Title: Investigating methods for inferring causality from observational data: an application to longitudinal cohort data
Funder: Department of Health and Social Care

Abstract: Background In health services research, it is vital to know whether an event, such as treatment or modifiable exposure, has a causal effect on the outcome to deliver the best treatments and health policy interventions to patients and the public. Due to limited evidence available from randomised controlled trials, policy makers are becoming increasingly reliant on supplementary information from observational data to evaluate the effectiveness of treatments and interventions. It is often unclear which methods for inferring causality from observational data are the most appropriate for any given situation. In particular, there is limited information on the applicability of causal observational analysis in time to event data.Aims The aims of this project are to answer the following: How can observational data be analysed to obtain causal (treatment) effect estimates? To extend recent methods for obtaining causal effect estimates from observational data to time to event outcomes To study the appropriateness of approaches to causal inference for a treatment effect, using the effect of statins on the development of type 2 diabetes as a test case, and for a modifiable exposure, using the effect of smoking on lung function as an example To assess the relative performance of different causal inference methods To inform future analyses of treatment effects and public health interventions in observational data Plan of investigation A variety of different methods will be used to answer the above research questions. The first will be answered by reviewing the current methods for estimating causal treatment effects from observational data and assessing the advantages, disadvantages and assumptions of each method.To address the second question, flexible parametric models, which are often used in survival analysis, will be used to extend the Aalen additive hazards instrumental variables model.The third question will be answered by applying different causal inference methods to observational data examples from CPRD (Clinical Practice Research Datalink) and EXCEED (Extended Cohort for E-health, Environment and DNA). These include propensity score methods, instrumental variables approaches and structural equation models.To address the fourth question, an extensive simulation study will be conducted to compare the different causal inference methods in terms of power, bias and coverage. The data will be simulated based on features of the case study data to ensure that the simulated data are as realistic as possible. Continuous and binary exposures will be considered along with a variety of endpoints including continuous, binary and time to event outcomes.Finally, a guidance document will be created outlining which methods seem the most appropriate in different scenarios based on the above investigations. This document will include a hierarchical ordering of the assumptions of each analysis method. Anticipated Impact This research will inform future analyses of causal treatment and exposure effects. In particular, policy makers will be better able to augment the information from trial data to assess the cost effectiveness of different treatments and hence to release innovative treatments to patients more quickly.

Lay abstract: Background: Health research aims to determine the most effective treatment for an outcome of interest to ensure that patients receive the best available treatments. Treatments can be compared using randomised trials or observational data. Randomised Trials: In Randomised Controlled Trials (RCT) patients are randomly allocated to different treatment groups, for example, based on the flip of a coin. This makes different groups similar in terms of factors such as patients' age and gender. RCTs are hence able to reliably estimate the treatment effects but they are costly to run, have selected participation and may fail to capture long-term effects. Observational Studies: Observational studies include data routinely collected by GP surgeries and hospitals as well as those gathered for other studies. Observational data is readily available and can address long-term effects due to regular follow-up. However, in observational studies researchers have no control over which treatments patients were given. Hence, the different treatment groups could differ in other ways leading to incorrect conclusions about treatment effects. For example, older people may be more likely to be prescribed drugs to control blood pressure than younger patients. To compare different treatments fairly, researchers need to consider such differences. Although there are plenty of statistical methods that adjust for differences between treatment groups in observational data, it is not clear which methods give the most accurate estimates of the true treatment effects. Researchers often want to measure the time until an event, such as hospital admission, occurs. There are fewer methods available to compare treatment effects in observational data when looking at time-to-event outcomes. A National Institute for Health and Care Excellence (NICE) report highlighted the need for further research into methods to obtain reliable treatment effect estimates from observational data. Aims and Plan of Investigation: This project aims to assess which methods obtain the most accurate estimates of true treatment effects. Firstly, I will review the methods used to adjust for differences between treatment groups in observational data and will compare their advantages and disadvantages. Secondly, I will extend methods to analyse the time until an event in observational data to enable estimation of treatment effects while correctly adjusting for differences between treatment groups. Different methods will be compared using two real examples. Previous studies indicate that patients taking statins may have a higher risk of developing type-two diabetes compared to patients that do not take statins. These studies used methods that may not adjust for all differences between treatment groups. I will use methods that can, in theory, adjust for such differences to estimate the effect of statins on the development of type-two diabetes. The aim is to provide guidance on the true risks of taking statins although the latter is unknown. The second example will estimate the effect of smoking on lung function. Effects of smoking are far better understood than the statin effect in the previous example and so robust methods to adjust for differences between smokers and non-smokers can be more confidently compared. I will then simulate (i.e. make up) data where I know the true' treatment effect. Using these data, I will assess which methods can estimate this `known' treatment effect most accurately. The simulated data will be created based on the examples above so that they look like real patient data. Finally, a guidance document will be created to inform researchers on the most reliable methods for estimating treatment effects from observational data in different real world settings. Anticipated Impact. This research will inform future analyses of observational data and ensure that researchers and clinicians can correctly assess which treatments are the best for patients.

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