ArticleTherapeutic innovation & regulatory science2023
Statistical Evaluation of Responder Analysis in Stem Cell Clinical Trials.
Article in Therapeutic innovation & regulatory science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn evaluating treatment efficacy, there is an ongoing discussion about which endpoint is more efficient to represent the treatment effect. Absolute change (AC) is the difference between before and after treatment, while relative change (RC) is the AC relative to the baseline value. Principal investigators sometimes support the credibility of relative change, but the FDA is more likely to support absolute change. Therefore, whether these two endpoints can be translated or combined is worth investigating in order to satisfy both parties.
methodsIn this article, a motivating example is presented to show that the choice of endpoint will result in different conclusions. The compared relationship of AC and RC is discussed in terms of required sample size, power, and precision. A new type of responder endpoint that combines the concepts of AC and RC is proposed. The comparative relationship regarding sample size, power, and precision of the proposed responder endpoint and the original two endpoints are also investigated.
resultsAs a result, the performance of AC and RC is highly dependent on the choice of threshold that is often informed based on minimum clinically important difference or other clinical experience. Therefore, an absolute translation between them is hard to achieve. Inspired by the concept of responder analysis, three types of responder endpoints are proposed and discussed. The pattern of the third type of responder endpoint is having higher power, higher precision, and less required sample size in estimating the treatment effect compared to AC and RC within a range of thresholds. This advantage becomes more obvious when applying higher AC and RC thresholds and lower [Formula: see text] threshold.
conclusionThe proposed endpoint incorporates the information from the AC and RC endpoints and could be another wise choice when designing clinical trials especially when there is no absolute preference between AC and RC.
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Registered trials
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