Title: Application anD Development of Statistical Methods to model complex Associations in meta-analysis for intervention and Risk predicTion research (ADDSMART)
Funder: Department of Health and Social Care

Abstract: BackgroundCombining evidence from multiple clinical studies can give a wider and more complete picture of the body of evidence. Studies are usually identified through systematic reviews, where the investigator methodically searches previous research on a pre-specified question. Combining the results from individual studies statistically is termed aggregate meta-analysis. Limitations of this approach could be addressed by retrieving and re-analysing the original datasets.Sharing datasets has become increasingly popular and is often expected by funders or journals. Combining and re-analysing shared datasets at the patient level is called Individual Participant Data (IPD) meta-analysis. IPD meta-analysis is superior to standard meta-analysis in its ability to address confounding. It is possible to link participants' individual characteristics with the outcome and therefore investigate potential effects in patient subgroups that might be missed used standard meta-analysis. Despite the advantages shown and attention, this relatively novel study design has received in previous years, there is still uncertainty around the conduct of such in IPD and there are recognized limitations with the statistical methodology used for IPD in both intervention and prognostic research.Most clinical studies ignore complex associations when investigating effects, as they lack the power to do so. Complex relationships arise in intervention and prognostic research; including interactions, effect modifiers or non-linear terms. For example, risk of preterm delivery is highest among underweight and obese women (at BMI extremes). Ignoring complex associations means that potential effects are only assessed for an average patient, and ignores the possibility that different patients might experience differing intervention effects. Grouping patient populations and investigating subgroup effects has been shown to have serious limitations, such as a reduction in statistical power and an undue dependence on the actual cut-offs chosen to define subgroups. Applied medical research such as IPD meta-analysis should allow for more accurate modelling of complex associations, however methodology is currently lacking.Aims and objectivesTo identify and improve IPD meta-analysis methods, enabling the effective assessment of complex associations in intervention and prognostic research. Identify methods to assess complex associations Evaluate the methods identified using three databases Confirm that identified methods are valid under different assumptions and how they might be improved Identify approach to combine complex methods when using IPD in prognostic research Plan of investigationThrough a systematic review I will identify methods for modelling complex associations in IPD meta-analysis. This will include how to examine and estimate non-linear terms and interactions in an IPD project, and how to examine non-linear associations within treatment-covariate interactions (such as treatment-effect modifiers). Using three datasets as well as simulations, I will assess the performance of published methods and then suggest improvements.Benefits to patients and the NHSIn an environment of limited resources health interventions need to be increasingly targeted to the specific patient populations where they provide the greatest gain and are therefore most cost-effective. Assessing complex associations in applied medical research allows a more precise identification of such populations and provides more accurate estimations of risk in a prediction model.

Lay abstract: Lifestyle changes such as diet or exercise have been investigated for controlling weight gain during pregnancy and reducing the chance of problems like pregnancy-related diabetes or babies being born too small. However lifestyle changes might not always work in the same way for different women. Factors, such as ethnic background, whether a woman has had a baby before and how active a women is, can all make a difference on whether lifestyle changes can help prevent poor pregnancy outcomes.Many different types of lifestyle interventions for pregnant women have been studied in the UK and internationally. Unfortunately the results vary considerably which makes it difficult to find an answer to what works and for whom. There could be many reasons, such as: differences in the lifestyle changes tested, varying definitions of what is a poor pregnancy outcome or the characteristics of the women in the different studies. If, for example, a lifestyle change works better in younger women then the results of a study will depend on the ages of the women included. These conflicting results suggest the need for further research, but exposing more and more pregnant women and their babies to research is often thought to be unethical as well as being time consuming and expensive. In order to avoid running yet more new studies, researchers could combine the results from previous research. The best approach is to collect the actual datasets from the studies and re-analyse them. This is called an Individual Participant Data (IPD) meta-analysis and is the focus of this Fellowship proposal.Combining and re-analysing the results of previously conducted studies (called meta-analysis) provides many benefits to the NHS and patients. For example, using already collected data, helps the NHS save money and avoids exposing further patients to research. It also includes a much wider range of participants and interventions than a single study ever could - this makes recommendations for many more patients possible. For these and other reasons, meta-analysis studies have greatly increased over the last years and are likely to be used increasingly in the future.However, there are problems with IPD meta-analysis. Because of the range of women's characteristics and lifestyle changes tested in the individual studies, it is important to identify which women will benefit most from which lifestyle changes, and by how much. This involves complex statistical methods. Some approaches have been suggested but each has been shown to have flaws, and better methods are required.During this five year project I will focus on improving statistical methods to identify which subgroups of women benefit most from which lifestyle changes. Recently I have worked as the statistician on an IPD meta-analysis study, including over 12,000 women, to prevent obesity in pregnancy. This data will be used for my work. For example, I will show how the risk of bad pregnancy outcomes changes depending on a women's weight gain during pregnancy and other characteristics such as age. The methods developed will be applicable to many fields of health research and I will produce guidelines that can be used for IPD meta-analysis in general. I will confirm my findings both theoretically (statistical simulations) and on at least two actual examples of IPD meta-analysis.This work will benefit the NHS by providing more targeted advice, more quickly, while saving the cost of running new trials. It will support researchers and clinicians through better guidance. Patients will benefit by helping to ensure that they only receive interventions that have been shown to be effective for their specific characteristics.

Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: REPRODUCTIVE HEALTH AND CHILDBIRTH