes Drug Research

	Type 2 diabetes mellitus (T2DM) is one of the Western world’s most common chronic conditions, with global prevalence increasing rapidly. For people with T2DM, it is important to control the level of glucose in the blood so as to maintain good health and decrease the risk (or at least delay the onset) of complications associated with the condition, including retinopathy, cardiovascular disease, nephropathy and neuropathy. It has long been acknowledged that achieving good control of blood glucose requires that healthcare professionals (HCPs) and patients work together to increase the patients’ knowledge, self-efficacy, and engagement in self-management behaviours, including taking their antihyperglycemic medication. However, adherence to diabetes medication regimens and other aspects of self-management are poor. In recent years, HCPs have recognized that the psychosocial aspects of living with diabetes must be prioritized to maximize the improvement in clinical outcomes; indeed a recent consensus statement from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) highlights the importance of providing care that is respectful of and responsive to individual patient preferences, needs, and values.

	In the past decade there have been a slew of antihyperglycemic medications complete pivotal clinical trials. The purpose of these trials is to establish the safety and efficacy of the medication and to inform marketing authorization decision-making. To this end, efficacy continues to be defined using a glucose-centric model, with primary endpoints based on blood glucose reduction and/or control. However, many newer medications offer additional benefits, including weight loss and blood pressure control. One may assume that this incremental clinical benefit, if well communicated to patients, would increase the utilization of, and adherence to, these newer therapies. However, the same therapies are oftentimes associated with negative side-effects, including gastrointenstinal and urinary complications, as well as unknown long-term safety profiles. All of this data will be utilized by marketing authorization decision-makers and reflected in product labelling if approved, but patients’ perceptions of, and experiences with medication are not routinely reflected in the same labelling. This is a problem for clinicians, prescribers and researchers interested in translational medicine; in establishing safety and efficacy in a trial and extrapolating that to non-trial environments, where we aim to maximise both clinical and psychosocial outcomes, or at least ensure that clinical outcomes are not achieved to the detriment of psychosocial ones. For this we need to gain insights into the patients’ subjective experience of therapy during the pivotal clinical trials.

	Patient-Reported Outcome (PRO) questionnaires can be used to collect subjective clinical and psychosocial outcome data directly from the patients in clinical trials in order to describe the patient’s perception of a disease and its treatment(s) without the interpretation of responses by a physician or anyone else. They are particularly relevant and important when eliciting data that can only be captured by asking patients directly about how they feel or function. In looking at the newer classes of medication for T2DM (e.g. DPP-4 inhibitors, GLP-1 receptor analogues, novel insulins, SGLT-2 inhibitors) PRO questionnaires have been used to inform endpoints in most of the pivotal programs. As with the concept of blood glucose, a PRO can contain many related but distinct concepts, including symptoms of diabetes, health-related quality of life, psychological well-being, and treatment satisfaction. Each concept captures a different aspect of an individual’s experiences, thoughts, and feelings about T2DM and/or its treatment, although there is overlap. These, as well as perceived impact of weight, have all been measured as endpoints across the pivotal trials of the newer classes of medication for T2DM.

	This PRO data has the potential to offer insights into the subjective experience of therapy and provide an informed perspective of the patients’ experience of treatment, and the associated patient-perceived benefit-to-risk ratio of the medication. Unfortunately, PRO data has not traditionally been held in high esteem in T2DM, partly owing to the traditional glucose-centric model of care, and partly owing to variable scientific credibility in their measurement and lack of clarity in their interpretation. However, in recent years the science of PROs has progressed considerably, with the US and European regulators releasing guidance documents clarifying their expectations of the scientific rigour that should be incorporated into the development and selection of PRO endpoints. A recent PRO extension to the CONSORT statement highlights that well developed PRO instruments can provide reliable and valid data in a manner which is meaningful to trialists, clinicians and patients alike. Despite this, the only PRO data to be reflected in product labelling for any antihyperglycemic mediation for T2DM is blood glucose (based on patient self-recording of objective data). That was, until the European approval of Trulicity

	In the clinical efficacy and safety section of the summary of product characteristics for Trulicity

, the European Medicines Agency (EMA) included the following text: "Dulaglutide significantly improved total treatment satisfaction compared to exenatide twice daily. In addition, there was significantly lower perceived frequency of hyperglycaemia and hypoglycaemia compared to exenatide twice daily." Demonstrating improvements from baseline in treatment satisfaction with Trulicity

, and showing that this improvement was greater with Trulicity

 when compared to exenatide twice daily allows the developer of Trulicity

, Eli Lilly, to summarize with one metric the patients’ appraisal of his or her experience with treatment (i.e., both processes and outcomes), their perception of the relative balance between positive clinical outcomes and negative side-effects of Trulicity

, and their perceived benefit of improved clinical efficacy and reduced injection frequency versus an efficacious comparator. Perhaps most importantly for translational researchers, the data is important for clinical feasibility; a patient who is satisfied with their treatment is more likely to engage and adhere, and will thus therefore derive the maximal therapeutic benefit that the medication can offer.

	Such PRO data, taken together with clinical efficacy and safety data, can assist HCPs in providing holistic care in T2DM that is in line with patient preferences, needs and values per the principles of evidence-based medicine. The inclusion of PRO data in the product label demonstrates a regulatory acknowledgement of the relevance of the patient perspective as it pertains to the saturated market of antihyperglycemic medication for T2DM, and gives HCPs confidence in the scientific credibility of the PRO endpoint.

	To my mind therefore this is a huge step on the path to which one day, I hope, will yield an even more patient-centric model in which the patients’ perspective becomes a key endpoints in pivotal trials of atihyperglycemic medication for T2DM; perhaps even as part of a composite endpoint; improvement of clinical outcomes in the absence of psychosocial detriment.

Type 2 diabetes mellitus is one of the Western world’s most common chronic conditions, with global prevalence increasing rapidly.

The Patient’s Voice is Finally Being Heard in Diabetes Drug Research

 (dulaglutide), approved by the EMA in November 2014 for the treatment of Type 2 diabetes in adults, is the first diabetes drug to have a Patient Reported Outcome (PRO) (other than self-monitored blood glucose profiles) included in it’s summary of product characteristics ("label"). The following text has been included under the clinical efficacy and safety section: "Dulaglutide significantly improved total treatment satisfaction compared to exenatide twice daily. In addition, there was significantly lower perceived frequency of hyperglycaemia and hypoglycaemia compared to exenatide twice daily."

	This claim is important for multiple reasons. Not only does it exemplify EMA’s stance on the use of PROs in new medical product development, but also because the developer–Eli Lilly–has garnered important insight into patients’ perspectives of dulaglutide. Demonstrating improvements from baseline in treatment satisfaction with dulaglutide, and showing that this improvement was greater with dulaglutide when compared to exenatide twice daily allows Eli Lilly to summarize with one metric both the relative balance (from the users’ perspective) between positive clinical outcomes and negative side-effects of dulaglutide, and the patient perceived benefit of improved clinical efficacy and reduced injection frequency versus an efficacious comparator. It also loosely allows them to hypothesize patients’ adherence to dulaglutide in clinical practice. As indicated in a 

 by Eli Lilly when the data was published in 2013, "These results, coupled with dulaglutide's positive clinical data, suggest that…(dulaglutide) may be an attractive treatment option for patients with Type 2 diabetes."

	The data is derived from a Phase III trial comparing dulaglutide to exenatide twice daily (open-label) and placebo (double-blind) when added to maximally tolerated doses of metformin and pioglitazone

. The treatment satisfaction data was presented at the American Diabetes Association meeting in 2013.

	Since Type 2 diabetes is a chronic self-management condition, patients are asked to engage with many aspects of self care, including the routine taking of medication to control their blood glucose. Adherence rates for medication in Type 2 diabetes is poor, despite patients' acknowledgement about the importance of their medication (and the clinical and economic detriments of non-adherent behavior).

	A patient who is satisfied with their treatment is more likely to engage and will therefore derive the maximal therapeutic benefit that medication can offer. Treatment satisfaction is therefore a relevant endpoint for clinical research in Type 2 diabetes, and perhaps more importantly, a key point for discussion between provider and patient in evidence-based prescribing. The inclusion of treatment satisfaction data in the label for dulaglutide shows that the EMA is acknowledging the relevance of the patient perspective as it pertains to the saturated market of Type 2 diabetes treatment.

	Additional editorials/commentaries related to this topic include; selecting PRO measures in diabetes research

, and the importance of understanding patient preferences in diabetes research

http://www.ncbi.nlm.nih.gov/pubmed/24879836

http://www.abstractsonline.com/Plan/ViewAbstract.aspx?mID=3217&sKey=e68ac573-fe45-4c2f-9485-6270854fc10b&cKey=9164e616-5bf1-4f0c-83bf-d82c6601db84&mKey=89918d6d-3018-4ea9-9d4f-711f98a7ae5d

http://spectrum.diabetesjournals.org/content/27/4/229.short

http://www.hoajonline.com/jdrcm/2050-0866/3/7

http://onlinelibrary.wiley.com/doi/10.1111/1753-0407.12187/epdf

https://www.ert.com/clinical/resources/webinars

Trulicity® (dulaglutide), approved by the EMA in November 2014 for the treatment of Type 2 diabetes in adults, is the first diabetes drug to have a Patient Reported Outcome.

Author | Matthew Reaney