NIHRDataInsights/HRCSData huggingface dataset:
Title: SITAR growth curve analysis - a statistical method to assess longitudinal growth and development data
Funder: Medical Research Council

Abstract: The SITAR method of growth curve analysis summarises individual growth patterns of e.g height or weight in three parameters (size, tempo and velocity) that are estimated as subject-specific random effects, plus a cubic spline estimate of the average growth curve. It explains over 95% of the age-specific variance in the outcome measure, and as such is an effective summary of individual growth patterns. The subject-specific random effects can then be related to earlier growth-affecting exposures or later health outcomes. Thus it is relevant in translational medicine and life course epidemiology. The aims of the project are (i) to further develop the SITAR methodology, (ii) to apply it to a series of existing growth studies, and (iii) to develop an existing SITAR software library in the R statistical language for its wider dissemination. Methodological developments will include applying the method to developmental as well as growth outcomes, and allowing the fitting of multiple mean curves rather than just one. The application studies are collaborations involving longitudinal data covering height, weight, spirometry and brain growth in childhood and puberty. The R library allows other statisticians and epidemiologists to apply the method relatively simply.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Methods and tools for structural models integrating multiple high-throughput omics data sets in genetic epidemiology
Funder: Medical Research Council

Abstract: In recent years, new methods for biological measurements using sophisticated technologies (genomics, epigenomics, transcriptomics, proteomics, metabolomics) have enabled the simultaneous measurement of thousands of potential molecular biomarkers of disease. Many biomarkers reflect environmental exposures, including lifestyle, occupational and dietary factors, and thus serve to study in a comprehensive way the interaction between genes and environment in relation to disease outcomes. Structural equation models (SEM) provide an ideal framework for extending current individual analyses of "omics" data sets to deal with integration of multiple omics data sets with clinical outcomes and environmental and lifestyle risk factors. Our aim is to combine SEM with Bayesian variable selection priors, in order to select small sets of biomarkers most relevant to the disease pathways of interest. The implementation of the models will build on our existing multi-variate regression software, hence taking advantage of the years of work that have produced the ability to analyse very high-dimensional data, whilst robustly accounting for the multi dimensional aspects of these data. The methods will be developed with reference to case studies from the North Finnish Birth Cohort, whose Principal Investigator is a co-Investigator on this project. We will also benefit from collaborations with the Airwave Health Monitoring Study and the European Prospective Investigation into Cancer and Nutrition cohort, both of which are hosted at Imperial.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Joint modelling of multivariate longitudinal and event time outcomes in clinical research
Funder: Medical Research Council

Abstract: Due to current trends in medical practice towards personalised medicine, models that utilise all available information at a specific time point during patient's follow-up are proven to be quite valuable for physicians to gain a better understanding of the disease dynamics and ultimately take the most optimal decision of patient's treatment at that follow-up time point. We propose to develop a novel methodology to enable construction of prediction indices at the individual patient level combining the information at different time points on the multiple dimensions of longitudinal biomarkers and clinical endpoints. Although limited number of studies were extended for multivariate dimensions of longitudinal biomarkers and event times focusing on specific aspects of motivating clinical studies, the focus was on estimating the association between the two processes, rather than producing individual and time-specific predictions of the event conditional on biomarker in combination. Most extensions rely on strong parametric assumptions regarding the two processes or are based on computationally complex techniques. A major drawback of above studies is the lack of user-friendly, readily available software that implement the models in real time. Further, the potential applications of this methodology within pharmacogenetic association studies, where true genetic effect on a patient's response to drugs is evaluated, have never been exploited. Our proposal is built around above limitations within the development and application of multivariate joint modelling; aiming to develop a novel, but sufficiently flexible model by exploiting the underlying dependencies between multivariate dimensions of longitudinal and event time outcomes through correlated random effects; discuss a novel application of the proposed model in pharmacogenetics studies, and implement software in R and develop a training package to ensure practical relevance of models in mainstream medical research.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Automated phenotyping to accurately infer functional variants in clinical genetics
Funder: Medical Research Council

Abstract: We will pursue three primary methodological approaches required to allow us to leverage CFPS together with genotype data to aid clinical diagnoses. 1. Develop methods to allow clinicians to explore and interpret data in CFPS. We will build on the foundation of the CFPS pipeline to return clinically meaningful data interpretations. This will require a 2nn based and Bayesian Risk framework for estimating the probability that two faces show similarity. Using dimensionality reduction approaches such as Stochastic Neighbour Embedding we will create navigation and visualisation software to explore informative vectors in CFPS. We will develop a likelihood based method for assessing consistency within groups to detect endophenotype stratification within syndromes. 2. Infer causative genotype-phenotype associations by integrating functional gene networks within the CFPS. Next, we will develop methods to integrate DNA variation into CFPS. This will allow the automatic analysis of craniofacial phenotypes to predict the likelihood of functional pathways being causally disrupted in syndromes. We will use functional genetic pathway information to improve the predictive power of CFPS for clinical genetics and, vice versa, we will also use CFPS to enhance the power of functional gene networks in computational biology. To explore relationships between facial similarities (CFPS) and gene functional similarities we will use machine learning to alter the structure of informative vectors in CFPS to create a honed Functional Genetic CFPS (fgCFPS). 3. Create a robust statistical framework to infer perturbations in functional genetic pathways from facial phenotypes. Finally, we will develop methods for querying fgCFPS to aid the understanding of complex genetic disorders with unknown aetiology. From the new fgCFPS we will develop methods that assign likelihood estimates for the contributions of functional pathways to syndromes where causative genetics are unknown or complex.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: HMD: Missing data in propensity score analyses of Electronic Health Records Data
Funder: Medical Research Council

Abstract: Propensity score methods are often used to assess treatment effects in observational data, particularly where a large number of confounding variables need to be accounted for. However, the confounding variables often have a non-trivial proportion of missing values. Methods for handling missing data within propensity score analyses are relatively under-investigated; while missing data methodology is now well established for a range of standard substantive scientific models, this does not necessarily directly translate to the propensity score context, due to the differing ways in which the confounders are used. Fundamental to analyses of partially observed data is the accessible framing of the additional assumptions entailed, and statistical methods for valid inference under these assumptions. This project brings this approach to this setting through (i) evaluation of missing value indicator methods; (ii) development of multiple imputation strategies consistent with propensity score methodologies; (iii) using these to lift the restrictions on our doubly robust estimators (robust to misspecification of confounding or substantive models) via doubly robust multiple imputation, and (iv) applying the methodologies to the practically important area of time varying confounding. Through the dissemination strategy outlined elsewhere (including development of software) our research will enable practitioners to: (1) Frame appropriate assumptions regarding missing data in the context of their data and research questions; (2) Understand the impact of these on the validity of complete records and missing indicator type analyses; (3) Choose appropriate multiple imputation models consistent with the substantive propensity score model; (4) Apply methods with a degree of robustness to misspecification of key components of the propensity score analysis; (5) Understand, and clearly report, the assumptions, strengths and limitations of the analyses performed.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Evidence synthesis of diagnostic test performance from a decision-making perspective
Funder: Medical Research Council

Abstract: Aims: To evaluate, improve and develop methods for (i) making better use of 'all available data' in meta-analyses of diagnostic test accuracy, (ii) informing an economic decision model based on the synthesised evidence and (iii) comparing and choosing between competing diagnostic tests. Objectives: 1) To investigate the circumstances in which inferences are improved by accounting for between-study correlation in sensitivity and specificity. 2) To evaluate, and potentially to extend, methods for incorporating multiple points on the ROC curve from some studies into a meta-analysis. 3) To develop methods for incorporating explicit threshold values into the meta-analysis model, when available. 4) To investigate a range of methods for informing the decision model, and produce guidelines on the appropriate approach in different scenarios. 5) To develop methods for network meta-analysis of diagnostic test accuracy. 6) To investigate the validity of test comparisons based on less-than-ideal data, for example when tests A and B have only been evaluated in separate sets of studies. 7) To perform state-of-the-art analyses of multiple real examples. 8) To develop a 2-3 day short course, to disseminate high quality methodology. Methodology: I envision completing several of these objectives via extensions of the Rutter and Gatsonis (Statistics in Medicine, 2001) model for meta-analysis of diagnostic test accuracy, which is one of several suggested parameterisations for bivariate meta-analysis in this area. I will also draw on the literature on signal detection theory, economic decision analyses, and network meta-analysis. Scientific Opportunities: 1) The availability of improved methodology in this area to statistical analysts 2) Better summary evidence on the accuracy of diagnostic tests, and the relative accuracy of competing tests. 3) Improved, more cost-effective, decision-making regarding testing strategies 4) My own professional development


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Developing diagnostic methods for clinical genetics - phenotyping from faces in photos.
Funder: Medical Research Council

Abstract: Aim 1. Enable complete phenotype descriptions from photos for clinicians. I will adapt existing computer vision and machine learning methods for the specific purpose of obtaining phenotype descriptions. CFPS already performs exceptionally well given the limited information that it exploits thus far. Developing the accuracy of craniofacial point annotations and feature descriptors for capturing phenotype characteristics, inferring 3D orientation from 2D images, and applying machine learning approaches will ensure that CFPS reaches its full potential. Aim 2. Robustly predict causative mutations from DNA variants and CFPS. I will lead research that will aid genetic diagnoses with CFPS. The collective body of work to bring CFPS and DNA variant data together is essential for CFPS to become ready for clinical genetic data. To do this I will create family representations to differentiate between de novo and inherited phenotype. With a vector representation of a face during development CFPS will be able to capture and predict disease progression. I will also put in place a robust statistical framework to infer causative DNA variants in rare de novo mutation diseases to empower the search for ultra-rare syndromes. Aim 3. Develop the big data framework for implementing CFPS in global healthcare. Current ethical, law and data security concerns are a major hurdle in the path of bringing CFPS to patients. Within the scope of this Fellowship I will address each of these challenges. Patient images through collaborations and patient contributions will involve applying for REC approval to ask for consent and for people to contribute photos from their family albums. This will be both an exercise in public engagement but also en masse data collection. I will be working with ethics, law and eHealth data management questions to enable CFPS to develop into a form which will be not only powerful, but also usable in clinical settings.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: GENERIC HEALTH RELEVANCE
Title: Developing a wellbeing measure for public health evaluations: integrating capabilities and happiness
Funder: Medical Research Council

Abstract: The nature of public health interventions raises methodological challenges for their evaluators. One of the challenges is how to measure and value outcomes 'beyond health'. The aim of this fellowship is to develop a broad outcome measure based on two wellbeing approaches (capabilities and happiness) for the evaluation of public health interventions in a low-income setting. The research is structured in four sections. 1. To explore the determinants of happiness in Malawi. A microeconometric model will be built using happiness as outcome and with a number of explanatory variables. A series of multivariate regression analyses will be performed to explore the association between happiness and health status, material wealth and socio-economic background. 2. To examine the conceptual and empirical relationship between the capability approach and happiness. The methods will draw on latent variable structural models to explore whether and which capabilities have an impact on happiness. The model will specify a system of equations for jointly explaining happiness and capabilities. It will also allow for latent personality traits affecting both, to take account of any unobservable heterogeneity. 3. To develop a multidimensional measure that combines both approaches to wellbeing. Building on methods developed during my PhD, a series of focus groups with women of reproductive age will be organised with the Public Health Foundation of India to explore the concept of wellbeing. 4. To explore the feasibility of using the wellbeing measure in the evaluation of a community based maternal depression programme in India. One of the methodological challenges will include how to aggregate the changes in wellbeing for people whose initial wellbeing is low with the changes for those whose initial wellbeing is high. The wellbeing measure developed during the fellowship will be ready to be used for the evaluation of public interventions targeting women of reproductive age.


Research activity codes: 3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING;8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE
Title: Understanding the risks of polypharmacy: How important are drug interactions?
Funder: Medical Research Council

Abstract: Modern medicine is characterised by an increasing trend towards polypharmacy, the prescription of multiple concurrent medicines to an individual. However, polypharmacy has been associated with worsening health outcomes in recent research. It is possible that this excess harm is due to an increase in the risk of drug interactions. These are not well studied in clinical trials which are underpowered for rare outcomes and are usually conducted in younger, fitter patients than those who are prescribed the drugs in real life. I aim to investigate the population level impact of drug interactions in primary care. My objectives are: 1. To describe the prescription pattern of pairs of drugs with a potentially serious interaction 2. For three frequently co-prescribed drug pairs where a potentially serious interaction has been described, to quantitatively estimate the effect of this interaction 3. To directly or indirectly measure the reduction in the intended beneficial drug effects due to an interaction I will use the CPRD, a large database of primary care electronic health records, to conduct this research. I will use cross-sectional studies conducted at several time points to achieve the first objective. For the remaining objectives, I will use cohort and case only methods to conduct this research. The latter benefit from being inherently resistant to time invariant confounding. I will also use a variety of other techniques to try and reduce the possibility for confounding including propensity scoring, sensitivity analysis and negative control studies. Medical benefits of this work include providing quantitative data on commonly prescribed drug pairs that is valuable information at the point of prescription. Scientific benefits include providing empirical estimates of the effects of drug interactions that are often only predicted on theoretical grounds and also pioneering new techniques, such as the case only methods, in the study of drug interactions.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Investigating the determinants of health worker performance in Senegal
Funder: Medical Research Council

Abstract: In many Low- and Middle-Income Countries were the performance of health workers is low, governments are increasingly seeking to make sure that the way in which health workers are paid encourages them to work more efficiently and effectively. To that aim, interest in payment mechanisms linking part of the remuneration to achieving certain performance targets (often termed Performance-Based Financing or PBF) has recently grown. Many are hopeful that these schemes will improve health outcomes in places where, despite huge investments, they remain low. Despite this enthusiasm, there is limited evidence on the effects of PBF, and no understanding of whether and how financial incentives improve the performance of health care workers. The research aims to evaluate whether and how PBF contributes to improved health worker performance in Senegal, where it will take advantage of the introduction of a PBF programme in a quasi-experimental manner. Following a process evaluation that will help determine the degree of implementation of the programme, a large cross-sectional survey will be carried out to collect a broad range of health workers' performance outcomes in control and PBF facilities. Comparing these outcomes between health workers working in facilities with and without PBF, we will be able to determine the causal impact of PBF on health workers' performance. As the survey will also collect information on health workers' motivation and working conditions, we will be able to understand some of the causal pathways through which any changes occur. Findings from the project will not only inform the national discussions around the scale-up of PBF in Senegal, but they will also feed into the policy debates on human resources performance in other countries and on the global health scene. This will be achieved through an active research uptake plan that will include the production of policy briefs, the organisation of workshops and a number of networking activities.


Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES;8.2 HEALTH AND WELFARE ECONOMICS
Health categories: GENERIC HEALTH RELEVANCE
Title: Geostatistical methods for disease risk-mapping
Funder: Medical Research Council

Abstract: The overall aim is to develop and apply novel geostatistical methods for disease-risk mapping. Specific objectives A. Combining data from multiple spatially referenced surveys, including a mix of randomised and non-randomised surveys; B. Geostatistical modelling of zero-inflated prevalence data; C. Multivariate spatial modelling of risk and exposure when exposures are measured incompletely and/or with error. D. Development of efficient computational procedures for analysis of large spatial data-sets. Methods In order to pursue objectives A, B and C, a review will first be carried out so as to identify existing statistical methods for each specific issue. New statistical models and associated likelihood-based methods will then be developed as discussed in 'Case for support'. Simulation studies will be conducted under different suitable scenarios in order to quantify the benefit of the newly developed methodology over existing approaches, in each objective A, B and C. Applications to real data-sets provided by our collaborators will also be conducted. For objective D, two different approaches will be explored: 1) low-rank approximations of spatial processes based on convolution kernel representations; 2) ''tapering'' techniques, i.e. computational procedures that treat observations at locations sufficiently far apart as independent responses. The resulting algorithms will then be used to fit the proposed models to the (simulated and real) data, and compared with existing approaches that use analytical approximations, such as [1]. Opportunities The developed methodology will mainly be applied to disease prevalence mapping of tropical diseases. Throughout the fellowship, we will be alert to new collaborative opportunities. For more details see 'Collaboration Explanations'. References [1] Rue H., Martino S. and Chopin N. (2009) Approximate Bayesian Inference for Latent Gaussian Models Using INLA. JRSSB, 71, 319-392.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE