Sample size considerations for historical control studies with survival outcomes

Zhu, Hong; Zhang, Song; Ahn, Chul

doi:10.1080/10543406.2015.1052495

Cited by 8 publications

(10 citation statements)

References 22 publications

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“…59 Other approaches are based on exact unconditional estimation, 60 a maximization approach, 61 and a flexible sample size formula for survival outcomes that controls arbitrary percentiles of the conditional power and type I error rate, conditional on the historical control response rate. 62 The approach with the greatest flexibility for determining the sample size is based on simulation. 63 In many cases (particularly many Bayesian applications), it is the only approach.…”

Section: Review Of Methods For Determining the Sample Size And Operatmentioning

confidence: 99%

Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials: Review of Methods and Opportunities

Lim

Walley

Yuan

et al. 2018

Ther Innov Regul Sci

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The goal of clinical trial research is to deliver safe and efficacious new treatments to patients in need in a timely and cost-effective manner. There is precedent in using historical control data to reduce the number of concurrent control subjects required in developing medicines for rare diseases and other areas of unmet need. The purpose of this paper is to provide a review for a regulatory and industry audience of the current state of relevant statistical methods, and of the uptake of these approaches and the opportunities for broader use of historical data in confirmatory clinical trials. General principles to consider when incorporating historical control data in a new trial are presented. Bayesian and frequentist approaches are outlined including how the operating characteristics for such a trial can be obtained. Finally, examples of approved new treatments that incorporated historical controls in their confirmatory trials are presented.

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Section: Review Of Methods For Determining the Sample Size And Operatmentioning

confidence: 99%

Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials: Review of Methods and Opportunities

Lim

Walley

Yuan

et al. 2018

Ther Innov Regul Sci

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show abstract

“…Let

{\overset{false^}{λ}}_{c}

and

{\overset{false^}{λ}}_{e}

denote the maximum likelihood estimates (MLE) of λ c and λ e , respectively. The following asymptotic distribution generally holds 8,10 :

\log ((), \frac{{\overset{false^}{λ}}_{c}}{{\overset{false^}{λ}}_{e}}) ~ N ((),, \log ((), \frac{λ_{c}}{λ_{e}}), \frac{1}{E ((), d_{e})} + \frac{1}{E ((), d_{e})}) .

…”

Section: An Existing Design Based On Asymptotic Inferencementioning

confidence: 99%

“…Following the paradigm in existing literature, 6‐8 we assume exponentially distributed survival times. Exponential survival has been used in clinical trial design to model the survival distribution, for example, fig.…”

Section: An Existing Design Based On Asymptotic Inferencementioning

confidence: 99%

“…By computing P ( W > Z 1 − α ), power of the asymptotic test at significance level α for hypothesis (2) is:

P_{a} ((),, Δ, λ_{c}) = 1 - normalΦ ((), \frac{Z_{1 - α} \sqrt{1 / E (d_{c}) + 1 / E (d_{e})} - \log (normalΔ)}{\sqrt{1 / E ((), d_{c}) + 1 / E ((), d_{e})}}),

where Δ is the ratio between λ c and λ e , that is, Δ = λ c / λ e , and Φ is the cumulative density function (CDF) of standardized normal distribution. The power calculation in (4) has been referred to as “the unconditional power” in Reference 10 and “the power from randomized trial method” in Reference 8. Following this power calculation, the expected number of events in the experimental arm to achieve power 1 − β can be derived as:

E ((), d_{e}) = \frac{E ((), d_{c}) {(Z_{1 - α} + Z_{1 - β})}^{2}}{E ((), d_{c}) {(\log (normalΔ))}^{2} - {(Z_{1 - α} + Z_{1 - β})}^{2}} .

…”

Section: An Existing Design Based On Asymptotic Inferencementioning

confidence: 99%

“…Emrich 7 conducted power analysis given further follow‐up of control group subjects. Zhu et al 8 proposed a sample size formula for controlling arbitrary percentiles of conditional power and Type I error. To the best of our knowledge, none of the above existing methods investigated the impact of normal approximation in power analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Designing historical control studies with survival endpoints using exact statistical inference

Han

2020

Pharmaceutical Statistics

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SummaryHistorical control trials compare an experimental treatment with a previously conducted control treatment. By assigning all recruited samples to the experimental arm, historical control trials can better identify promising treatments in early phase trials compared with randomized control trials. Existing designs of historical control trials with survival endpoints are based on asymptotic normal distribution. However, it remains unclear whether the asymptotic distribution of the test statistic is close enough to the true distribution given relatively small sample sizes in early phase trials. In this article, we address this question by introducing an exact design approach for exponentially distributed survival endpoints, and compare it with an asymptotic design in both real examples and simulation examples. Simulation results show that the asymptotic test could lead to bias in the sample size estimation. We conclude the proposed exact design should be used in the design of historical control trials.

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Externally Controlled Studies Using Real-World Data in Patients With Hematological Cancers

Hermans,

van der Maas,

van Norden

et al. 2024

JAMA Oncol

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ImportanceThe use of real-world data (RWD) external control arms in prospective studies is increasing. The advantages, including the immediate availability of a control population, must be balanced with the requirements of meeting evidentiary standards.ObjectiveTo address the question of whether and to what extent the methods of RWD studies compare to standard methods used in randomized clinical trials.Evidence ReviewA systematic search across 4 electronic databases and Google Scholar was conducted from January 1, 2000, to October 23, 2023. Studies were included in the systematic review if they compared an intervention arm in a clinical trial to an RWD control arm in patients with hematological cancers and if they were published between 2000 and 2023.FindingsThirty-two prospective intervention studies incorporating external control data from RWD sources of patients with hematological cancers were identified. A total of 4306 patients from intervention arms and 10 594 from RWD control arms were included across all studies. Only 2 studies (6%) included prospectively collected RWD. The complete trial inclusion criteria were applied to the RWD cohort in 7 studies (22%). Four studies (13%) published the statistical analysis plan and prespecified use of RWD. A total of 23 studies (72%) applied matching algorithms for trial and RWD cohorts, including matching for demographic, disease, and/or therapy-related characteristics. The end point criteria were the same as the trial in 8 studies (25%). In contrast, 12 studies (38%) used different end points, and 12 (38%) did not provide an end point definition for the RWD. Twelve studies (38%) had a median follow-up difference of less than a year between arms. Eight studies (25%) reported toxic effect data for the trial arm, of which 5 studies reported toxic effect data for the RWD arm.Conclusions and RelevanceIn this systematic review, limitations were observed in the application of clinical trial eligibility criteria to RWD, statistical rigor and application of matching methods, the definition of end points, follow-up, and reporting of adverse events, which may challenge the conclusions reported in studies using RWD.

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Sample size considerations for historical control studies with survival outcomes

Cited by 8 publications

References 22 publications

Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials: Review of Methods and Opportunities

Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials: Review of Methods and Opportunities

Designing historical control studies with survival endpoints using exact statistical inference

Externally Controlled Studies Using Real-World Data in Patients With Hematological Cancers

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