Late-stage pharmaceutical R&amp;D and pricing policies under two-stage regulation

Jobjörnsson, Sebastian; Förster, Martin; Pertile, Paolo; Burman, Carl‐Fredrik

doi:10.1016/j.jhealeco.2016.06.002

Cited by 19 publications

(26 citation statements)

References 34 publications

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“…However, I agree entirely with the authors, that as soon as practical decision-making involving economics is involved, it is the value of information that is important. In this connection, I can recommend the work of Forster, Pertile and colleagues 5,6 . See also Burman et al 7 Thus, I think to make good their claim, the authors would, at the very least, need to simulate from a universe in which the intervention was not necessarily better than the control.…”

Section: Grant Informationmentioning

confidence: 96%

“…The chief claim of this paper is that an RCT designed to test a hypothesis using traditional rules of inference might have more participants than required, if the goal is to make a good decision. Waste in research arises from routine use of arbitrary levels of statistical confidence 5 and because the trial data are considered in isolation 6 . The marginal value of the information acquired for the purpose of making a good decision is not made explicit.…”

Section: Introductionmentioning

confidence: 99%

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Smaller clinical trials for decision making; a case study to show p-values are costly

et al. 2018

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Background: Clinical trials might be larger than needed because arbitrary levels of statistical confidence are sought in the results. Traditional sample size calculations ignore the marginal value of the information collected for decision making. The statistical hypothesis testing objective is misaligned with the goal of generating information necessary for decision-making. The aim of the present study was to show that for a case study clinical trial designed to test a prior hypothesis against an arbitrary threshold of confidence more participants were recruited than needed to make a good decision about adoption. Methods: We used data from a recent RCT powered for traditional rules of statistical significance. The data were also used for an economic analysis to show the intervention led to cost-savings and improved health outcomes. Adoption represented a sensible investment for decision-makers. We examined the effect of reducing the trial’s sample size on the results of the statistical hypothesis-testing analysis and the conclusions that would be drawn by decision-makers reading the economic analysis. Results: As the sample size reduced it became more likely that the null hypothesis of no difference in the primary outcome between groups would fail to be rejected. For decision-makers reading the economic analysis, reducing the sample size had little effect on the conclusion about whether to adopt the intervention. There was always high probability the intervention reduced costs and improved health. Conclusions: Decision makers managing health services are largely invariant to the sample size of the primary trial and the arbitrary p-value of 0.05. If the goal is to make a good decision about whether the intervention should be adopted widely, then that could have been achieved with a much smaller trial. It is plausible that hundreds of millions of research dollars are wasted each year recruiting more participants than required for RCTs.

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Section: Grant Informationmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Smaller clinical trials for decision making; a case study to show p-values are costly

et al. 2018

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“…Commercial drug development is heavily dependent on EU regulations, EMA decisions and national reimbursement decisions. IDeAl has demonstrated that if pharmaceutical companies experience non-transparency in such societal decision rules, such as uncertainty of how benefit/risk and cost/effectiveness are weighted, the industry will not be able to design the best possible trial programs [ 7 ]. Given a successful trial, it also models the sponsor’s pricing and the reimburser’s reaction to that.…”

Section: Level Of Evidence - Decision Theorymentioning

confidence: 99%

Lessons learned from IDeAl — 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials

Hilgers

Bogdan

Burman

et al. 2018

Orphanet J Rare Dis

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BackgroundIDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers.MethodThe description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project’s more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages’ output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials.ResultsThe results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl’s work as well as relating important methodologies by IDeAl’s definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials.ConclusionIDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.

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Section: Introductionmentioning

confidence: 99%

Smaller clinical trials for decision making; using p-values could be costly

et al. 2018

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Clinical trials might be larger than needed because arbitrary high Background: levels of statistical confidence are sought in the results. Traditional sample size calculations ignore the marginal value of the information collected for decision making. The statistical hypothesis testing objective is misaligned with the goal of generating information necessary for decision-making. The aim of the present study was to show that a clinical trial designed to test a prior hypothesis against an arbitrary threshold of confidence may recruit too many participants, wasting scarce research dollars and exposing participants to research unnecessarily.We used data from a recent RCT powered for traditional rules of Methods: statistical significance. The data were also used for an economic analysis to show the intervention led to cost savings and improved health outcomes. Adoption represented a good investment for decision-makers. We examined the effect of reducing the trial's sample size on the results of the statistical hypothesis-testing analysis and the conclusions that would be drawn by decision-makers reading the economic analysis.As the sample size reduced it became more likely that the null Results: hypothesis of no difference in the primary outcome between groups would fail to be rejected. For decision-makers reading the economic analysis, reducing the sample size had little effect on the conclusion about whether to adopt the intervention. There was always high probability the intervention reduced costs and improved health.Decision makers managing health services are largely invariant Conclusions: to the sample size of the primary trial and the arbitrary p-value of 0.05. If the goal is to make a good decision about whether the intervention should be adopted widely, then that could have been achieved with a much smaller trial. It is plausible that hundreds of millions of research dollars are wasted each year recruiting more participants than required for RCTs.

show abstract

Late-stage pharmaceutical R&D and pricing policies under two-stage regulation

Cited by 19 publications

References 34 publications

Smaller clinical trials for decision making; a case study to show p-values are costly

Smaller clinical trials for decision making; a case study to show p-values are costly

Lessons learned from IDeAl — 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials

Smaller clinical trials for decision making; using p-values could be costly

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