Noninferiority trials with nonadherence to the assigned randomized treatment

2019 ◽  
Vol 16 (6) ◽  
pp. 673-681 ◽  
Author(s):  
Edward L Korn ◽  
Robert J Gray ◽  
Boris Freidlin
Keyword(s):  
Sensitivity Analysis ◽  
Sample Size ◽  
Risk Score ◽  
Standard Therapy ◽  
Error Probability ◽  
Experimental Therapy ◽  
Random Assignment ◽  
Type 1 Error ◽  
Noninferiority Trials

Background: Nonadherence to treatment assignment in a noninferiority randomized trial is especially problematic because it attenuates observed differences between the treatment arms, possibly leading one to conclude erroneously that a truly inferior experimental therapy is noninferior to a standard therapy (inflated type 1 error probability). The Lachin–Foulkes adjustment is an increase in the sample size to account for random nonadherence for the design of a superiority trial with a time-to-event outcome; it has not been explored in the noninferiority trial setting nor with nonrandom nonadherence. Noninferiority trials where patients have knowledge of a personal prognostic risk score may lead to nonrandom nonadherence, as patients with a relatively high risk may be more likely to not adhere to the random assignment to the (reduced) experimental therapy, and patients with a relatively low risk score may be more likely to not adhere to the random assignment to the (more aggressive) standard therapy. Methods: We investigated via simulations the properties of the Lachin–Foulkes adjustment in the noninferiority setting. We considered nonrandom in addition to random nonadherence to the treatment assignment. For nonrandom nonadherence, we used the scenario where a risk score, potentially associated with the between-arm treatment difference, influences patients’ nonadherence. A sensitivity analysis is proposed for addressing the nonrandom nonadherence for this scenario. The noninferiority TAILORx adjuvant breast cancer trial, where eligibility was based on a genomic risk score, is used as an example throughout. Results: The Lachin–Foulkes adjustment to the sample size improves the operating characteristics of noninferiority trials with random nonadherence. However, to maintain type 1 error probability, it is critical to adjust the noninferiorty margin as well as the sample size. With nonrandom nonadherence that is associated with a prognostic risk score, the type 1 error probability of the Lachin–Foulkes adjustment can be inflated (e.g. doubled) when the nonadherence is larger in the experimental arm than the standard arm. The proposed sensitivity analysis lessens the inflation in this situation. Conclusion: The Lachin–Foulkes adjustment to the sample size and noninferiority margin is a useful simple technique for attenuating the effects of random nonadherence in the noninferiority setting. With nonrandom nonadherence associated with a risk score known to the patients, the type 1 error probability can be inflated in certain situations. A proposed sensitivity analysis for these situations can attenuate the inflation.

scholarly journals Maximum type 1 error rate inflation in multiarmed clinical trials with adaptive interim sample size modifications

2014 ◽  
Vol 56 (4) ◽  
pp. 614-630 ◽  
Author(s):  
Alexandra C. Graf ◽  
Peter Bauer ◽  
Ekkehard Glimm ◽  
Franz Koenig
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Error Rate ◽  
Type 1 Error ◽  
Multiarmed Clinical Trials

Tappas: An adaptive enrichment phase 3 trial of TRC105 and pazopanib versus pazopanib alone in patients with advanced angiosarcoma (AAS).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. TPS11081-TPS11081 ◽  
Author(s):  
Robin Lewis Jones ◽  
Steven Attia ◽  
Cyrus R. Mehta ◽  
Lingyun Liu ◽  
Kamalesh Kumar Sankhala ◽  
...  
Keyword(s):  
Sample Size ◽  
Adaptive Design ◽  
Single Agent ◽  
The United States ◽  
Phase 3 ◽  
Type 1 Error ◽  
Secondary Endpoints ◽  
Enrichment Design ◽  
Special Protocol

TPS11081 Background: AAS is an aggressive soft tissue sarcoma (STS) of endothelial cell origin with an expected median overall survival of 8-12 months. Pazopanib (P) is approved for treatment of advanced STS following progression on chemotherapy. In a retrospective study of 40 AAS patients treated with single agent P the median PFS was 3.1 months and median OS 9.9 months with no complete responses. Endoglin is an essential angiogenic receptor expressed on AAS that is upregulated following VEGF inhibition, and TRC105, an endoglin antibody, given with P produced durable complete responses in AAS patients with median PFS of 5.6 months in refractory patients including those receiving prior P. The TAPPAS trial is the first randomized Phase 3 trial performed in AAS, and was initiated following protocol assistance from the EMA and Special Protocol Assessment from the FDA. Methods: TAPPAS is a randomized multicenter study of TRC105/P vs P alone in the United States and Europe that is actively enrolling cutaneous and non-cutaneous AAS patients and incorporates an adaptive enrichment design. Key inclusion criteria: 0, 1 or 2 prior lines of therapy, ECOG ≤ 1. Primary endpoint is PFS and secondary endpoints include ORR and OS. The initial sample size of 124 patients, followed until 95 PFS events, provides more than 80% power to detect a hazard ratio of 0.55. At the time of interim analysis, projected to occur upon the occurrence of 40 events in approximately 70 patients, the result will be classified as belonging to either the favorable, promising, enrichment or unfavorable zones, based on conditional power. The sample size and PFS events will be unchanged in the favorable and unfavorable zones, and will be increased to a total of 200 patients followed for 170 PFS events in the promising zone. The trial will enroll 100 additional patients, with cutaneous disease only, in the enrichment zone and will follow them until 110 events are observed in the total cutaneous population. An independent DMC will follow the trial for safety and futility. The adaptive design requires the enrollment of fewer patients, preserves type-1 error, and protects power to detect a clinically meaningful survival benefit. (NCT 02979899). Clinical trial information: NCT02979899.

Statistical Power in Psychiatric Research

1986 ◽  
Vol 20 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Kevin D. Bird ◽  
Wayne Hall
Keyword(s):  
Sample Size ◽  
Error Rate ◽  
Statistical Power ◽  
Error Rates ◽  
Psychiatric Research ◽  
Type 1 Error ◽  
Type 2 Error ◽  
Power Analyses

Statistical power is neglected in much psychiatric research, with the consequence that many studies do not provide a reasonable chance of detecting differences between groups if they exist in the population. This paper attempts to improve current practice by providing an introduction to the essential quantities required for performing a power analysis (sample size, effect size, type 1 and type 2 error rates). We provide simplified tables for estimating the sample size required to detect a specified size of effect with a type 1 error rate of α and a type 2 error rate of β, and for estimating the power provided by a given sample size for detecting a specified size of effect with a type 1 error rate of α. We show how to modify these tables to perform power analyses for multiple comparisons in univariate and some multivariate designs. Power analyses for each of these types of design are illustrated by examples.

1535-P: An Insulin Resistance Genetic Risk Score (IR_GRS) Is Associated with Mortality in Type 1 Diabetes (T1D)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1535-P
Author(s):  
RACHEL G. MILLER ◽  
TINA COSTACOU ◽  
SUNA ONENGUT-GUMUSCU ◽  
WEI-MIN CHEN ◽  
STEPHEN S. RICH ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score

scholarly journals Accounting for confounding by time, early intervention adoption, and time-varying effect modification in the design and analysis of stepped-wedge designs: application to a proposed study design to reduce opioid-related mortality

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lior Rennert ◽  
Moonseong Heo ◽  
Alain H. Litwin ◽  
Victor De Gruttola
Keyword(s):  
Mixed Effects ◽  
Effect Modification ◽  
External Factors ◽  
Mixed Effects Models ◽  
Time Varying ◽  
Intervention Effect ◽  
Type 1 Error ◽  
And Control ◽  
Related Mortality

Abstract Background Beginning in 2019, stepped-wedge designs (SWDs) were being used in the investigation of interventions to reduce opioid-related deaths in communities across the United States. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of intervention components, and time-varying effect modification— in which external factors differentially impact intervention and control clusters. Results In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to how these external factors impact study endpoints and what estimands are most appropriate in the presence of such factors.

Joint testing of overall and simple effects for the two-by-two factorial trial design

2021 ◽  
Vol 18 (5) ◽  
pp. 521-528
Author(s):  
Eric S Leifer ◽  
James F Troendle ◽  
Alexis Kolecki ◽  
Dean A Follmann
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Control ◽  
Multiple Testing ◽  
Pressure Control ◽  
Factorial Analysis ◽  
Testing Procedures ◽  
Type 1 Error ◽  
Multiple Testing Procedures ◽  
To Receive

Background/aims: The two-by-two factorial design randomizes participants to receive treatment A alone, treatment B alone, both treatments A and B( AB), or neither treatment ( C). When the combined effect of A and B is less than the sum of the A and B effects, called a subadditive interaction, there can be low power to detect the A effect using an overall test, that is, factorial analysis, which compares the A and AB groups to the C and B groups. Such an interaction may have occurred in the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD BP) which simultaneously randomized participants to receive intensive or standard blood pressure, control and intensive or standard glycemic control. For the primary outcome of major cardiovascular event, the overall test for efficacy of intensive blood pressure control was nonsignificant. In such an instance, simple effect tests of A versus C and B versus C may be useful since they are not affected by a subadditive interaction, but they can have lower power since they use half the participants of the overall trial. We investigate multiple testing procedures which exploit the overall tests’ sample size advantage and the simple tests’ robustness to a potential interaction. Methods: In the time-to-event setting, we use the stratified and ordinary logrank statistics’ asymptotic means to calculate the power of the overall and simple tests under various scenarios. We consider the A and B research questions to be unrelated and allocate 0.05 significance level to each. For each question, we investigate three multiple testing procedures which allocate the type 1 error in different proportions for the overall and simple effects as well as the AB effect. The Equal Allocation 3 procedure allocates equal type 1 error to each of the three effects, the Proportional Allocation 2 procedure allocates 2/3 of the type 1 error to the overall A (respectively, B) effect and the remaining type 1 error to the AB effect, and the Equal Allocation 2 procedure allocates equal amounts to the simple A (respectively, B) and AB effects. These procedures are applied to ACCORD BP. Results: Across various scenarios, Equal Allocation 3 had robust power for detecting a true effect. For ACCORD BP, all three procedures would have detected a benefit of intensive glycemia control. Conclusions: When there is no interaction, Equal Allocation 3 has less power than a factorial analysis. However, Equal Allocation 3 often has greater power when there is an interaction. The R package factorial2x2 can be used to explore the power gain or loss for different scenarios.

Three essays on the commercialization of university discoveries

2020 ◽  
Author(s):  
◽  
Hao Cheng
Keyword(s):  
Selection Process ◽  
Empirical Studies ◽  
Project Selection ◽  
University Of Missouri ◽  
Type 1 Error ◽  
Ex Post ◽  
The University ◽  
Type 2 Error

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Universities commercialize their discoveries at an increasing pace in order to maximize their economic impact and generate additional funding for research. They form technology transfer offices (TTOs) to evaluate the commercial value of university inventions and choose the most promising ones to patent and commercialize. Uncertainties and asymmetric information in project selection make the TTO choices difficult and can cause both type 1 error (forgo valuable discoveries) and type 2 error (select low-value discoveries). In this dissertation, I examine the TTO's project selection process and the factors that influence the choice of academic inventions for patenting and commercialization, the type 1 error committed, and the final licensing outcome. The dissertation contains three essays. In the first essay, I analyze project selection under uncertainty when both the quality of the proposed project and the motives of the applicant are uncertain. Some inventors may have an incentive to disguise the true quality and commercial value of their discoveries in order to conform to organizational expectations of disclosure while retaining rights to potentially pursue commercialization of their discoveries outside the organization's boundaries for their own benefit. Inventors may equally, ex post, lose interest to the commercialization of their invention due to competing job demands. I develop a model to examine the decision process of a university TTO responsible for the commercialization of academic inventions under such circumstances. The model describes the conditions that prompt Type 1 and Type 2 errors and allows for inferences for minimizing each. Little is known about the factors that make project selection effective or the opposite and there has been limited empirical analysis in this area. The few empirical studies that are available, examine the sources of type 2 error but there is no empirical work that analyzes type 1 error and the contributing factors. Research on type 1 error encounters two main difficulties. First, it is difficult to ascertain the decision process and second, it is challenging to approximate the counterfactual. Using data from the TTO of the University of Missouri, in the second essay I study the factors that influence the project selection process of the TTO in and the ex post type 1 error realized. In most cases, universities pursue commercialization of their inventions through licensing. There have been a few empirical studies that have researched the factors that affect licensing and their relative importance. In the third essay, I examine the characteristics of university inventions that are licensed using almost 10 years of data on several hundred of inventions, their characteristics, and the licensing status.

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