A key hurdle facing the 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 DM (diabetes mellitus) marketplace 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, mode and method of administration, as opposed to statistically significant differences in glucose control, which are clinically relevant.
There is no consensus in the pharmaceutical industry on the most appropriate population-based balance between diabetes-related physiological outcomes (such as HbA1c) and measurement of quality of life (QoL). However, data derived from patient reported outcomes (PROs) measures can give added value in supporting key biomedical endpoints. It can also provide an evidence base to direct resources towards treatment most valued by patients and increase patient acceptability and adherence.
The key challenge however, for pharma – mindful of thecurrent regulatory issues (FDA) (EMEA)– is the selection and implementation of a PRO for a clinical trial and to justify the cost in the development of an effective PRO measurement strategy.
Health status and quality of life
A critical aspect of the study design is selecting the right PRO for a specific trial to ensure that the benefits of the primary physiological endpoint of treatment are measured. Pharma is faced with a plethora of potential PROs each purporting to measure – often without a sound theoretical or measurement model – a specific health construct such as health status or quality of life. As a result the choice of a PRO is often made according to whether the instrument has been used in previous studies or its name appears to be appropriate for the intended use. There is also the tendency for those conducting clinical trials to treat the more commonly measured health constructs such as QoL, Health-related quality of life and health status as interchangeable in the selection process – they are not.
While there is no universally accepted definition of QoL for example, there is the general consensus that it is based on the individual’s subjective evaluation of the psychological, physical and social aspects of their life and that these changes over time are as a result of different influences such as treatment.
Quality of life measures contrast with the commonly used measures of health-status such as the SF-36 and EQ-5D which are often referred to as indicators of QoL. The focus of these measures is rather on the quality of health including the biological and physiological dysfunctioning, symptoms and functional impairment. While such outcomes can impact on patients’ QoL they cannot be accurately described as indicators of QoL.
Selecting an appropriate PRO is of course the most critical aspect in the measurement of secondary endpoints and in the absence of some universally agreed definition as to what the measured health concept is and its relationship to the primary endpoints, choosing the appropriate PROM can be problematic.
Developing 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 outcomes.
A 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.
Components of each of the key stages of the strategy are shown here which includes stressing the importance of making explicit the expected treatment effects e.g. primary biomedical endpoint(s).
A key feature of the measurement strategy included the necessity as part of the PRO selection process, the careful consideration of the relationship between the PRO’s item content (conceptual model) and the expected secondary endpoint (e.g. health status).
The endpoint model
Central to the measurement strategy is the endpoint model which provides the rationale for the measurement model, by making explicit the associations among the different health outcomes, patient and domains to be measured by the PRO.
Based on the understanding of the disease and the expected treatment effects the model shows the specific links between the primary clinical endpoint – of a reduction in the risk of hypoglycaemia – and secondary endpoint – the subsequent impact on patients’ QoL – to be measured by the PROM. Once the relevant endpoint(s) (outcome (s)) has been identified, the appropriate PROM can be selected or developed.
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Categories: Patient reported outcomes