![]() ![]() 8 to identify relevant TAs for immunotherapies 9. The analyses presented in this paper are based on the systematic search of the NICE website conducted by Bullement et al. Our aim was to identify any features that may a priori suggest whether some of these methods would be more appropriate for long-term extrapolation under specific circumstances. For this, we fitted the models to the data used within each HTA submission and compared the generated outcomes with those observed in the longer follow-up survival data. Using the TAs identified as part of this prior review and the associated evidence base, we assessed the ability of standard parametric survival models, mixture cure models, and spline models to accurately capture longer-term survival outcomes. The review targeted TAs for immunotherapies in cancer indications. #Engauge digitizer more points in fit trial#8 assessed the accuracy of the extrapolation methods preferred by manufacturers, evidence review groups, and NICE committees for a set of NICE single technology appraisals in oncology when considering subsequent trial data cuts becoming available after the original submission. Nevertheless, the most appropriate approaches for accurate long-term extrapolation remain unclear.īullement et al. This issue has more recently led to the increased consideration in technology appraisals (TAs) of more flexible methods for long-term extrapolation including spline and mixture cure modeling methods and, subsequently, the production of new guidance covering more flexible extrapolation methods 7. However, these models often do not provide sufficient flexibility to reflect the long-term outcomes anticipated for ICIs. The most common methods for extrapolating time-to-event outcomes are standard parametric survival models, the use of which is well documented 6. Thus, it is necessary to use methods that allow extrapolation beyond the trial period. #Engauge digitizer more points in fit full#ICIs have been demonstrated to provide substantial improvements in the long-term survival of patients (the extent of which varies by technology and indication) however, these improvements are often not captured well when the available clinical trial data are less mature 3, 4.ĭespite these data limitations, long-term estimates of time-to-event outcomes (including OS) are still required to support HTA decisions, given the request of HTA agencies such as the UK National Institute for Health and Care Excellence (NICE) to capture the full benefits (and costs) experienced by patients over their lifetimes 5. The mechanism of action for these therapies can lead to a delayed, but lasting, clinical response due to the timing of the immune system response and thus an expectation of marked improvement in clinical outcomes 2. 1 the issue is further exacerbated for novel treatment options including immune-checkpoint inhibitors (ICIs), a type of immuno-oncology therapy. However, this leads to a reduction in the extent of long-term clinical trial evidence available to support HTA decisions. To accelerate patient access to innovative medicines identified as having promising benefits, regulatory approvals, and submissions are often based on less mature data and surrogate outcomes or proxies for overall survival (OS) such as progression-free survival 1. The time between the initiation of new clinical trials, regulatory approval, and subsequent HTA submissions is becoming shorter. Uncertainty around lifetime survival projections based on short-term regulatory trial data are often, if not always, central to decision uncertainty in health technology assessment (HTA). ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |