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  • Question on Consultation

    Has all of the relevant evidence been taken into account?
  • Question on Consultation

    Are the summaries of clinical and and cost effectiveness reasonable interpretations of the evidence?
  • Question on Consultation

    Are the recommendations sound and a suitable basis for guidance to the NHS?
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    Are there any equality issues that need special consideration and are not covered in the medical technology consultation document?
  • Question on Document

    Are there any other relevant ongoing studies that address the evidence gaps?

3 Approach to evidence generation

3.1 Evidence gaps and ongoing studies

Table 1 summarises the evidence gaps and ongoing studies that might address them. Information about evidence status is derived from the external assessment group's (EAG) report; evidence not meeting the scope and inclusion criteria is not included. The table shows the evidence available to the committee when the guidance was published. The EAG did not identify any ongoing studies that may address the evidence gaps.

Table 1 Evidence gaps and ongoing studies

Evidence gap

Neupulse

Clinical effectiveness compared with NHS standard care

Limited evidence

Clinical impact of Neupulse in different subgroups

No evidence

Longer term data on the clinical impact of Neupulse

No evidence

Impact of Neupulse on health-related quality of life

No evidence

Resource use

No evidence

Clinical and cost-effectiveness in people with comorbidities

No evidence

3.2 Data sources

Data could be collected using a combination of primary data collection, suitable real-world data sources, and data collected through the technology itself (for example, engagement data).

NICE's real-world evidence framework provides detailed guidance on assessing the suitability of a real-world data source to answer a specific research question.

The NHS England Secure Data Environment (SDE) service could potentially support this research. This platform provides access to high-standard NHS health and social care data that can be used for research and analysis. SDEs are data storage and access platforms that bring together many sources of data, such as from primary and secondary care, to enable research and analysis. They could be used to collect data to address the evidence gaps. The sub-national SDEs are designed to be agile and can be modified to suit the needs of new projects. Within an SDE, the data may be linked to other useful data such as that from primary care and could provide information on important confounders (for example, comorbidities).

The NHS Digital's Improving Access to Psychological Therapies data set (IAPT) and Mental Health Services Data Set (MHSD) are real-world data sets that could also be used to collect information about the impact that disorders have on mental health.

The quality and coverage of real-world data collections are of key importance when used in generating evidence. Active monitoring and follow up through a central coordinating point is an effective and viable approach to ensuring good-quality data with broad coverage.

3.3 Evidence collection plan

The suggested approach to addressing the evidence gaps for Neupulse is a longitudinal, parallel cohort study over 12 months. The study will follow 2 groups of people (intervention arm and a control arm) over 3 months (ideally 6 months), and compare their outcomes.

The studies should enrol a representative population, that is, people who would be offered standard care, including behavioural therapy, without digital technologies. This may include face-to-face appointments and monitoring. The studies should compare people with tic disorders or Tourette syndrome using digital technologies for self-management with a similar group having standard care. Eligibility for inclusion, and the point of starting follow up, should be clearly defined and consistent across comparison groups to avoid selection bias.

Data should be collected in all groups from the point at which a person would become eligible for standard care. The data from both the intervention and comparison groups should be collected at appropriate time intervals and up to a minimum of 3 months. Data from people in different centres, with comparable standard care and patient population, but no access to digital technologies for self-management, should form the comparison group. Ideally, the studies should be run across multiple centres, aiming to recruit centres that represent the variety of care pathways in the NHS.

Despite consistent eligibility criteria, non-random assignment to interventions can lead to confounding bias, complicating interpretation of the treatment effect. So, approaches should be used that balance confounding factors across comparison groups, for example, using propensity score methods. To achieve this robustly, data collection will need to include prognostic factors related both to the intervention delivered and patient outcomes. These should be defined with input from clinical specialists. Also, analysis should be stratified according to the severity of the tic disorder. Incomplete records and demographically imbalanced groups can lead to bias if unaccounted for. Data collection should follow a predefined protocol and quality assurance processes should be put in place to ensure the integrity and consistency of data collection. See NICE's real-world evidence framework, which provides guidance on the planning, conduct, and reporting of real-world evidence studies. This document also provides best practice principles for robust design of real-world evidence when assessing comparative treatment effects using a prospective cohort study design.

3.4 Data to be collected

Study criteria

  • At recruitment, eligibility criteria for suitability of using the digital technology and inclusion in the real-world study should be reported, and include:

    • a diagnosis status

    • position of the technology in the clinical pathway

    • the point that follow up starts

    • a detailed description of the standard care offered.

Baseline information and patient outcomes

  • Information about individual characteristics at baseline, for example, sex, age, ethnicity, socioeconomic status, clinical diagnosis (and date of diagnosis), details of any comorbidities and treatments. Other important covariates should be chosen with input from clinical specialists

  • Changes in tic severity using the Yale Global Tic Severity Rating Scale total score at baseline and during follow up (minimum of 3 months and ideally at 6 months).

  • Changes in patient quality of life using the Gilles de la Tourette Syndrome-Quality of Life Scale at baseline and over follow up (for a minimum of 3 months).

  • Information on healthcare resource use and exacerbation-related hospitalisation costs related to tic disorders and Tourette syndrome, including emergency department visits, hospital admissions and length of stay, and GP visits.

  • Any changes in a person's medication and any referrals to other services.

Implementation

  • Costs of digital technologies for supporting treatment of tic disorders and Tourette syndrome, including licence fees, healthcare professional staff time and training costs to support the service and integration with NHS systems

  • access and uptake including the number and proportion of eligible people who were able to, or accepted an offer to, access the technology

  • engagement and drop-out information, including reasons for refusal of treatment or stopping treatment

  • acceptability of the technology to people using it

Safety monitoring outcomes

  • Any adverse events arising from using digital technologies to support treatment of tic disorders and Tourette syndrome.

Data collection should follow a predefined protocol and quality assurance processes should be put in place to ensure the integrity and consistency of data collection. See NICE's real-world evidence framework, which provides guidance on the planning, conduct, and reporting of real-world evidence studies.

3.5 Evidence generation period

The evidence generation period should be 2 years (during which a minimum of 1 year of follow-up data will be collected). This will be enough time to implement the evidence generation study, collect the necessary information and analyse the collected data.