scholarly journals Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials

2022 ◽  
Author(s):  
Kevin Kunzmann ◽  
Michael J. Grayling ◽  
Kim May Lee ◽  
David S. Robertson ◽  
Kaspar Rufibach ◽  
...  
Keyword(s):  
Sample Size ◽  
Conditional Power ◽  
Confirmatory Trials

A Revisit of Sample Size Decisions in Confirmatory Trials

2010 ◽  
Vol 2 (2) ◽  
pp. 239-248 ◽  
Author(s):  
Christy Chuang-Stein ◽  
Ruoyong Yang
Keyword(s):  
Sample Size ◽  
Confirmatory Trials

scholarly journals Comparison of futility monitoring guidelines using completed phase III oncology trials

2016 ◽  
Vol 14 (1) ◽  
pp. 48-58 ◽  
Author(s):  
Qiang Zhang ◽  
Boris Freidlin ◽  
Edward L Korn ◽  
Susan Halabi ◽  
Sumithra Mandrekar ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Alternative Hypothesis ◽  
Phase Iii ◽  
Interim Result ◽  
Type I ◽  
Conditional Power ◽  
Phase Iii Clinical Trials ◽  
The Impact ◽  
Analytical Approaches

Background: Futility (inefficacy) interim monitoring is an important component in the conduct of phase III clinical trials, especially in life-threatening diseases. Desirable futility monitoring guidelines allow timely stopping if the new therapy is harmful or if it is unlikely to demonstrate to be sufficiently effective if the trial were to continue to its final analysis. There are a number of analytical approaches that are used to construct futility monitoring boundaries. The most common approaches are based on conditional power, sequential testing of the alternative hypothesis, or sequential confidence intervals. The resulting futility boundaries vary considerably with respect to the level of evidence required for recommending stopping the study. Purpose: We evaluate the performance of commonly used methods using event histories from completed phase III clinical trials of the Radiation Therapy Oncology Group, Cancer and Leukemia Group B, and North Central Cancer Treatment Group. Methods: We considered published superiority phase III trials with survival endpoints initiated after 1990. There are 52 studies available for this analysis from different disease sites. Total sample size and maximum number of events (statistical information) for each study were calculated using protocol-specified effect size, type I and type II error rates. In addition to the common futility approaches, we considered a recently proposed linear inefficacy boundary approach with an early harm look followed by several lack-of-efficacy analyses. For each futility approach, interim test statistics were generated for three schedules with different analysis frequency, and early stopping was recommended if the interim result crossed a futility stopping boundary. For trials not demonstrating superiority, the impact of each rule is summarized as savings on sample size, study duration, and information time scales. Results: For negative studies, our results show that the futility approaches based on testing the alternative hypothesis and repeated confidence interval rules yielded less savings (compared to the other two rules). These boundaries are too conservative, especially during the first half of the study (<50% of information). The conditional power rules are too aggressive during the second half of the study (>50% of information) and may stop a trial even when there is a clinically meaningful treatment effect. The linear inefficacy boundary with three or more interim analyses provided the best results. For positive studies, we demonstrated that none of the futility rules would have stopped the trials. Conclusion: The linear inefficacy boundary futility approach is attractive from statistical, clinical, and logistical standpoints in clinical trials evaluating new anti-cancer agents.

scholarly journals A Review of Bayesian Perspectives on Sample Size Derivation for Confirmatory Trials

2021 ◽  
pp. 1-9
Author(s):  
Kevin Kunzmann ◽  
Michael J. Grayling ◽  
Kim May Lee ◽  
David S. Robertson ◽  
Kaspar Rufibach ◽  
...  
Keyword(s):  
Sample Size ◽  
Confirmatory Trials

scholarly journals Effectiveness of Oral Probiotics as Adjuvant Therapy in Reproductive Aged Women with Vaginal Discharge

2018 ◽  
pp. 3
Author(s):  
Junita Indarti ◽  
Utomo Budidarmo
Keyword(s):  
Sample Size ◽  
Vaginal Discharge ◽  
Lactobacillus Reuteri ◽  
Controlled Trial ◽  
Lactobacillus Rhamnosus ◽  
P Value ◽  
Conditional Power ◽  
Satisfaction Level ◽  
Double Blind ◽  
Reproductive Aged Women

  Objective: To investigate the efficacy of oral proboiotics and prove the high proportion of cure and satisfaction levels of post-treatment patients with a combination of antimicrobial-probiotic oral Lactobacillus rhamnosus GR-1 and Lactobacillus reuteri RC-14 compared to a combination of antimicrobial-placebo in the treatment of reproductive aged patients with vaginal discharge in the outpatient obstetrics and gynecologic clinic in Dr. Cipto Mangunkusumo Hospital, Jakarta, Indonesia and Arifin Achmad Regional Hospital Pekanbaru, Riau, Indonesia. Methods:This was a randomized, double-blind, placebo-controlled trial involving50 subjects consisting of reproductive aged women. Data were collected using syndromic approach, probiotics were given as an adjuvant for standard antimicrobial therapy versus placebo as control, response was recorded 4 weeks later, for cure proportion and satisfaction level. Statistical analysis was performed to assess the variables. Interim analysis with conditional power assesment and futility testing were performed at midway due to insufficient sample size. Research was approved by Ethics Commitee for Health Researches Faculty of Medicine University of Indonesia-Dr. Cipto Mangunkusumo Hospital in March 2016. Results: A total of 50 subjects participated in this study. and analyzed (25 subjects in each group), cure proportion 56%(14) of the treatment and 60%(15)on the control group, with relative risk of 1.1, Chi-square test p value (0.77, 95% CI; 0.57 to 2 , 11). High satisfaction level (score ≥67) was higher in the placebo (52,6%, 10 subjects) compared to probiotic group (47,4%, 9 subjects), p value 0,65 (≥0,05). Conditional power and futility testing curve, revealed Z = -0.2865, conditional power 0.11 to 0.13, and futility index of 0.87 to 0.88, equals to low possibility of statistical significance with full sample size (84). Conclusion: There was no clinical and statistical difference in the proportion of cure rate and the level of satisfaction in patients of probiotics vs placebo groups after treatment for 4 weeks. The initial hypothesis of higher proportion of the cure ratein the treatment group still cannot be excluded, due to insufficient samples. Keywords: bacterial vaginosis, , lactobacillus reuteri RC-14, lactobacillus rhamnosus GR-1, randomized double blind controlled trial, trichomoniasis , vaginal discharge,  vulvovaginal candidiasis.

scholarly journals Smoothing Corrections for Improving Sample Size Recalculation Rules in Adaptive Group Sequential Study Designs

2021 ◽  
Author(s):  
Carolin Herrmann ◽  
Geraldine Rauch
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Evaluation Criteria ◽  
High Variability ◽  
Conditional Power ◽  
Performance Score ◽  
Group Sequential ◽  
Conditional Performance ◽  
Sequential Study ◽  
Study Designs

Abstract Background An adequate sample size calculation is essential for designing a successful clinical trial. One way to tackle planning difficulties regarding parameter assumptions required for sample size calculation is to adapt the sample size during the ongoing trial.This can be attained by adaptive group sequential study designs. At a predefined timepoint, the interim effect is tested for significance. Based on the interim test result, the trial is either stopped or continued with the possibility of a sample size recalculation. Objectives Sample size recalculation rules have different limitations in application like a high variability of the recalculated sample size. Hence, the goal is to provide a tool to counteract this performance limitation. Methods Sample size recalculation rules can be interpreted as functions of the observed interim effect. Often, a “jump” from the first stage's sample size to the maximal sample size at a rather arbitrarily chosen interim effect size is implemented and the curve decreases monotonically afterwards. This jump is one reason for a high variability of the sample size. In this work, we investigate how the shape of the recalculation function can be improved by implementing a smoother increase of the sample size. The design options are evaluated by means of Monte Carlo simulations. Evaluation criteria are univariate performance measures such as the conditional power and sample size as well as a conditional performance score which combines these components. Results We demonstrate that smoothing corrections can reduce variability in conditional power and sample size as well as they increase the performance with respect to a recently published conditional performance score for medium and large standardized effect sizes. Conclusion Based on the simulation study, we present a tool that is easily implemented to improve sample size recalculation rules. The approach can be combined with existing sample size recalculation rules described in the literature.

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