Title: Personalised predictions of outcomes in adolescence and adulthood for children diagnosed with Autism Spectrum Disorders: A Systematic review and IPD meta-analysis, evaluation of methodology, and external validation study.
Funder: Department of Health and Social Care

Abstract: Research Question Based on their individual characteristics and circumstances, what is the range of likely outcomes in adolescence or adulthood for a child with a diagnosis of autism spectrum disorder (ASD)? Background Autism spectrum disorders (ASD) is a neurodevelopmental condition which effects 700,000 (1%) people in the UK. ASD is highly heterogeneous with varying levels of severity of symptoms and differences in co-occurring conditions. Mapping out future outcomes is challenging for professionals and parents and currently no personalised prognostic tools exist to aid this task. Prognostic modelling for children with ASD poses statistical challenges: repeated assessments are required as a child develops; outcomes are measured using continuous measures and it is important to estimate prediction intervals, describing the uncertainty in any predictions. Prediction from repeated measures of prognostic factors is referred to as dynamic prediction. There has been limited application of methods for dynamic prediction to continuous outcomes, and the calculation of prediction intervals from dynamic prediction models. Aims We aim to develop prognostic models to predict behavioural and emotional outcomes in adolescence and a broad range of measures summarising different aspects of outcomes in adulthood. Methods We will consult with autistic adults, parents of autistic children and professionals working with autistic children to ensure that the prognostic tools we develop are able to meet the needs of these groups. We will conduct an individual patient data (IPD) meta-analysis to develop prognostic models. A systematic review will identify relevant data sets for which we will seek to obtain individual participant data. In the first instance we will develop prognostic models with prognostic factors measured at a single timepoint, using multiple linear regression. Prediction intervals will be calculated using non-parametric methods. Methods for dynamic prediction including frequentist and Bayesian mixed effects models and methods for calculating prediction intervals will be evaluated using simulated data. The best methods will be applied to develop further prognostic models. We will conduct external validation with new data collected from autism services at the South London and Maudsley NHS trust. Timelines for delivery Stakeholder engagement will be ongoing throughout the fellowship. In the first 9 months I will conduct the systematic review and set up the external validation study. Months 10-24 will focus on methodological aspects and obtaining and cleaning data for the IPD meta-analysis. Prognostic models will be developed in months 24-30. The final six months of the fellowship will consist of analysis of external validation data, writing up of the thesis and dissemination. Anticipated impact and dissemination Where prognostic mode we develop have undergone successful external validation we will look to integrate them into routine clinical practice in parallel with the MyHealthE system, an existing digital platform for collecting data on mental health symptoms. Methodology for dynamic prediction with continuous outcomes will have applications across many clinical areas. Dissemination will include presentations at national and international meetings, publications, and short videos explaining the output of the fellowship. Autistic people and parents of autistic children will be consulted to ensure that communication is appropriate.

Lay abstract: Aims Our aim is to develop tools that will predict future characteristics and experiences of autistic children. We will make predictions about a child's teenage years and early 20s. These predictions will be personal, based on the child's individual circumstances. We will provide ranges around the predictions that express how confident we are in the prediction. For example, if we are less certain, the range will be wide, showing that outcomes very different to the prediction may be likely. As we develop our models we will encounter statistical problems which have not been addressed before, or where solutions have only been applied in very different settings. This fellowship will evaluate methods which can overcome these challenges. Background Autism spectrum disorder is a condition which causes people to find social communication and interaction difficult. It can cause people to feel overwhelmed in daily life and engage in repetitive or restrictive behaviour. Autism effects around 700,000 individuals in the United Kingdom. It is described as a spectrum due to the different way autism can effect every person. There can be variation in the profoundness of autism symptoms, and differences in other difficulties that people experience. For example, autistic people are more likely to experience learning difficulties or problems with attention and hyperactivity. These differences make describing the future impact of autism on a child challenging. Currently no tools exist to help describe what teenage or adult life may be like for an autistic child. Design and methods We will aim to develop our tools from all existing data that is relevant to the predictions we will make. To find datasets I will conduct a systematic search of published scientific articles, looking for datasets which followed autistic people from childhood into their teenage years or early 20s. In the teenage years we will predict behavioural and emotional problems. These include unhappiness or worry, issues around anger or challenging physical behaviour, and difficulties concentrating on particular tasks. In the early 20s we will predict a wide range of characteristics and experiences, attempting to describe many things that may be important to an adult. This will include the ability to complete daily tasks, whether a person is likely to have a job, and problems like the ones we predict in the teenage years. Statistical techniques we apply will account for systematic differences between datasets, use information from repeated assessments made as a child grows, and use methods that avoid unrealistic assumptions when producing descriptions of how uncertain a prediction may be. We will collect new data to allow us to see how good our predictions are for people whose data was not used to develop the models. This data will include children and adolescents similar to those who may use the tools in routine practice. Patient/service user, carer and public involvement Throughout the fellowship we will consult with autistic people and parents of autistic children. We will discuss whether predictive factors are sensible and acceptable, communicating the results of predictions, and making sure we think about possible negative consequences of making predictions. Dissemination Results will be published in scientific journals and presented at conferences including the Autistica research festival and the International Society for Autism Research conference. Successfully developed tools will be added to MyHealthE, an existing computer system that is used by families and clinicians. We will work with the autistic people and parents of autistic children that are involved in the fellowship to effectively communicate the results to a wide audience. We will create short videos to explain what prediction is and how we will describe our confidence in predictions.

Research activity codes: 2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: MENTAL HEALTH