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, 6 (3), 125-132

From Clinical Trial Efficacy to Real-Life Effectiveness: Why Conventional Metrics Do Not Work


From Clinical Trial Efficacy to Real-Life Effectiveness: Why Conventional Metrics Do Not Work

Jean-Pierre Boissel et al. Drugs Real World Outcomes.


Background: Randomised, double-blind, clinical trial methodology minimises bias in the measurement of treatment efficacy. However, most phase III trials in non-orphan diseases do not include individuals from the population to whom efficacy findings will be applied in the real world. Thus, a translation process must be used to infer effectiveness for these populations. Current conventional translation processes are not formalised and do not have a clear theoretical or practical base. There is a growing need for accurate translation, both for public health considerations and for supporting the shift towards personalised medicine.

Objective: Our objective was to assess the results of translation of efficacy data to population efficacy from two simulated clinical trials for two drugs in three populations, using conventional methods.

Methods: We simulated three populations, two drugs with different efficacies and two trials with different sampling protocols.

Results: With few exceptions, current translation methods do not result in accurate population effectiveness predictions. The reason for this failure is the non-linearity of the translation method. One of the consequences of this inaccuracy is that pharmacoeconomic and postmarketing surveillance studies based on direct use of clinical trial efficacy metrics are flawed.

Conclusion: There is a clear need to develop and validate functional and relevant translation approaches for the translation of clinical trial efficacy to the real-world setting.

Conflict of interest statement

Jean-Pierre Boissel, Frédéric Cogny and François-Henri Boissel are employees and shareholders of Novadiscovery, which currently has a patent pending on the Effect Model Business Applications. Nicholas Marko has no conflicts of interest that are directly relevant to the content of this article.


Fig. 1
Fig. 1
Distribution of risk without treatment (Rc) in three simulated populations, A, B and C, each comprising 100,000 individuals who were all assumed to have the same disease and, therefore, were all at risk of a clinical event but the event rates in the untreated individuals (Rc) differed in each population
Fig. 2
Fig. 2
Variation of absolute benefit with risk without treatment for two drugs (1 and 2) in the same population A. a The absolute benefit (AB) as a function of the risk without treatment (Rc) in population A for drug 1: b AB as a function of Rc in population A for drug 2

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