Inference in response‐adaptive clinical trials when the enrolled population varies over time

Biometrics ◽  
2021 ◽  
Author(s):  
Massimiliano Russo ◽  
Steffen Ventz ◽  
Victoria Wang ◽  
Lorenzo Trippa
Keyword(s):  
Clinical Trials ◽  
Adaptive Clinical Trials ◽  
Over Time

scholarly journals A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials

2021 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Marco Novelli ◽  
Maroussa Zagoraiou
Keyword(s):  
Clinical Trials ◽  
Primary Outcome ◽  
Wald Test ◽  
Simple Solution ◽  
Nuisance Parameters ◽  
Simulation Studies ◽  
Fundamental Properties ◽  
Adaptive Clinical Trials ◽  
Treatment Comparisons ◽  
Practical Recommendations

AbstractThe present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.

scholarly journals Critical concepts in adaptive clinical trials

2018 ◽  
Vol Volume 10 ◽  
pp. 343-351 ◽  
Author(s):  
Jay JH Park ◽  
Kristian Thorlund ◽  
Edward J Mills
Keyword(s):  
Clinical Trials ◽  
Adaptive Clinical Trials

scholarly journals Bayesian modeling and prediction of accrual in multi-regional clinical trials

2014 ◽  
Vol 26 (2) ◽  
pp. 752-765 ◽  
Author(s):  
Yi Deng ◽  
Xiaoxi Zhang ◽  
Qi Long
Keyword(s):  
Clinical Trials ◽  
Poisson Process ◽  
Process Model ◽  
Nonhomogeneous Poisson Process ◽  
Modeling And Prediction ◽  
Accuracy And Precision ◽  
Start Up ◽  
Accrual Rate ◽  
Model Region ◽  
Over Time

In multi-regional trials, the underlying overall and region-specific accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associated with multi-regional trials have not been adequately investigated. In this paper, we clarify the implication of the multi-regional nature on modeling and prediction of accrual in clinical trials and investigate a Bayesian approach for accrual modeling and prediction, which models region-specific accrual using a nonhomogeneous Poisson process and allows the underlying Poisson rate in each region to vary over time. The proposed approach can accommodate staggered start-up times and different initial accrual rates across regions/centers. Our numerical studies show that the proposed method improves accuracy and precision of accrual prediction compared to existing methods including the nonhomogeneous Poisson process model that does not model region-specific accrual.

scholarly journals Stability of ARDS subphenotypes over time in two randomised controlled trials

Thorax ◽  
2018 ◽  
Vol 73 (5) ◽  
pp. 439-445 ◽  
Author(s):  
Kevin Delucchi ◽  
Katie R Famous ◽  
Lorraine B Ware ◽  
Polly E Parsons ◽  
B Taylor Thompson ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Outcomes ◽  
Randomised Controlled Trials ◽  
Latent Class ◽  
Lung Protective Ventilation ◽  
Controlled Trials ◽  
Transition Analysis ◽  
Class 1 ◽  
Randomised Controlled ◽  
Over Time

RationaleTwo distinct acute respiratory distress syndrome (ARDS) subphenotypes have been identified using data obtained at time of enrolment in clinical trials; it remains unknown if these subphenotypes are durable over time.ObjectiveTo determine the stability of ARDS subphenotypes over time.MethodsSecondary analysis of data from two randomised controlled trials in ARDS, the ARMA trial of lung protective ventilation (n=473; patients randomised to low tidal volumes only) and the ALVEOLI trial of low versus high positive end-expiratory pressure (n=549). Latent class analysis (LCA) and latent transition analysis (LTA) were applied to data from day 0 and day 3, independent of clinical outcomes.Measurements and main resultsIn ALVEOLI, LCA indicated strong evidence of two ARDS latent classes at days 0 and 3; in ARMA, evidence of two classes was stronger at day 0 than at day 3. The clinical and biological features of these two classes were similar to those in our prior work and were largely stable over time, though class 2 demonstrated evidence of progressive organ failures by day 3, compared with class 1. In both LCA and LTA models, the majority of patients (>94%) stayed in the same class from day 0 to day 3. Clinical outcomes were statistically significantly worse in class 2 than class 1 and were more strongly associated with day 3 class assignment.ConclusionsARDS subphenotypes are largely stable over the first 3 days of enrolment in two ARDS Network trials, suggesting that subphenotype identification may be feasible in the context of clinical trials.

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