Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

Treatment selection using prototyping in latent-space with application to depression treatment

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.

Independence estimators for re-randomisation trials in multi-episode settings: a simulation study

Background Re-randomisation trials involve re-enrolling and re-randomising patients for each new treatment episode they experience. They are often used when interest lies in the average effect of an intervention across all the episodes for which it would be used in practice. Re-randomisation trials are often analysed using independence estimators, where a working independence correlation structure is used. However, research into independence estimators in the context of re-randomisation has been limited. Methods We performed a simulation study to evaluate the use of independence estimators in re-randomisation trials. We focussed on a continuous outcome, and the setting where treatment allocation does not affect occurrence of subsequent episodes. We evaluated different treatment effect mechanisms (e.g. by allowing the treatment effect to vary across episodes, or to become less effective on re-use, etc), and different non-enrolment mechanisms (e.g. where patients who experience a poor outcome are less likely to re-enrol for their second episode). We evaluated four different independence estimators, each corresponding to a different estimand (per-episode and per-patient approaches, and added-benefit and policy-benefit approaches). Results We found that independence estimators were unbiased for the per-episode added-benefit estimand in all scenarios we considered. We found independence estimators targeting other estimands (per-patient or policy-benefit) were unbiased, except when there was differential non-enrolment between treatment groups (i.e. when different types of patients from each treatment group decide to re-enrol for subsequent episodes). We found the use of robust standard errors provided close to nominal coverage in all settings where the estimator was unbiased. Conclusions Careful choice of estimand can ensure re-randomisation trials are addressing clinically relevant questions. Independence estimators are a useful approach, and should be considered as the default estimator until the statistical properties of alternative estimators are thoroughly evaluated.

Apixaban compared with warfarin to prevent thrombosis in thrombotic antiphospholipid syndrome: a randomized trial

Thrombotic antiphospholipid syndrome (TAPS) is characterized by venous, arterial, or microvascular thrombosis. Patients with TAPS merit indefinite anticoagulation and warfarin has historically been the standard treatment. Apixaban is an oral factor Xa inhibitor anticoagulant that requires no dose adjustment or monitoring. The efficacy and safety of apixaban compared with warfarin for TAPS patients remain unknown. This multicenter prospective randomized open-label blinded endpoint study assigned anticoagulated TAPS patients to apixaban or warfarin (target INR 2-3) for 12 months. The primary efficacy outcome was clinically overt thrombosis and vascular death. Apixaban was first given at 2.5 mg twice daily. Two protocol changes were instituted based on recommendations from the data safety monitoring board. After the 25th patient was randomized, the apixaban dose was increased to 5mg twice daily, and after the 30th patient was randomized, subjects with prior arterial thrombosis were excluded. Primary outcomes were adjudicated by independent experts blinded to treatment allocation. Patients randomized between 23 February 2015 and 7 March 2019 to apixaban (n=23) or warfarin (n=25) were similar. Among the components of the primary efficacy outcome, only stroke occurred in 6 of 23 patients randomized to apixaban compared with 0 of 25 patients randomized to warfarin. The study ended prematurely after the 48th patient was enrolled. Conclusions from our study are limited due to protocol modifications and low patient accrual. Despite these limitations, our results suggest that apixaban may not be routinely substituted for warfarin to prevent recurrent thrombosis (and especially stokes) among patients with TAPS (ClinicalTrials.gov: NCT02295475).

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Background: Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power. Results: The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during Phase II and III. This approach is based on comparing the performance of the different treatment arm in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm (Rule 1), and treatment selection, removing an arm from the trial permanently (Rule 2). The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package. Conclusion: The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

SARS-CoV-2 blood RNA load predicts outcome in critically ill COVID-19 patients

Background SARS-CoV-2 RNA loads in patient specimens may act as a clinical outcome predictor in critically ill patients with COVID-19. Methods We evaluated the predictive value of viral RNA loads and courses in the blood compared to the upper and lower respiratory tract loads of critically ill COVID-19 patients. Daily specimen collection and viral RNA quantification by RT-qPCR was performed in all consecutive 170 COVID-19 patients between March 2020 and February 2021 during the entire ICU stay (4145 samples analyzed). Patients were grouped according to their 90-days outcome as survivors (n=100) or non-survivors (n=70). Results In non-survivors, blood SARS-CoV-2 RNA loads were significantly higher at the time of admission to the ICU (p=0.0009). Failure of blood RNA clearance was observed in 33/50 (66 %) of the non-survivors compared to 12/64 (19 %) of survivors (p<0.0001). As determined by multivariate analysis, taking sociodemographic and clinical parameters into account, blood SARS-CoV-2 RNA load represents a valid and independent predictor of outcome in critically ill COVID-19 patients (OR [log10]: 0.23 [0.12 – 0.42], p<0.0001) with a significantly higher effect for survival compared to the respiratory tract SARS-CoV-2 RNA loads (OR [log10]: 0.75 [0.66 – 0.85], p<0.0001). Blood RNA loads exceeding 2.51 x 10 3 SARS-CoV-2 RNA copies/ml were found to indicate a 50% probability of death. Consistently, 29/33 (88%) of the non-survivors with failure of virus clearance exceeded this cut-off value constantly. Conclusion Blood SARS-CoV-2 load is an important independent outcome predictor and should be further evaluated for treatment allocation and patient monitoring.

Added Value of a Blinded Outcome Adjudication Committee in an Open-Label Randomized Stroke Trial

Background and Purpose: Blinded outcome assessment in trials with prospective randomized open blinded end point design is challenging. Unblinding can result in misclassified outcomes and biased treatment effect estimates. An outcome adjudication committee assures blinded outcome assessment, but the added value for trials with prospective randomized open blinded end point design and subjective outcomes is unknown. We aimed to assess the degree of misclassification of modified Rankin Scale (mRS) scores by a central assessor and its impact on treatment effect estimates in a stroke trial with prospective randomized open blinded end point design. Methods: We used data from the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). The primary outcome was the mRS at 90 days. Standardized, algorithm-based telephone interviews to assess the mRS were conducted from a central location by an experienced research nurse, unaware but not formally blinded to treatment allocation (central assessor). Masked reports of these interviews were adjudicated by a blinded outcome committee. Misclassification was defined as an incorrect classification of the mRS by the central assessor. The effect of endovascular treatment on the mRS was assessed with multivariable ordinal logistic regression. Results: In MR CLEAN, 53/500 (10.6%) of the mRS scores were misclassified. The degree and direction of misclassification did not differ between treatment arms ( P =0.59). Benefit of endovascular treatment was shown on the mRS when scored by the central assessor (adjusted common odds ratio, 1.60 [95% CI, 1.16–2.21]) and the outcome adjudication committee (adjusted common odds ratio, 1.67 [95% CI, 1.21–2.20]). Conclusions: Misclassification by the central assessor was small, randomly distributed over treatment arms, and did not affect treatment effect estimates. This study suggests that the added value of a blinded outcome adjudication committee is limited in a stroke trial with prospective randomized open blinded end point design applying standardized, algorithm-based outcome assessment by a central assessor, who is unaware but not formally blinded to treatment allocation. REGISTRATION: URL: https://www.isrctn.com ; Unique identifier: ISRCTN10888758.