Estimation of multivariate treatment effects in contaminated clinical trials

2021 ◽  
Author(s):  
Zi Ye ◽  
Solomon W. Harrar
Keyword(s):  
Clinical Trials ◽  
Treatment Effects

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

scholarly journals Re-defining response and treatment effects for neuro-oncology immunotherapy clinical trials

2015 ◽  
Vol 123 (3) ◽  
pp. 339-346 ◽  
Author(s):  
David A. Reardon ◽  
Hideho Okada
Keyword(s):  
Clinical Trials ◽  
Treatment Effects

Testing hypotheses under adaptive randomization with continuous covariates in clinical trials

2018 ◽  
Vol 28 (6) ◽  
pp. 1609-1621
Author(s):  
Xiaoming Li ◽  
Jianhui Zhou ◽  
Feifang Hu
Keyword(s):  
Clinical Trials ◽  
Hypothesis Testing ◽  
Treatment Effects ◽  
Linear Models ◽  
Adaptive Designs ◽  
Cumulative Distribution ◽  
Type I ◽  
Percentile Method ◽  
Continuous Covariates ◽  
Leibler Divergence

Covariate-adaptive designs are widely used to balance covariates and maintain randomization in clinical trials. Adaptive designs for discrete covariates and their asymptotic properties have been well studied in the literature. However, important continuous covariates are often involved in clinical studies. Simply discretizing or categorizing continuous covariates can result in loss of information. The current understanding of adaptive designs with continuous covariates lacks a theoretical foundation as the existing works are entirely based on simulations. Consequently, conventional hypothesis testing in clinical trials using continuous covariates is still not well understood. In this paper, we establish a theoretical framework for hypothesis testing on adaptive designs with continuous covariates based on linear models. For testing treatment effects and significance of covariates, we obtain the asymptotic distributions of the test statistic under null and alternative hypotheses. Simulation studies are conducted under a class of covariate-adaptive designs, including the p-value-based method, the Su’s percentile method, the empirical cumulative-distribution method, the Kullback–Leibler divergence method, and the kernel-density method. Key findings about adaptive designs with independent covariates based on linear models are (1) hypothesis testing that compares treatment effects are conservative in terms of smaller type I error, (2) hypothesis testing using adaptive designs outperforms complete randomization method in terms of power, and (3) testing on significance of covariates is still valid.

Considerations for Evaluating Treatment Effects From Randomized Clinical Trials

2017 ◽  
Vol 102 (6) ◽  
pp. 917-923 ◽  
Author(s):  
Stephen J. Ruberg ◽  
Mouna Akacha
Keyword(s):  
Clinical Trials ◽  
Treatment Effects ◽  
Randomized Clinical Trials

scholarly journals Application of multi-state models in cancer clinical trials

2018 ◽  
Vol 15 (5) ◽  
pp. 489-498 ◽  
Author(s):  
Jennifer G Le-Rademacher ◽  
Ryan A Peterson ◽  
Terry M Therneau ◽  
Ben L Sanford ◽  
Richard M Stone ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Complete Remission ◽  
Treatment Effects ◽  
Remission Rate ◽  
Disease Process ◽  
Time Dependent ◽  
Cancer Clinical Trials ◽  
Cox Models ◽  
Multiple Paths ◽  
State Models

Background/aims The goal of this article is to illustrate the utility of multi-state models in cancer clinical trials. Our specific aims are to describe multi-state models and how they differ from standard survival methods, to illustrate how multi-state models can facilitate deeper understanding of the treatment effect on multiple paths along the disease process that patients could experience in cancer clinical trials, to explain the differences between multi-state models and time-dependent Cox models, and to briefly describe available software to conduct such analyses. Methods Data from 717 newly diagnosed acute myeloid leukemia patients who enrolled in the CALGB 10603 trial were used as an illustrative example. The current probability-in-state was estimated using the Aalen–Johansen estimator. The restricted mean time in state was calculated as the area under the probability-in-state curves. Cox-type regression was used to evaluate the effect of midostaurin on the various clinical paths. Simulation was conducted using a newly constructed shiny application. All analyses were performed using the R software. Results Multi-state model analyses of CALGB 10603 suggested that the overall improvement in survival with midostaurin seen in the primary analysis possibly resulted from a higher complete remission rate in combination with a lower risk of relapse and of death after complete remission in patients treated with midostaurin. Simulation results, in a three-state illness-death without recovery model, demonstrate that multi-state models and time-dependent Cox models evaluate treatment effects from different frameworks. Conclusion Multi-state models allow detailed evaluation of treatment effects in complex clinical trial settings where patients can experience multiple paths between study enrollment and the final outcome. Multi-state models can be used as a complementary tool to standard survival analyses to provide deeper insights to the effects of treatment in trial settings with complex disease process.

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