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. 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. Output from the WebPlot program (both manual and automated) is shown below plotted together with output from my Digitgraph function, showing close agreement for all three lines.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). The program is considerably more sophisticated than my spreadsheet, providing not only a magnified image of the graph at the cursor location, but also allowing the option of either manual selection of data points, or a fully automated process to detect the graph line(s) and generate the data points. The screen-shot below shows the downloaded version. The download is free (with no advertising), with a button for voluntary donations. I also recently discovered the Webplot Digitizer program, that can either be used on-line, or as a download. The new version can be downloaded from: DigitGraph2.zip I have updated the instructions for the procedure to cover Excel’s new dynamic array feature, which can return an array of data from a function entered in a single cell. I have posted here previously a spreadsheet that allows XY data to be extracted from images of graphs, maps or other images of objects in a single plane: How to digitise a scanned image.
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