MODULE 5 Group Sequential Designs Group Sequential Designs

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MODULE 5 Group Sequential Designs

MODULE 5 Group Sequential Designs

Group Sequential Designs Group Sequential Trials (GST) facilitate interim analyses Interim analyses are those

Group Sequential Designs Group Sequential Trials (GST) facilitate interim analyses Interim analyses are those which occur while a trial is ongoing In a GST, accrued data is analysed at pre-specified times E. g. After half the subjects have been measured At an interim analysis, can either stop for benefit or futility If neither found, continue trial until end/next interim analysis

Why Use a Group Sequential Design? Recruitment often sequential over long time period Can

Why Use a Group Sequential Design? Recruitment often sequential over long time period Can follow study results as they come in instead of waiting 1. Stopping for Efficacy Improved practice and to market sooner Cut cost of expensive trial/study 2. Stopping for Futility Prevents unnecessary risk exposure Allows to quickly adjust or abandon trial

Error Spending (Lan & De. Mets) Two Criteria for early stopping 1. 2. Efficacy

Error Spending (Lan & De. Mets) Two Criteria for early stopping 1. 2. Efficacy (α-spending) Futility (β-spending) Multiple Error Spending Functions § O’Brien Fleming, Pocock etc. § Both α and β spending work similarly § Can be very liberal or conservative At each interim analysis, spending a proportion of the total error Makes analysis at endpoint more conservative

Group Sequential Example “A sample size of 242 subjects (121 per treatment group) provides

Group Sequential Example “A sample size of 242 subjects (121 per treatment group) provides at least 80% power to detect a relative difference of 53% between botulinum toxin A and standardized anticholinergic therapy, assuming a treatment difference of -0. 80 and a common SD of 2. 1 (effect size = 0. 381), and a two-sided type I error rate of 5%. Sample size has been adjusted to allow for a 10% loss to follow-up over the 6 months of treatment as well as one interim analysis to stop early for benefit. ” Source: AG, Visco et al (2012) Parameter Significance Level (2 sided) Onabotulinumtoxin. A Mean Anticholinergic Mean Standard Deviation (Both) Power # Interim Analyses α Spending Function Expected Dropout Value 0. 05 -2. 3 -1. 5 2. 1 80% 1 O’Brien. Fleming 10%

n. Query Plans for Group Sequential Improve methods and UX for current GSD tables

n. Query Plans for Group Sequential Improve methods and UX for current GSD tables More flexibility in designs (e. g. skip looks) and spending functions More options for boundary parameterisation in tables/plots Add additional options for survival (accrual, piece-wise etc. ) Extend GSD methods to other design types and endpoints One Sample, non-inferiority/equivalence, informationbased Addition of other types of adaptive trials and outputs

References Group Sequential Designs Demets, D. L. , & Lan, K. K. (1994). Interim

References Group Sequential Designs Demets, D. L. , & Lan, K. K. (1994). Interim analysis: the alpha spending function approach. Statistics in medicine, 13(13‐ 14), 1341 -1352. Jennison, C. , & Turnbull, B. W. (1999). Group sequential methods with applications to clinical trials. CRC Press. Visco, A. G. , et al (2012). Anticholinergic therapy vs. onabotulinumtoxina for urgency urinary incontinence. New England Journal of Medicine, 367(19), 1803 -1813.

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