DOI: 10.25934/pr00009826
title: Development and application of new statistical methods for meta-analysis to evaluate treatment effectiveness and surrogate relationships in biomarker subgroups

description: Lung cancer is the third most common cancer in the UK, representing 13% of all new cancer cases. 80-85% of lung cancer cases are in non-small cell lung cancer (NSCLC). Ten-year survival for lung cancer in England is 9.5% and in the UK lung cancer is the most common cause of death due to cancer. 

Traditional treatment for NSCLC includes chemotherapy which aims to eradicate or reduce the tumour. However, chemotherapy treatments are not specific to cancer cells and can have a toxic effect on normal cells. Alternatively, targeted therapies target proteins that determine how cancer cells grow, divide and spread. Currently, in the UK, advanced NSCLC patients are routinely tested for a selection of genetic biomarkers (which are unique features of a patients’ DNA) to inform the best targeted treatment for the patient. 

Every new treatment which is developed is investigated to assess whether it is safe and effective. Ideally, a randomized controlled trial (RCT) will be conducted where patients are randomly assigned to receive the new treatment or a comparator and are followed up over time to assess their outcomes. As treatments become more targeted, they often become more effective but are only suitable for smaller populations of patients. This results in smaller RCTs which take longer to conduct and generates greater uncertainty around estimates of effectiveness, i.e., how well the new treatment works. 

When there is high uncertainty around effectiveness estimates obtained from a single RCT, it can be beneficial to combine data from multiple RCTs to estimate the overall treatment effect from all available studies. This is done using a statistical method known as meta-analysis. Combination of multiple trials in meta-analysis also allows researchers to investigate potential effect modifiers - things that influence how effective the treatment is, which are particular to individual patients (such as genetic biomarkers) that alter treatment effectiveness. 

In this project, we focus on different ways to estimate treatment effects in biomarker subgroups. We consider several different meta-analysis methods to combine data from RCTs to investigate effect modification. Some methods only use summary data such as the average treatment effect or the proportion of patients with a genetic mutation in the study. Other methods use individual participant data (IPD), which provide information on the clinical outcome or the characteristics for each individual in the study; for example, the exact combination of genetic mutations for each patient. 

To compare the performance of these different statistical methods, we will apply them to studies investigating treatments for NSCLC. The differences in genetic biomarkers between patients could potentially bias the treatment effect estimates. We hope that by illustrating how different statistical methods can be applied to a pre-existing dataset we will demonstrate how these methods can be successfully applied in future meta-analyses.