Title: Estimating time to next event for patients with recurrent events
Funder: Department of Health and Social Care

Abstract: Background Current prognostic models in epilepsy and asthma estimate which patient subgroups have higher recurrence risks, based only on limited information about prior events. Although such models are relatively useful they do not fully utilise the information that can be collected from patients on the history of all previous events, and they cannot be updated whenever a patient has a recurrence. Additionally, published prognostic models only estimate overall risk of an event rather than the more clinically informative time to next event. Aims To provide epilepsy patients and asthma patients with an estimate of time to, and risk of, their next event. Additionally, to develop statistical methodology enabling prognostic models to: include all events, not just those occurring prior to randomisation (or start of treatment), estimate risk at multiple time points, and be updateable whenever a patient has a recurrence. Plan of InvestigationCHAR(13) + CHAR(10) This fellowship will include applied and methodological work and will consider epilepsy patients and asthma patients. Initially, a prognostic model will be developed to estimate risk of a specified event, including information on the timing and frequency of all events, as well as patient characteristics and test results. This will require novel statistical methodology which extends recurrent event theory by accounting for the structural relationship between the outcome and the recurrent events. Next, a dynamic prediction model will be developed to estimate risk of next event which can be updated each time an event occurs. Again novel methodology will be required which accounts for the relationship between the outcome and the recurrent events. Estimates of time to next event will then be calculated, again utilising complete event history. Novel methodology will be developed to ensure time estimates can be obtained for any model. A website, with a user-friendly interface will then be developed, with the input of both patients and clinicians, to estimate time to next event for epilepsy patients and asthma patients, to inform patient counselling and management of recurrent conditions. At each stage, the developed models will be validated, using independent data, to ensure they are generalisable, and therefore can be utilised in clinical practice. This project will employ three large epilepsy datasets and one extensive asthma dataset. Summary of Benefits There are many clinical and methodological benefits to this research. The most important clinical benefit is likely to be more informative time-related predictions of risk (for example, given that you have been event-free for x months, there is a 90% chance of a recurrence within the next 18 to 24 months), rather than general predictions of overall risk (5% chance of recurrence). As a result of novel statistical methodology it will be possible to estimate risk of next event and the more informative time to next event. The models would also be updatable following any recurrences. So, instead of working only once, for a specified event, these models would continue to work whenever a patient has a recurrence. Risk estimates would also be available at multiple time-points.This has the potential to more accurately assess the impact of a range of treatments on outcome risk to better inform treatment choice. Additionally, patient guidance regarding risks (such as driving and occupation for epilepsy patients) may improve and there is the potential to change asthma patients' behaviour by demonstrating the impact of lifestyle changes on their likely time to next event. Finally, there will be wider methodological benefits including better, more appropriate, modelling of chronic conditions in general, with more relevant patient-centred outcomes.

Lay abstract: Estimating the risk of recurring events for certain conditions such as epilepsy (recurring seizures) and asthma (recurring asthma attacks) is difficult. Statistical methods (or models) exist to help predict which patient subgroups have a higher risk of an event than others. However, most of these predicting models only include information about events prior to randomisation or starting treatment. Additionally, they cannot be updated after any additional recurrences, and they estimate overall risk of an event rather than when the next event will occur. Through applied and methodological work, models will be developed providing patients with an estimate of the time until their next seizure or asthma attack, rather than just the chance of it happening at some point in the future.This project will benefit from the use of three large epilepsy datasets and one extensive asthma dataset. Initially, novel methodologies will be developed to enable the risk of a specified event, such as 365 continuous days without a seizure, to be estimated utilising information about all seizure recurrences, not just those that happened before randomisation, or the start of treatment. Other useful patient information such as age, gender, and test results will also be included to inform the estimates.New statistical methods will then be developed to estimate risk of the next event, rather than a specified event. This will ensure that the prognostic models can be updated each time an event occurs.These models will be developed for both epilepsy and asthma patients. Following discussions with patients and clinicians it was felt that estimates of time to next event would be preferable to estimates of risk. Therefore, this project will conclude by developing methods to calculate estimates of time to next event, be that a seizure or an asthma attack.Ultimately, a user-friendly website will be developed with input from patients and clinicians. This will help clinicians and/or patients to input full information about a patient, including details of any previous seizures or asthma attacks, and obtain an estimate of time to their next event. This has the potential to change patient behaviour such as showing a patient with asthma the benefits of stopping smoking in relation to their next attack. It will also enable clinicians to better plan appointments and will potentially improve treatment decisions. For patients with epilepsy, it will also improve the advice they receive from clinicians about driving and work.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE