To Stop or Not to Stop? Approaches to Randomized Discontinuation Trials

Teixeira‐Pinto, Armando

doi:10.1056/evide2200127

Cited by 1 publication

(5 citation statements)

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“…As previously reported, the power of RCTs from the Cochrane database tended to be even lower (median power: 13%, with 78% of trials < 80% power). 9 The SNR distribution was scaled by the observed standard error to derive the prior for the treatment effect in a particular trial ( Figure S4 ). Examples of resulting priors are plotted in Figure S5.…”

Section: Resultsmentioning

confidence: 99%

“…Consistent with previous work, we found that the actual power of most phase III trials is low relative to the power specified during trial design. 9,29 Lower power increases the risk of false negative findings. When an underpowered trial does reach “statistical significance”, the effect is usually overestimated and leads to replication failure.…”

Section: Discussionmentioning

confidence: 99%

“…In previous work, an informative prior for addressing treatment effect exaggeration was developed using 23,551 randomized trials in the Cochrane Database of Systematic Reviews. 9,19–22 In the present study, we applied this methodology for the creation of a phase III, oncology-specific prior distribution of the treatment effect. 19–22 In brief, the z-score for each RCT was computed as the point estimate for the log hazard ratio (log HR) divided by the standard error.…”

Section: Methodsmentioning

confidence: 99%

“…The distribution of the power was then obtained as a transformation of the SNR. 9 Note that this is the power against the (unobserved) true effect, and not the so-called “post hoc” power, or the power against the effect that was assumed in the sample size calculation. 23 Lastly, the prior for the treatment effect was obtained by scaling the distribution of the SNR by the standard error.…”

Section: Methodsmentioning

confidence: 99%

“…3–5 P -value thresholds may lead to both overestimation and underestimation of effects, particularly in scenarios where power is lower than planned. 6–9 For example, a significant P value (e.g., P < 0.05) in a trial designed with 80% power does not imply an 80% probability that the experimental treatment was beneficial. Therefore, novel tools to improve the interpretation of primary outcomes in oncology trials are sorely needed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology

Sherry,

Msaouel,

Kupferman

et al. 2024

Preprint

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PurposeThe primary results of phase III oncology trials may be challenging to interpret, given that such results are generally based on meetingP-value thresholds. The probability of whether a treatment is beneficial, although a more intuitive summary of the results, is not provided by most trials. In this study, we developed and released a user-friendly tool that calculates the probability that a treatment studied in a phase III oncology trial is beneficial using published summary statistics.MethodsWe curated the primary time-to-event outcomes of 415 phase III, superiority design, therapeutic randomized controlled trials of oncologic treatments enrolling 338,600 patients and published between 2004 and 2020. A phase III oncology-specific prior probability distribution for the treatment effect was developed based on an estimated three-component zero-mean mixture distribution of the observed z-scores. Using this prior, we computed the probability of any benefit (hazard ratio < 1) and the probability of clinically meaningful benefit (hazard ratio < 0.8) for each trial. The distribution of signal-to-noise ratios of phase III oncology trials was compared with that of 23,551 randomized trials from the Cochrane Database of Systematic Reviews.ResultsThe signal-to-noise ratios of phase III oncology trials tended to be much larger than randomized trials from the Cochrane database. Still, the median power of phase III oncology trials was only 49% (IQR, 14% to 95%), and the power was less than 80% in 65% of trials. Using the developed phase III, oncology-specific prior, only 53% of trials claiming superiority (114 of 216) had a ≥ 90% probability of providing clinically meaningful benefits. Conversely, the probability that the experimental arm was superior to the control arm (HR < 1) exceeded 90% in 17% of trials interpreted as having no benefit (34 of 199).ConclusionBy enabling computation of contextual probabilities for the treatment effect from summary statistics, our robust, highly practical tool, now posted on a user-friendly webpage, can aid the wider oncology community in the interpretation of phase III trials.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology

Sherry,

Msaouel,

Kupferman

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

To Stop or Not to Stop? Approaches to Randomized Discontinuation Trials

Cited by 1 publication

References 4 publications

An Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology

An Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology

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