Using Matched Cohort Methods to Investigate the Risk of Respiratory Diseases in Survivors of Adult Cancer

An increasing number of studies are being conducted using health data generated when a patient interacts with any healthcare service (e.g a GP or hospital visit). This data can be used to conduct comparative epidemiological studies to investigate the risk of adverse health outcomes in diseased individuals (exposed group) compared those without a history of disease (unexposed group). Matched cohort methods are commonly used in this setting to select the unexposed group. In this method an exposed individual is matched with one or more unexposed individuals on one or more patient characteristics (e.g age and sex). In theory, this will allow a less biased comparison between the exposed and unexposed cohorts as matching will make the groups more similar to each other. However, this assumption has not been tested; little is known about when one should use a matched cohort design, how the matching should be conducted and what characteristics should be used to match individuals.

The main aim of this project is to investigate a real-world epidemiological research question. Particularly, we aim to quantify the risk of respiratory diseases in individuals who have a history of cancer, compared to those with no history of cancer as part of a greater ambition of using big data to identify areas of disease prevention in cancer survivors

The secondary aim is to evaluate the best methods to conduct and analyse matched cohort studies for clinical research of long-term outcomes

This project will be conducted in four steps:
Step 1: Conduct a descriptive systematic literature review to systematically review the evidence on the association between cancer and subsequent respiratory disease. This step will be used to define the specific primary research respiratory outcomes of interest.

Step 2: Conduct a narrative review to understand how matched cohort methods are used in epidemiological research and the current reasoning behind selecting this study design as well as identify the current research gaps.

Step 3: Conduct a simulation study. We will generate an experimental simulated dataset which will act as a "controlled environment" in which we will be able to conduct experiments to test the assumptions that underpin the matched cohort design. The specific research questions that will be addressed will be identified from the narrative review (step 2). The aim of this stage is to create the most optimal matching strategies for matched cohort studies. The results of this stage will be used to develop open-access coding macros that can be used by other researchers to conduct matched cohort studies. This step will meet interdisciplinarity skills by developing data analytics technology that can be applied to many different research areas. Furthermore, this step will necessitate the development of high-level informatics skills by requiring advanced computational knowledge in order to generate simulation data.

Step 4: Apply the findings of step 2 & 3 to a real-world epidemiological question. Particularly, we would like to investigate the impact of cancer and its treatment on the risk of developing subsequent adverse respiratory outcomes in the growing population of cancer survivors in a real-world health dataset that captures data from millions of individuals. We will use matched cohort methods to address the research question, employing the coding macros built in step 3. This step involves the development of a variety of priority skills such as data analytics, big data visualisation and high-performance computing. Finally, a key aim of this stage will be the involvement of patient groups in research that concerns their own health. To this end, we will design a series of workshops with cancer survivors focussed specifically on obtaining a mutual benefit for both patients and researchers.