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Integrating Patient Preferences into Health Technology Assessment: Five Steps to Success

Patient-centered research experts Kevin Marsh, executive director; and Christine Michaels-Igbokwe, senior research associate, use data from a recent review1 to identify five key steps in considering when and how to use patient preference (PP) studies to support your health technology assessment (HTA).

What Do Patients Want From Health Technology Assessment?

Learning what patients want is opening eyes – and opportunities – for therapeutic innovators and decision makers. As patient preferences become increasingly important in health care decisions, sponsors and HTA agencies are beginning to consider ways to use PP studies to inform HTA. PPs have been incorporated into HTA in a variety of ways. Two approaches are now encouraged by agencies, including the U.K.’s National Institute for Health and Care Excellence (NICE) – using PP to (1) inform the selection of patient relevant endpoints in clinical trials and (2) benefit-risk assessment (BRA).

Outlined below are five key steps that will help you plan a PP study to inform endpoint selection or BRA that can provide insights relevant to a subsequent HTA.

Step 1: Plan Early

Impactful PP studies need to be planned early. We often see “quick fix” PP studies in which patient preferences are done after the Phase III trial is completed or after an HTA application has been submitted and challenged by an agency. In this case, patient insights come too late to guide better clinical trial design or more confident payor acceptance.

To meaningfully inform endpoint selection, PP studies must be done alongside Phase II trials – at the latest. Prior to Phase II, it is important to understand patient views on perceived burden of disease and desired treatment outcomes. This type of patient insight can help determine whether a more rigorous, quantitative PP study can add value to an HTA, and be useful in ensuring that the PP study captures patient priorities.

Step 2: Determine Whether the HTA Involves Considerations Sensitive to Patient Preferences

While there is no firm guidance, recent case studies point to some areas where a PP study may add value, such as when a novel endpoint is being used, where a market is crowded, where the evidence on the meaningfulness of a benefit has yet to be developed or where innovations pose questions about patient uptake of treatments.

Some of the most exciting new innovations offer good case studies of where PP studies can add value. For example, chimeric antigen receptor T-cell (CAR-T) therapy is a novel immunotherapy that is being developed as a cancer treatment. CAR-T therapies can be delivered as a one-time treatment. Compared to multiple cycles of chemotherapy, which would be the most relevant treatment alternative for many patients, this reduced treatment burden may be viewed very positively. Further, CAR-T may offer improvements in efficacy. However, CAR-Ts are also associated with some important risks. Nearly 50% of patients may experience cytokine release syndrome (CRS), a potentially severe and life-threatening side effect of the treatment, and more than 30% could experience symptoms of neurotoxicity. This combination of benefits and risks may be valued differently by different patients, depending on the treatment alternatives available to them, their expected outcomes with and without treatment, and their risk tolerance. A PP study can thus provide insight into the relative importance of these benefits and risks, and thus which patients will be willing to take CAR-Ts.2

Another example can be seen in emerging Alzheimer’s (AZ) therapies. Traditionally, the only treatments available for AZ patients have targeted disease symptoms. However, several treatments in the pipeline are now targeting underlying disease mechanisms with the hope of slowing or even reversing disease progression. Clinical studies of these drugs focus on clinical assessments measuring the reduction or reversal of buildup of amyloid plaque or tau protein in the brain. However, the extent to which this can translate to improvements in patient outcomes is unclear. Furthermore, should improvements in patient outcomes be demonstrated, the meaningfulness of these improvements is also unclear. PP studies could help address both these challenges – (1) assessing the value that patients put on improved outcomes and (2) their willingness to tolerate uncertainties that improvements in clinical markers of AZ will translate into improvements in patient outcomes. Preferences may also vary by disease stage or patient risk factors; such data would be useful in developing an understanding of patients most likely to take up new treatments, given the uncertainty in the existing evidence base.

Step 3: Test Your Concept with Key Stakeholders

The relevance and appropriate form of PP data can be informed by consultation with HTA agencies and other relevant stakeholders, including patients and patient representatives. NICE, for example, has a scientific advisory process specific to PP research. In these discussions, it is important to verify that the types of outputs your study generates will be useful for their decision making and how it might be expected to influence decisions, as well as that the design of the study will provide data that the agency considers valid. HTA agencies have traditionally relied more on general population preferences to inform their decision making, and the use of PP studies remains novel. In these circumstances, consultation with agencies is important to ensuring PP studies are impactful.

Step 4: Review the Literature; Apply What You Already Know

Health research applications of PP are relatively recent, but evidence based on patient preferences is growing. A literature review of PP findings for the same or a related condition may show that your questions have already been answered. Evidence is also emerging on the transferability of PP data from one setting or patient group to another. Even if your target condition or patient population has not been studied, it may be possible to transfer evidence from other patient groups in a manner sufficient to address HTA agency questions.

Findings from a literature search can also serve to build confidence in your strategy or provide guidance in designing a PP study to address your unanswered questions.

Step 5: Match Your Method to Your Question

PP studies use a wide range of methodologies, depending on the research questions being asked. Discrete choice experiments (DCEs) are most used, but there are other methods such as best-word scaling (BWS), thresholding technique (TT) or swing weighting. The appropriateness of each method may be influenced by (1) the feasible sample size, (2) the difficulty and complexity of the research questions and (3) the type of outputs required. After settling on the appropriate method, it is important to ensure that the design of your PP instrument will provide data on the outcomes of interest. For instance, this includes careful consideration of the attributes that will be included, clear description and presentation of the attributes, appropriately framing the choice question and considering whether the choice context warrants the inclusion of an opt-out or status-quo alternative.

The World Is Watching and Waiting for Guidance

Big regulatory events are on the horizon as global agencies grapple with the best ways to collect and apply PP data. Most notably, the Patient Preferences in Benefit-Risk Assessments during the Drug Life Cycle (PREFER) under the Innovative Medicines Initiative (IMI), a collaboration of academics, industry, regulators and HTA agencies, is expected to publish its recommendations on how PP data can be used by decision makers in late 2021 or early 2022. PREFER has already initiated an effort to get patient preference methods qualified by the European Medicines Agency (EMA) and the EU network of HTA bodies. Their recommendations will be watched early for further assistance on how PP can support HTA.

With this guidance just around the corner, we expect to see an increased use of PP data in HTA submissions, as well as innovation in the way that PP data informs HTA agencies’ decisions, including the potential for integration of PP into economic evaluation.

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