A convenient formula for sample size calculations in clinical trials with multiple co-primary continuous endpoints

Tomoyuki Sugimoto; Takashi Sozu; Toshimitsu Hamasaki

doi:10.1002/pst.505

A convenient formula for sample size calculations in clinical trials with multiple co-primary continuous endpoints

Pharmaceutical Statistics ◽

10.1002/pst.505 ◽

2012 ◽

Vol 11 (2) ◽

pp. 118-128 ◽

Cited By ~ 25

Author(s):

Tomoyuki Sugimoto ◽

Takashi Sozu ◽

Toshimitsu Hamasaki

Keyword(s):

Clinical Trials ◽

Sample Size ◽

Sample Size Calculations ◽

Convenient Formula

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Sample size calculations for detecting disease-modifying osteoarthritis drug effects on the incidence of end-stage knee osteoarthritis in clinical trials: Data from the Osteoarthritis Initiative

Seminars in Arthritis and Rheumatism ◽

10.1016/j.semarthrit.2018.12.002 ◽

2019 ◽

Vol 49 (1) ◽

pp. 3-8 ◽

Cited By ~ 1

Author(s):

Klaus Flechsenhar ◽

Janina S Ried ◽

Jeffrey B Driban ◽

Lori Lyn Price ◽

Timothy McAlindon

Keyword(s):

Clinical Trials ◽

Knee Osteoarthritis ◽

Sample Size ◽

Drug Effects ◽

Sample Size Calculations ◽

Osteoarthritis Initiative ◽

Disease Modifying ◽

End Stage

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Sample Size Calculations for Clinical Trials with Ordinal Data

Sample Sizes for Clinical Trials ◽

10.1201/9781584887409.ch14 ◽

2009 ◽

pp. 215-245

Keyword(s):

Clinical Trials ◽

Sample Size ◽

Ordinal Data ◽

Sample Size Calculations

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Control arm heterogeneity in trials for unselected advanced triple-negative breast cancer (aTNBC).

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.1082 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 1082-1082

Author(s):

Kinisha Gala ◽

Ankit Kalucha ◽

Samuel Martinet ◽

Anushri Goel ◽

Kalpana Devi Narisetty ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Sample Size ◽

Single Agent ◽

Progression Free Survival ◽

Sample Sizes ◽

Time To Event ◽

Sample Size Calculations ◽

Subgroup Analyses ◽

Combination Regimens

1082 Background: Primary endpoints of clinical trials frequently include subgroup-analyses. Several solid cancers such as aTNBC are heterogeneous, which can lead to unpredictable control arm performance impairing accurate assumptions for sample size calculations. We explore the value of a comprehensive clinical trial results repository in assessing control arm heterogeneity with aTNBC as the pilot. Methods: We identified P2/3 trials reporting median overall survival (mOS) and/or median progression-free survival (mPFS) in unselected aTNBC through a systematic search of PubMed, clinical trials databases and conference proceedings. Trial arms with sample sizes ≤25 or evaluating drugs no longer in development were excluded. Due to inconsistency among PD-L1 assays, PD-L1 subgroup analyses were not assessed separately. The primary aim was a descriptive analysis of control arm mOS and mPFS across all randomized trials in first line (1L) aTNBC. Secondary aims were to investigate time-to-event outcomes in control arms in later lines and to assess time-trends in aTNBC experimental and control arm outcomes. Results: We included 33 trials published between June 2013-Feb 2021. The mOS of control arms in 1L was 18.7mo (range 12.6-22.8) across 5 trials with single agent (nab-) paclitaxel [(n)P], and 18.1mo (similar range) for 7 trials including combination regimens (Table). The mPFS of control arms in 1L was 4.9mo (range 3.8-5.6) across 5 trials with single-agent (n)P, and 5.6mo (range 3.8-6.1) across 8 trials including combination regimens. Control arm mOS was 13.1mo (range 9.4-17.4) for 3 trials in first and second line (1/2L) and 8.7mo (range 6.7-10.8) across 5 trials in 2L and beyond. R2 for the mOS best-fit lines across control and experimental arms over time was 0.09, 0.01 and 0.04 for 1L, 1/2L and 2L and beyond, respectively. Conclusions: Median time-to-event outcomes of control arms in 1L aTNBC show considerable heterogeneity, even among trials with comparable regimens and large sample sizes. Disregarding important prognostic factors at stratification can lead to imbalances between arms, which may jeopardize accurate sample size calculations, trial results and interpretation. Optimizing stratification and assumptions for power calculations is of utmost importance in aTNBC and beyond. A digitized trial results repository with precisely defined patient populations and treatment settings could improve accuracy of assumptions during clinical trial design.[Table: see text]

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