Title: A methodological and applied study of the practical use of alternative methods for adjusting survival estimates in the presence of treatment switching in oncology trials to inform clinical policy and resource allocation decisions
Funder: Department of Health and Social Care

Abstract: Background Randomised controlled trials (RCTs) are used to produce evidence on the effectiveness and cost-effectiveness of new treatments, and intention-to-treat (ITT) analyses (which compare groups as randomised) are used to estimate the treatment effect. For ethical reasons treatment switching where patients randomised to the control group are permitted to switch onto the experimental treatment at some point during follow-up is particularly common in oncology trials where no other non-palliative treatments are available. This causes problems for ITT analyses because the separation of the randomised treatment arms is lost. If control group patients benefit from the experimental treatment, an ITT analysis will underestimate the true survival benefit associated with the new treatment. This could lead a cost-effective treatment to be regarded as cost-ineffective, and reimbursement may not be achieved, with drastic implications for future patients. The consequences of inappropriately allocated health care resources are lost lives, lost quality of life, and an inefficient use of scarce health care budgets. There is critical need for more robust and reliable analyses to adjust for treatment switching. My doctoral research used simulation studies to assess the performance of statistical methods for adjusting for switching. This demonstrated that the rank preserving structural failure time model (RPSFTM) produces very low bias when its pivotal common treatment effect' assumption is met, but that bias substantially increases when this assumption is violated. The inverse probability of censoring weights (IPCW) method produced generally low levels of bias, but became prone to extreme bias when switching proportions were very high (approximately 90%). A two-stage adjustment method was more stable, producing generally low bias across all scenarios. However, this method can only be applied in certain circumstances, and was prone to bias when switching proportions were high. Based upon my doctoral research, recommendations have been made on the use of adjustment methods. However, decision-makers remain reluctant to rely upon these methods, because key questions and uncertainties remain regarding their assumptions and characteristics. Aims The research question is: What analytical techniques and methods can be used to address barriers that currently restrict the use of treatment switching adjustment methods? The aim is to develop and test analytical techniques and methods that address four specific issues which have led decision-makers to be reluctant to rely upon adjustment methods: (i) methods to test the common treatment effect' assumption, and for estimating the bias associated with departures from it; (ii) investigating the bias associated with recensoring; (iii) techniques for selecting covariates for inclusion in IPCW and two-stage analyses; (iv) methods for adjusting for multiple switches. The objective is to develop an updated analytical framework on the use of adjustment methods. Plan of investigation The project will involve seven stages: Identify relevant case studies; Re-run previous simulation study to investigate bias associated with recensoring in RPSFTM and two-stage analyses; Review covariate selection approaches for IPCW and two-stage methods; Develop methods for testing the validity of the common treatment effect assumption, and for estimating the bias associated with departures from it; Formalise methods for adjusting for multiple switches; Putting into practice findings from stages 2-5 in three case studies; Produce analytical framework, complete reports/manuscripts for dissemination. Summary of benefits This study will involve methodological development and updated recommendations that will allow adjustment methods to be used more confidently. This is of benefit for any stakeholder who wishes to estimate the true effectiveness of a new treatment in trials affected by switching. Most critically, it will contribute to the ultimate aim of allowing trials to be designed and analysed in a way that protects the ethical treatment of trial participants, whilst minimising risk for future patients.

Lay abstract: Background When new drugs are developed governments have to decide whether or not they should be paid for within the public health system. The effectiveness of the drug and its value for money are considered. New drugs are tested against existing treatments (standard care) in randomised controlled trials (RCTs). In RCTs people are randomly split into two groups. One group is given the new drug and the other group (called the 'control group') is given standard care. The effectiveness of the new drug is usually determined using an intention-to-treat' (ITT) analysis. In an ITT analysis, outcomes (like survival) are compared between the two groups. In RCTs of new cancer drugs, people who are put in the control group are often allowed to switch onto the new drug. This is called treatment switching' and often happens if an early analysis to check how the trial is going finds that the new drug is working better than the standard treatment. When treatment switching happens the ITT analysis does not show the true effectiveness of the new drug compared to the standard treatment, because some of the people in the control group actually received the new drug. This means that the new drug might not look very effective, nor look like good value for money. As a result, governments might decide not to pay for the new drug, meaning that it is not available for future patients. This could mean that future patients live shorter, lower quality lives. We need to look for other ways than ITT to measure the effectiveness of the new drug when treatment switching happens to ensure new drugs are evaluated fairly. Previous work I have done a lot of work on different statistical methods that could be used instead of the ITT analysis. Overall, the methods are complicated and do not always produce reliable results. Because of this, governments and other decision-makers are worried about using them and often rely on the ITT analysis instead. I have looked at how well the statistical methods work in simulation studies'. In simulation studies the effect size' of the new drug can be set in a computer model, then the statistical methods can be tested to see how close they come to estimating this true' effect. This is helpful, but because the simulation study' is not real, it might not show how well the methods would work in real life. Aims The purpose of this study is to further investigate the use of the statistical methods, and how we can tell if they have worked well when we apply them in an actual RCT. The aim is to identify the situations in which the different methods can be applied, and when we can be confident in their results. This will help us to uncover how good a new drug really is when treatment switching has been allowed in an RCT. Design and methods I have identified four areas where problems with the statistical methods cause governments and other decision-makers to be worried about using them leading them to rely on the ITT analysis instead. I will study each one of these areas and will work to provide ways of solving the problems. The solutions will then be applied to three real-life RCTs, to show how the statistical methods should be used to adjust for treatment switching. I will provide guidance to help researchers who need to use these methods to analyse future RCTs. Patient and public involvement This project will help us to find out how effective new treatments really are when treatment switching is allowed in RCTs. This is in the best interests of trial participants and the public. This will mean governments can make better decisions about new drugs, and therefore will protect future patients. Two patient representatives have helped in the planning of this project, and will carry on advising me from the patient viewpoint. This is important because RCTs may need to be run differently, particularly in how data is collected and when switching can occur. If that is the case, having the service user's perspective on what should be done is very important. Dissemination I will publish at least three reports about the different parts of this project. The patient representatives will advise on how the results should be disseminated to patients and the public, as well as to doctors and other researchers. This work will be presented at relevant conferences in the UK and around the world.

Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: CANCER AND NEOPLASMS