Enzalutamide in men with chemotherapy-naive metastatic prostate cancer (mCRPC): Results of phase III PREVAIL study.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. LBA1-LBA1 ◽  
Author(s):  
Tomasz M. Beer ◽  
Andrew J. Armstrong ◽  
Cora N. Sternberg ◽  
Celestia S. Higano ◽  
Peter Iversen ◽  
...  
Keyword(s):  
Error Rate ◽  
Interim Analysis ◽  
Radiographic Progression ◽  
Type I Error ◽  
Data Monitoring Committee ◽  
Phase Iii ◽  
Type I ◽  
Risk Of Death ◽  
Type I Error Rate ◽  
Upper Limit

LBA1^ Background: Enzalutamide, an orally administered androgen receptor inhibitor, improved overall survival (OS) in men with mCRPC who had received prior docetaxel therapy (Scher et al, NEJM 367:13, 2012). This study examined whether enzalutamide could prolong OS and radiographic progression-free survival (rPFS) in asymptomatic or mildly symptomatic chemotherapy-naive men with mCRPC. Methods: In this randomized, double-blind, placebo-controlled, multinational phase 3 study (NCT01212991), chemotherapy-naive patients with mCRPC were stratified by site and randomized 1:1 to enzalutamide 160 mg/day or placebo. OS and rPFS were co-primary endpoints and analyzed for the intent-to-treat population. Planned sample size was 1,680 with 765 deaths to achieve 80% power to detect a target OS hazard ratio (HR) of 0.815 with a type I error rate of 0.049 and a single interim analysis at 516 (67%) deaths. The co-primary endpoint of rPFS had sufficient power to detect a target HR of 0.57 and a type I error rate of 0.001 with a minimum of 410 events. Results: A total of 1,717 men were randomized (1,715 treated) between September 2010 and September 2012. The interim analysis at 539 deaths showed a statistically significant benefit of enzalutamide over placebo with a 30% reduction in risk of death (OS: HR 0.70; 95% CI: 0.59-0.83; P< 0.0001) and an 81% reduction in risk of radiographic progression or death (rPFS: HR 0.19; 95% CI: 0.15-0.23; P< 0.0001). At the time of the analysis, 28% of enzalutamide patients and 35% of placebo patients had died. Estimated median OS was 32.4 months (mo) (95% CI, 31.5–upper limit not yet reached [NYR]) in the enzalutamide arm vs 30.2 mo (95% CI, 28–upper limit NYR) in the placebo arm. Median rPFS was NYR (95% CI: 13.8–upper limit NYR) in the enzalutamide arm vs 3.9 mo (95% CI: 3.7-5.4) in the placebo arm. Seizure events were reported in two patients. The Independent Data Monitoring Committee considered the benefit-risk ratio to favor enzalutamide and recommended stopping the study and crossing placebo patients to enzalutamide. Secondary endpoints and safety analysis will be presented. Conclusions: Treatment with enzalutamide significantly improves OS and rPFS in men with chemotherapy-naive mCRPC. Clinical trial information: NCT01212991.

Adaptive designs in multi-reader multi-case clinical trials of imaging devices

2019 ◽  
Vol 29 (6) ◽  
pp. 1592-1611
Author(s):  
Zhipeng Huang ◽  
Frank Samuelson ◽  
Lucas Tcheuko ◽  
Weijie Chen
Keyword(s):  
Clinical Trials ◽  
Error Rate ◽  
Interim Analysis ◽  
Type I Error ◽  
Adaptive Methods ◽  
Type I ◽  
Type I Error Rate ◽  
Power And Control ◽  
Target Power ◽  
And Control

Evaluation of medical imaging devices often involves clinical studies where multiple readers (MR) read images of multiple cases (MC) for a clinical task, which are often called MRMC studies. In addition to sizing patient cases as is required in most clinical trials, MRMC studies also require sizing readers, since both readers and cases contribute to the uncertainty of the estimated diagnostic performance, which is often measured by the area under the ROC curve (AUC). Due to limited prior information, sizing of such a study is often unreliable. It is desired to adaptively resize the study toward a target power after an interim analysis. Although adaptive methods are available in clinical trials where only the patient sample is sized, such methodologies have not been established for MRMC studies. The challenge lies in the fact that there is a correlation structure in MRMC data and the sizing involves both readers and cases. We develop adaptive MRMC design methodologies to enable study resizing. In particular, we resize the study and adjust the critical value for hypothesis testing simultaneously after an interim analysis to achieve a target power and control the type I error rate in comparing AUCs of two modalities. Analytical results have been derived. Simulations show that the type I error rate is controlled close to the nominal level and the power is adjusted toward the target value under a variety of simulation conditions. We demonstrate the use of our methods in a real-world application comparing two imaging modalities for breast cancer detection.

Correction: “Influence of Selection Bias on the Test Decision – A Simulation Study”

2014 ◽  
Vol 53 (05) ◽  
pp. 343-343
Keyword(s):  
Selection Bias ◽  
Simulation Study ◽  
Error Rate ◽  
Type I Error ◽  
Block Size ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rate ◽  
Representation Error ◽  
Numeric Representation

We have to report marginal changes in the empirical type I error rates for the cut-offs 2/3 and 4/7 of Table 4, Table 5 and Table 6 of the paper “Influence of Selection Bias on the Test Decision – A Simulation Study” by M. Tamm, E. Cramer, L. N. Kennes, N. Heussen (Methods Inf Med 2012; 51: 138 –143). In a small number of cases the kind of representation of numeric values in SAS has resulted in wrong categorization due to a numeric representation error of differences. We corrected the simulation by using the round function of SAS in the calculation process with the same seeds as before. For Table 4 the value for the cut-off 2/3 changes from 0.180323 to 0.153494. For Table 5 the value for the cut-off 4/7 changes from 0.144729 to 0.139626 and the value for the cut-off 2/3 changes from 0.114885 to 0.101773. For Table 6 the value for the cut-off 4/7 changes from 0.125528 to 0.122144 and the value for the cut-off 2/3 changes from 0.099488 to 0.090828. The sentence on p. 141 “E.g. for block size 4 and q = 2/3 the type I error rate is 18% (Table 4).” has to be replaced by “E.g. for block size 4 and q = 2/3 the type I error rate is 15.3% (Table 4).”. There were only minor changes smaller than 0.03. These changes do not affect the interpretation of the results or our recommendations.

Controlling type I error rate for fast track drug development programmes

2003 ◽  
Vol 22 (5) ◽  
pp. 665-675 ◽  
Author(s):  
Weichung J. Shih ◽  
Peter Ouyang ◽  
Hui Quan ◽  
Yong Lin ◽  
Bart Michiels ◽  
...  
Keyword(s):  
Drug Development ◽  
Error Rate ◽  
Fast Track ◽  
Type I Error ◽  
Type I ◽  
Type I Error Rate

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes

2021 ◽  
pp. 174077452110101
Author(s):  
Jennifer Proper ◽  
John Connett ◽  
Thomas Murray
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Error Rate ◽  
Adaptive Design ◽  
Type I Error ◽  
Probability Model ◽  
Binary Outcomes ◽  
Type I ◽  
Operating Characteristics ◽  
Type I Error Rate

Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

Anova Tests for Homogeneity of Variance: Nonnormality and Unequal Samples

1977 ◽  
Vol 2 (3) ◽  
pp. 187-206 ◽  
Author(s):  
Charles G. Martin ◽  
Paul A. Games
Keyword(s):  
Error Rate ◽  
Type I Error ◽  
Type I ◽  
Empirical Comparison ◽  
Jackknife Test ◽  
Type I Error Rate ◽  
Power And Control ◽  
Homogeneity Of Variance ◽  
Test Use ◽  
And Control

This paper presents an exposition and an empirical comparison of two potentially useful tests for homogeneity of variance. Control of Type I error rate, P(EI), and power are investigated for three forms of the Box test and for two forms of the jackknife test with equal and unequal n's under conditions of normality and nonnormality. The Box test is shown to be robust to violations of the assumption of normality. The jackknife test is shown not to be robust. When n's are unequal, the problem of heterogeneous within-cell variances of the transformed values and unequal n's affects the jackknife and Box tests. Previously reported suggestions for selecting subsample sizes for the Box test are shown to be inappropriate, producing an inflated P(EI). Two procedures which alleviate this problem are presented for the Box test. Use of the jack-knife test with a reduced alpha is shown to provide power and control of P(EI) at approximately the same level as the Box test. Recommendations for the use of these techniques and computational examples of each are provided.

Group sequential designs with robust semiparametric recurrent event models

2018 ◽  
Vol 28 (8) ◽  
pp. 2385-2403 ◽  
Author(s):  
Tobias Mütze ◽  
Ekkehard Glimm ◽  
Heinz Schmidli ◽  
Tim Friede
Keyword(s):  
Error Rate ◽  
Joint Distribution ◽  
Recurrent Events ◽  
Type I Error ◽  
Type I ◽  
Sequential Designs ◽  
Group Sequential ◽  
Type I Error Rate ◽  
Sequential Procedures ◽  
Group Sequential Designs

Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures.

scholarly journals Data-driven region-of-interest selection without inflating Type I error rate

2016 ◽  
Vol 54 (1) ◽  
pp. 100-113 ◽  
Author(s):  
Joseph L. Brooks ◽  
Alexia Zoumpoulaki ◽  
Howard Bowman
Keyword(s):  
Error Rate ◽  
Type I Error ◽  
Region Of Interest ◽  
Data Driven ◽  
Type I ◽  
Type I Error Rate

Summarizing Monte Carlo Results in Methodological Research: The One- and Two-Factor Fixed Effects ANOVA Cases

1992 ◽  
Vol 17 (4) ◽  
pp. 315-339 ◽  
Author(s):  
Michael R. Harwell ◽  
Elaine N. Rubinstein ◽  
William S. Hayes ◽  
Corley C. Olds
Keyword(s):  
Monte Carlo ◽  
Error Rate ◽  
Fixed Effects ◽  
Type I Error ◽  
Type I ◽  
F Test ◽  
Unequal Variances ◽  
Type I Error Rate ◽  
Monte Carlo Studies ◽  
The One

Meta-analytic methods were used to integrate the findings of a sample of Monte Carlo studies of the robustness of the F test in the one- and two-factor fixed effects ANOVA models. Monte Carlo results for the Welch (1947) and Kruskal-Wallis (Kruskal & Wallis, 1952) tests were also analyzed. The meta-analytic results provided strong support for the robustness of the Type I error rate of the F test when certain assumptions were violated. The F test also showed excellent power properties. However, the Type I error rate of the F test was sensitive to unequal variances, even when sample sizes were equal. The error rate of the Welch test was insensitive to unequal variances when the population distribution was normal, but nonnormal distributions tended to inflate its error rate and to depress its power. Meta-analytic and exact statistical theory results were used to summarize the effects of assumption violations for the tests.

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