Title: Improving the monitoring and analysis of harm outcomes in phase II and III pharmacology trials to facilitate early detection of adverse drug reactions (ADRs)
Funder: Department of Health and Social Care

Abstract: BackgroundIt is common practice for pharmacological clinical trials to sequentially test accumulating data in a series of interim analyses for both ethical and economic reasons, the aim being to stop the trial early if there is sufficient evidence to reach a definitive conclusion. In recent years more randomised controlled trials (RCTs) are stopping early for apparent benefit, however little is reported on the frequency of trials stopped early for harm and the basis for these decisions when they are.Data obtained from RCTs contributes important information to the harm profile of a drug as it provides unbiased estimates of harm effects and provides a controlled comparison of the rate of harms allowing causality to be evaluated. It is common practice when monitoring harm data in an ongoing trial to present tabulations of the cumulative number of harm events by treatment arm to the data monitoring committee. Similarly at the study end it is common to present harm data in journal articles as the cumulative number of harm events by treatment arm sometimes along with relative risks or risk differences and sometimes with p-values from formal hypothesis tests. However this is often limited to the most common events, where an arbitrary rule is used to define common events e.g. an event rate of at least 5% in the treatment group. Presentation of harms in this way is ambiguous and often relies on invalid assumptions. Additionally integration of existing knowledge (e.g. data from pilot, earlier phase trials) for drug harm is often not formally included.There is a need for a formal approach to monitor emerging adverse drug reactions (ADRs) as highlighted by several high profile post-marketing withdrawals of drugs due to patient harm. However, there are many challenges associated with assessing harms in clinical trials such as lack of power and issues of multiplicity.There are statistical methods proposed for monitoring harms in clinical trials however they are not routinely used and many trials continue to rely on descriptive presentations and simplified analysis.AimsThe aim of this research is to ascertain current practice for monitoring harm data from phase II/III pharmacological trials and to develop novel tests to flag signals for ADRs in controlled trials through a time-to-event framework taking advantage of the control arm to enhance inference. I will extend these tests to incorporate existing knowledge of drug harms through Bayesian methods. I will explore the performance of these tests through simulations and existing data from two pharmacological RCTs.Plan of investigation Conduct a survey of statisticians at UK Clinical Research Collaboration (CRC) registered Clinical Trials Units (CTUs) and pharmaceutical companies to determine prespecified plans and current practice for analysis and reporting of harm data. Develop signal detection tests for emerging and prespecified harms, incorporating prior knowledge of the drug harms under a Bayesian framework to flag signals for ADRs in controlled trials. Develop statistical programs for their implementation. Assess the power and utility of the tests and robustness of the statistical programs for implementation using simulations and data from two ongoing pharmacological RCTs. OutcomesThrough this research I will develop a practical, easily implementable, objective method that formalises incorporation of existing and cumulating knowledge of drug harm to efficiently identify emerging ADRs in clinical trials. This will lead to improved patient care for clinical trial participants and future patients through earlier identification of potential harms which can be used to guide stopping for emerging ADRs in clinical trials or flag harms for monitoring in post-marketing surveillance studies. In the longer term this will lead to more accurate risk evaluation and safer drug development for patients.

Lay abstract: AimsClinical trials look at new interventions (such as drugs) for the treatment of diseases, infections or disorders. Randomised controlled trials (RCTs) are considered the best method to test the performance of these new treatments. RCTs compare new treatments to existing treatments or dummy drugs called placebos to decide if they should be used in clinical practice. Patients that agree to participate in RCTs are randomly allocated to one of the treatments and their outcomes are compared. A lot of work has gone into identifying the best method to analyse how well these new treatments perform in terms of improving outcomes but the methods to analyse the harmful side-effects are not as well developed.This research aims to improve the detection and reporting of harmful side-effects associated with drugs in RCTs. This will enable earlier detection and improved reporting of harmful effects. This will result in improved care and quality-of-life for patients participating in RCTs as well as patients that may already be receiving the treatment as part of their routine clinical care.The specific aims are: To review current practice for monitoring harmful side-effects in ongoing drug studies. To develop statistical tests which can be used to signal for emerging harmful drug side-effects in studies. To assess how these tests perform in different trial situations. BackgroundThe harmful side-effects experienced by patients participating in RCTs are called adverse events (these could include headaches, vomiting etc.). The cause of these harmful effects may not be related to the trial drug. However, those known or suspected to be related to the trial drug are called adverse drug reactions.It is common for drug side-effect data to be presented simply as the number of adverse events for each treatment group. When comparing the treatments this approach is often misleading and relies on a subjective assessment i.e. what one person deems to be an unacceptable level or increase in events may be acceptable to another. However, the objective methods used to compare improvements in outcomes are not appropriate for side-effect data. They can either fail to pick up important differences resulting in more patients than necessary suffering a harmful drug effect or result in the trial being stopped too early due to a chance imbalance in harmful effects.Alternative ways to identify harmful drug side-effects in RCTs have previously been developed but have a number of their own limitations such as failure to use all of the data. Therefore many studies continue to report only the number of harmful effects.This research will develop new statistical tools to monitor harmful effects that incorporate existing knowledge on the side-effects of the drug. The aim is that the tool will provide a signal if harmful side-effects emerge in an RCT.Design and methodsI will conduct a survey of statisticians involved in clinical trials to find out how they analyse and report harmful effects identified in RCTs.I will explore statistical models to develop tests that will signal emerging harms in RCTs, using existing information from for example previous trials. I will assess the performance of the tests using data from two ongoing clinical trials and by running simulations.Patient and public involvementI have spoken to patient representatives from ongoing drug RCTs, previous trial participants and established patient advisory groups who are willing to provide support throughout my fellowship. I will seek their input on which harmful events are of importance to patients and at what scale (e.g. 1% of patients, 10% of patients etc. experiencing the side-effect) and severity (e.g. mild events that require no intervention, moderate events requiring minimal intervention etc.) they would want a signal to be raised. The outcome of these discussions will be used to develop sham trial situations that will be used to identify the best test(s) to flag signals for emerging harmful drug side-effects.I will also gain their feedback about how best to communicate the output of the developed tests to ensure the results are comprehensible for use not only in trial related reports but to the wider public and the best way to disseminate the research.DisseminationI will disseminate this work at public events such as the Cambridge Science Festival and user group meetings; and to clinical researchers and statisticians at local seminars and scientific conferences and in peer reviewed statistical and clinical publications. Over the course of this research I will work closely with my academic colleagues to inform the analysis of ongoing trials and the design of new trials.

Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION);6.1 PHARMACEUTICALS
Health categories: GENERIC HEALTH RELEVANCE