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Classical statistical inference testing has its advantages and restrictions, which has led to the development of other methods, e.g. clinical significance testing by Jacobson and co-workers^[105;106]. However, these methods are restricted in their use because of the assumptions made, e.g. that data are normally distributed, etc. There are several other concerning issues related to Jacobson’s proposal: the dysfunctional and functional distributions overlap; how do we distinguish dysfunctional cases with extremely “normal”

scores from non-dysfunctional cases? How do we decide what constitutes a functional or dysfunctional population^[101]? The clinical significance method developed by Jacobson is conservative in its use, but the idea of defining clinical significance was a breakthrough for methods, models and ideas to follow.

Descriptive methods, such as percentage of patients improving, frequency distributions, etc., provide other means in the assessment of clinical significance of data. To ensure a transparent and relevant assessment, clinical significance must be defined and justified from

assessment to assessment. As Greenstein^[100] emphasises: “Clinical trials are conducted to answer clinical questions, and clinical parameters are used to monitor outcomes; therefore, the results should refer to the importance of the clinical data…… Ultimately, it is incumbent on investigators to address the clinical importance of their data to help clinicians choose effective therapies, because reliance on hypothesis testing to define ‘clinically significant findings’ is inadequate.”

In Chapter 5, the benefit-risk assessment method developed during the PhD project will be presented. How clinical significance can be perceived and how this can be captured in clinical data will also be discussed.

5 A benefit-risk assessment approach

The aim and purpose is - in a simple and structured fashion - to compare benefits and risks on the same scale, and thereby support decisions under drug development and in a marketing authorisation application. It is therefore important that the assessment is as

transparent as possible and that focus is on the clinical significance and relevance of data.

The development of a novel methodology and visualisation tool for the benefit-risk assessment of medicines and treatments will be elaborated. Previously, several different methods and tools were discussed. Relevant aspects of these methods are taken into consideration and built upon to develop a tailored method for the benefit-risk assessment of medicines and interventions.

The goal is not to create a decision model, which gives a go/no-go answer, but a tool for the decision-making process. Subjective input into the assessment is needed, but stringent rules are applied to retain transparency in the assessment. The main focus in this thesis will be on the general framework, scoring of data and visualisation and communication of the result. The technical part, where data-driven methods are used for scoring, will be described, but the focus will be on how the results obtained by these technical methods can be used in a clinical setting and in a benefit-risk assessment.

The proposed methodology was developed as an iterative process, starting with a simple approach that was adjusted and built upon continuously. A preliminary method was firstly developed. A suitable project was identified and a project team was invited to a workshop to test the method. The project team was asked to identify which type of data they would like to include in the assessment. Often they chose 2-3 phase II and III clinical trials.

Every criterion was identified and listed. This included not only primary and secondary endpoints, but also, e.g. biochemical markers. The new drug was scored against the comparator using either qualitative or quantitative tools.

A workshop was then organised where the entire project team was involved, that is to say the project manager, the international medical director, the regulatory affairs associate, the

statisticians, the medical writer, the safety surveillance advisor, the clinical pharmacologist and the medical affairs advisor. The team was then presented to the benefit-risk assessment method and was later asked to define the decision context, criteria and assign weights. We then provided the objective scores and the team could revise the subjective scores. An evaluation of uncertainty was performed and the final weighted scores were presented using different visualisation tools. Finally, the team made the final conclusion of the assessment.

The team then evaluated the process and this valuable input was then used to adjust the method. This process was repeated, as mentioned previously, with four different drugs and a different project team. New issues emerged each time that had to be dealt with, hence the method was developed in an iterative fashion.

As illustrated in Figure 13, the proposed benefit-risk assessment method is structured as an eight-step successive process. This structure serves the purpose of transparency in the process and it contributes to reducing the effects of unintended bias and feedback. In the following, the rationale for each step will be described. Basically, the eight steps can be divided into 3 main groups:

1. Introductory steps in an assessment 2. Evaluation of data

3. Visualisation and communication of results

Figure 13: The eight successive steps in the benefit-risk assessment method.