Some practical problems in implementing randomization

Minimization in randomized clinical trials

In randomized trials, comparability of the treatment groups is ensured through allocation of treatments using a mechanism that involves some random element, thus controlling for confounding of the treatment effect. Completely random allocation ensures comparability between the treatment groups for all known and unknown prognostic factors. For a specific trial, however, imbalances in prognostic factors among the treatment groups may occur. Although accidental bias can be avoided in the presence of such imbalances by stratifying the analysis, most trialists, regulatory agencies, and other stakeholders prefer a balanced distribution of prognostic factors across the treatment groups. Some procedures attempt to achieve balance in baseline covariates, by stratifying the allocation for these covariates, or by dynamically adapting the allocation using covariate information during the trial (covariate-adaptive procedures). In this Tutorial, the performance of minimization, a popular covariate-adaptive procedure, is compared with two other commonly used procedures, completely random allocation and stratified blocked designs. Using individual patient data of 2 clinical trials (in advanced ovarian cancer and age-related macular degeneration), the procedures are compared in terms of operating characteristics (using asymptotic and randomization tests), predictability of treatment allocation, and achieved balance. Fifty actual trials of various sizes that applied minimization for treatment allocation are used to investigate the achieved balance. Implementation issues of minimization are described. Minimization procedures are useful in all trials but especially when (1) many major prognostic factors are known, (2) many centers of different sizes accrue patients, or (3) the trial sample size is moderate.

A comparative study of Bayesian optimal interval (BOIN) design with interval 3+3 (i3+3) design for phase I oncology dose-finding trials

Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.

Compound salvia pellet might be more effective and safer for chronic stable angina pectoris compared with nitrates: A systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Chronic stable angina (CSA) resulted in a considerable burden for both individuals and the society. In this study we aimed to critically evaluate the effectiveness and safety of Compound salvia pellet compared with nitrates in the treatment of Chronic Stable Angina (CSA) pectoris, and to provide more credible evidence for clinical practice.

METHODS

A comprehensive and exhaustive search strategy was formulated to identify potential RCTs of compound salvia pellet for CSA in international and Chinese databases from their inception to July 4th, 2018. We also searched the bibliographies of relevant studies. Two reviewers independently assessed the quality of included trials by using Cochrane Risk of Bias Tool.

RESULTS

The literature search yielded 1849 citations and 51 RCTs (n = 4732) were included for meta-analysis after titles, abstracts and full text selection according to eligibility criteria. The pooled results suggested that compound salvia pellet was much more effective than nitrates in the improvement of angina symptoms (therapy = 4 weeks, RR = 1.23, 95%CI = [1.17, 1.30], P < .001, I = 0%; therapy = 4 weeks, RR = 1.13, 95%CI = [1.08, 1.17], P < .001, I = 45.6%), and ECG test (therapy = 4 weeks, RR = 1.24, 95%CI [1.14, 1.35], P < .001, I = 51.5%; and therapy > 4 weeks, RR = 1.30, 95%CI[1.20, 1.42], P < .001, I = 36.4%) in CSA. Compared with nitrates, the percentage of patients with adverse events significantly decreased when prescribed with compound salvia pellet (3.2% vs 17.0%).

CONCLUSION

Compound salvia pellet might be more effective on the improvement of angina symptoms, ECG test and with few adverse events compared with nitrates. While there are some limitations in this study, which may weaken the results, we believe the findings could provide useful information for stakeholders concerned with outcomes in patients with CSA. More rigorous RCTs with high quality are needed to confirm these findings.

Prevalence and reporting of recruitment, randomisation and treatment errors in clinical trials: A systematic review

Background/aims In clinical trials, it is not unusual for errors to occur during the process of recruiting, randomising and providing treatment to participants. For example, an ineligible participant may inadvertently be randomised, a participant may be randomised in the incorrect stratum, a participant may be randomised multiple times when only a single randomisation is permitted or the incorrect treatment may inadvertently be issued to a participant at randomisation. Such errors have the potential to introduce bias into treatment effect estimates and affect the validity of the trial, yet there is little motivation for researchers to report these errors and it is unclear how often they occur. The aim of this study is to assess the prevalence of recruitment, randomisation and treatment errors and review current approaches for reporting these errors in trials published in leading medical journals. Methods We conducted a systematic review of individually randomised, phase III, randomised controlled trials published in New England Journal of Medicine, Lancet, Journal of the American Medical Association, Annals of Internal Medicine and British Medical Journal from January to March 2015. The number and type of recruitment, randomisation and treatment errors that were reported and how they were handled were recorded. The corresponding authors were contacted for a random sample of trials included in the review and asked to provide details on unreported errors that occurred during their trial. Results We identified 241 potentially eligible articles, of which 82 met the inclusion criteria and were included in the review. These trials involved a median of 24 centres and 650 participants, and 87% involved two treatment arms. Recruitment, randomisation or treatment errors were reported in 32 in 82 trials (39%) that had a median of eight errors. The most commonly reported error was ineligible participants inadvertently being randomised. No mention of recruitment, randomisation or treatment errors was found in the remaining 50 of 82 trials (61%). Based on responses from 9 of the 15 corresponding authors who were contacted regarding recruitment, randomisation and treatment errors, between 1% and 100% of the errors that occurred in their trials were reported in the trial publications. Conclusion Recruitment, randomisation and treatment errors are common in individually randomised, phase III trials published in leading medical journals, but reporting practices are inadequate and reporting standards are needed. We recommend researchers report all such errors that occurred during the trial and describe how they were handled in trial publications to improve transparency in reporting of clinical trials.

Mobile access to virtual randomization for investigator-initiated trials

Background/aims Randomization is indispensable in clinical trials in order to provide unbiased treatment allocation and a valid statistical inference. Improper handling of allocation lists can be avoided using central systems, for example, human-based services. However, central systems are unaffordable for investigator-initiated trials and might be inaccessible from some places, where study subjects need allocations. We propose mobile access to virtual randomization, where the randomization lists are non-existent and the appropriate allocation is computed on demand. Methods The core of the system architecture is an electronic data capture system or a clinical trial management system, which is extended by an R interface connecting the R server using the Java R Interface. Mobile devices communicate via the representational state transfer web services. Furthermore, a simple web-based setup allows configuring the appropriate statistics by non-statisticians. Our comprehensive R script supports simple randomization, restricted randomization using a random allocation rule, block randomization, and stratified randomization for un-blinded, single-blinded, and double-blinded trials. For each trial, the electronic data capture system or the clinical trial management system stores the randomization parameters and the subject assignments. Results Apps are provided for iOS and Android and subjects are randomized using smartphones. After logging onto the system, the user selects the trial and the subject, and the allocation number and treatment arm are displayed instantaneously and stored in the core system. So far, 156 subjects have been allocated from mobile devices serving five investigator-initiated trials. Conclusion Transforming pre-printed allocation lists into virtual ones ensures the correct conduct of trials and guarantees a strictly sequential processing in all trial sites. Covering 88% of all randomization models that are used in recent trials, virtual randomization becomes available for investigator-initiated trials and potentially for large multi-center trials.

Applying the intention-to-treat principle in practice: Guidance on handling randomisation errors

Background:

The intention-to-treat principle states that all randomised participants should be analysed in their randomised group. The implications of this principle are widely discussed in relation to the analysis, but have received limited attention in the context of handling errors that occur during the randomisation process. The aims of this article are to (1) demonstrate the potential pitfalls of attempting to correct randomisation errors and (2) provide guidance on handling common randomisation errors when they are discovered that maintains the goals of the intention-to-treat principle.

Methods:

The potential pitfalls of attempting to correct randomisation errors are demonstrated and guidance on handling common errors is provided, using examples from our own experiences.

Results:

We illustrate the problems that can occur when attempts are made to correct randomisation errors and argue that documenting, rather than correcting these errors, is most consistent with the intention-to-treat principle. When a participant is randomised using incorrect baseline information, we recommend accepting the randomisation but recording the correct baseline data. If ineligible participants are inadvertently randomised, we advocate keeping them in the trial and collecting all relevant data but seeking clinical input to determine their appropriate course of management, unless they can be excluded in an objective and unbiased manner. When multiple randomisations are performed in error for the same participant, we suggest retaining the initial randomisation and either disregarding the second randomisation if only one set of data will be obtained for the participant, or retaining the second randomisation otherwise. When participants are issued the incorrect treatment at the time of randomisation, we propose documenting the treatment received and seeking clinical input regarding the ongoing treatment of the participant.

Conclusion:

Randomisation errors are almost inevitable and should be reported in trial publications. The intention-to-treat principle is useful for guiding responses to randomisation errors when they are discovered.

Training Cameroonian researchers on pragmatic knowledge translation trials: a workshop report

Limited health research capacity in one of the factors that prevents developing countries from attaining optimal health outcomes and achieving the Millennium Development Goals. We report here, the details of a workshop on pragmatic knowledge translation trials for Cameroonian researchers, the material covered and additional resources to support capacity development. At the end of this workshop, knowledge gains were noted and participants were able to initiate proposals for funding. These proposals were aimed at improving the clinical management of diabetes, hypertension and malaria.