If you are a payer or a policymaker, should you except the results of a global clinical trial or require additional evidence that a drug works in your country.\u00a0 There are a few options to consider. <\/p>\n
Delay approval until local confirmatory trial<\/strong>.\u00a0 Approach enacted in several Asian countries (see Habr et al. 2024<\/a>).\u00a0 While this policy reduces uncertainty, it delays access to potentially life-saving therapies. Conditionally adopt<\/strong>.\u00a0 In this case, patients gain access, which payers wait for post-market confirmatory trial is conducted in parallel.\u00a0 While this approach is good in theory, manufacturers may be incentivized to delay trial completion and enforcement is often weak (see Drummond et al. 2022<\/a>)Approve<\/strong>.\u00a0 Gets drugs to patients sooner, but does not resolve uncertainty. Reject<\/strong>. Strongest demand for evidence and least expensive, but most problematic for patients with high unmet needs\/few treatment options.\u00a0 <\/p>\n Clearly, if you have a very large clinical trial, with very A paper by Liang and Jiao (2026)<\/a> aims One key contribution of this paper is that they explicitly What do the authors find? <\/p>\n When uncertainty is high (alpha<0.3, delayed adoption pending shorter trial durations (1-2 years) mitigate the costs of When additional delays [in confirmatory trial completion] Reducing the launch price by 20% to 30% below the
\nstrong results for a new medical technology that is not that costly, you should
\nlean towards approving or conditional adoption.\u00a0
\nIf the clinical trial has small sample size, weak results and the
\ntreatment is expensive, you would lean more towards delayed approval or rejection.\u00a0 How can we formalize this decision?\u00a0 <\/p>\n
\nto create a decision analytic framework using a value of information (VOI)
\nmethodology to measure the pros and cons of each approach depending on the
\nquality of evidence among other factors.\u00a0
\nSpecifically, the authors use the power prior approach (see Ibrahim et al 2015<\/a>),
\nwhich is a flexible Bayesian method that down-weights external evidence based
\non its perceived relevance to the local context. \u00a0The methodology allows users to put an
\nexponent (alpha) on the Bayesian prior (the results from the trial) where if exponent
\nequals 1, then the external and local data are considered interchangeable; if
\nthe exponent equals 0, then the external data is discarded and only local data matters.\u00a0 In between, exponents closer to 1 give more
\nweight to the external trial data; those closer to 0 give less weight to
\nexternal data. Note that the model does not model jurisdiction specific
\ntreatment effects, but focuses on uncertainty in the applicability of the
\nexternal data to local contexts. <\/p>\n
\nmodel that conditional approval requiring a local trial weakens manufacturers incentives
\nto expedite the local trial (since the drug is already covered until the local
\ntrial results read out). <\/p>\n
\nlocal trial yields the highest benefits.\u00a0
\nHowever, once alpha exceeds 0.3, conditional adoption with local trial
\nrequirement (policy 2) becomes consistently optimal\u2026notably, when alpha >0.6,
\ndelayed adoption is even less favorable than immediate adoption without a trial
\nrequirement\u2026<\/em><\/p>\n
\nwaiting, making delayed adoption the preferred strategy at moderate to high
\nuncertainty levels. However, as trial timelines lengthen to 5 to 6 years,
\nconditional adoption becomes increasingly favored. Longer trial durations
\namplify the opportunity costs of delay\u2026<\/em><\/p>\n
\nare modest (1-2 years), conditional adoption remains the preferred strategy
\nacross a wide range of uncertainty levels. However, as additional delays extend
\nto 4 to 6 years, delayed adoption becomes increasingly favorable, particularly
\nunder moderate to high uncertainty, as the prolonged period of unresolved
\nuncertainty diminishes the NB of early access\u2026<\/em><\/p>\n
\nvalue-based price significantly enhances the relative benefit of conditional
\nadoption, even when uncertainty is substantial\u2026<\/em><\/p>\n