Understanding an impact of patient enrollment pattern on predictability of central (unstratified) randomization in a multi-center clinical trial.

In a multi-center randomized controlled trial (RCT) with competitive recruitment, eligible patients are enrolled sequentially by different study centers and are randomized to treatment groups using the chosen randomization method. Given the stochastic nature of the recruitment process, some centers may enroll more patients than others, and in some instances, a center may enroll multiple patients in a row, for example, on a given day. If the study is open-label, the investigators might be able to make intelligent guesses on upcoming treatment assignments in the randomization sequence, even if the trial is centrally randomized and not stratified by center. In this paper, we use enrollment data inspired by a real multi-center RCT to quantify the susceptibility of two restricted randomization procedures, the permuted block design and the big stick design, to selection bias under the convergence strategy of Blackwell and Hodges (1957) applied at the center level. We provide simulation evidence that the expected proportion of correct guesses may be greater than 50% (i.e., an increased risk of selection bias) and depends on the chosen randomization method and the number of study patients recruited by a given center that takes consecutive positions on the central allocation schedule. We propose some strategies for ensuring stronger encryption of the randomization sequence to mitigate the risk of selection bias.

Steady-state statistical properties and implementation of randomization designs with maximum tolerated imbalance restriction for two-arm equal allocation clinical trials.

In recent decades, several randomization designs have been proposed in the literature as better alternatives to the traditional permuted block design (PBD), providing higher allocation randomness under the same restriction of the maximum tolerated imbalance (MTI). However, PBD remains the most frequently used method for randomizing subjects in clinical trials. This status quo may reflect an inadequate awareness and appreciation of the statistical properties of these randomization designs, and a lack of simple methods for their implementation. This manuscript presents the analytic results of statistical properties for five randomization designs with MTI restriction based on their steady-state probabilities of the treatment imbalance Markov chain and compares them to those of the PBD. A unified framework for randomization sequence generation and real-time on-demand treatment assignment is proposed for the straightforward implementation of randomization algorithms with explicit formulas of conditional allocation probabilities. Topics associated with the evaluation, selection, and implementation of randomization designs are discussed. It is concluded that for two-arm equal allocation trials, several randomization designs offer stronger protection against selection bias than the PBD does, and their implementation is not necessarily more difficult than the implementation of the PBD.

Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations.

BACKGROUND: The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process. METHODS: We considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize n = 500 patients. Statistical properties of the recruitment process and the randomization procedures were assessed using Monte Carlo simulations. The operating characteristics included time to complete recruitment, number of centers that recruited a given number of patients, several measures of treatment imbalance and estimation efficiency under a linear model for the response, the expected proportions of correct guesses under two different guessing strategies, and the expected proportion of deterministic assignments in the allocation sequence. RESULTS: Maximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance-randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients-which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness-balance tradeoff. CONCLUSIONS: The choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment.

A roadmap to using randomization in clinical trials.

BACKGROUND: Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial. METHODS: We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice. RESULTS: Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size. CONCLUSIONS: The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

Seasonal variations in exacerbations and deaths in patients with COPD during the TIOSPIR® trial.

Background: Although COPD exacerbations are known to occur more frequently in winter, there is little information on hospitalizations and cause-specific mortality. This study aimed to examine seasonal variations in mortality and exacerbations in patients with COPD during the TIOtropium Safety and Performance In Respimat® (TIOSPIR®) trial. Patients and methods: TIOSPIR was a large-scale, multicenter trial, which assessed the safety and efficacy of tiotropium delivered via HandiHaler® (18 µg once daily) or Respimat® Soft Mist™ (2.5 or 5 µg once daily) inhaler in patients with COPD. Patients were aged ≥40 years, with a smoking history ≥10 pack-years, and post-bronchodilator forced expiratory volume in 1 second ≤70% and forced expiratory volume in 1 second/forced vital capacity ≤0.70. COPD exacerbations and deaths were monitored throughout the trial. The data were pooled to examine seasonal patterns. Southern hemisphere data were shifted by 6 months to align with northern hemisphere seasons. Results: TIOSPIR was conducted in 43 northern (n=15,968) and 7 southern (n=1,148) hemisphere (n=1,148) countries. The median duration of treatment was 835 days, with a mean follow-up of 2.3 years. Among 19,494 exacerbations, there were clear seasonal differences (winter, 6,646 [34.1%]; spring, 4,515 [23.2%]; summer, 3,198 [16.4%]; autumn, 5,135 [26.3%]). Exacerbations peaked in early winter (December in the northern hemisphere and June in the southern hemisphere), respiratory hospitalizations in midwinter, and respiratory deaths in early spring. Conclusion: Although winter poses a 2-fold hazard for COPD exacerbations vs summer, respiratory deaths peak in early spring. These data suggest that seasonal intensification of preventive treatments may impact COPD morbidity and mortality. Trial registration number: NCT01126437.

Tiotropium and olodaterol in the prevention of chronic obstructive pulmonary disease exacerbations (DYNAGITO): a double-blind, randomised, parallel-group, active-controlled trial.

BACKGROUND: Combinations of long-acting bronchodilators are recommended to reduce the rate of chronic obstructive pulmonary disease (COPD) exacerbations. It is unclear whether combining olodaterol, a long-acting beta-agonist, with tiotropium, a long-acting anti-muscarinic, reduces the rate of exacerbations compared with tiotropium alone. METHODS: This 52-week, double-blind, randomised, parallel-group, active-controlled trial randomly assigned (1:1) patients with COPD with a history of exacerbations using a randomised block design to receive tiotropium-olodaterol 5 µg-5 µg or tiotropium 5 µg once daily. Patients using inhaled corticosteroids continued this therapy. Treatment was masked to patients, investigators, and those involved in analysing the data. The primary endpoint was the rate of moderate and severe COPD exacerbations from the first dose of medication until 1 day after last drug administration. The primary analysis included all randomly assigned patients who received any dose of study medication but were not from a site excluded due to on-site protocol violations. The trial is registered with ClinicalTrials.gov, number NCT02296138. FINDINGS: Overall, 9009 patients were screened from 818 centres in 51 countries. We recruited 7880 patients between Jan 22, 2015 and March 7, 2016 (mean age 66·4 years [SD 8·5], 5626 [71%] were men, mean FEV1 percent predicted 44·5% [SD 27·7]): 3939 received tiotropium-olodaterol and 3941 tiotropium. The rate of moderate and severe exacerbations was lower with tiotropium-olodaterol than tiotropium (rate ratio [RR] 0·93, 99% CI 0·85-1·02; p=0·0498), not meeting the targeted 0·01 significance level. The proportion of patients reporting adverse events was similar between treatments. INTERPRETATION: Combining tiotropium and olodaterol did not reduce exacerbation rate as much as expected compared with tiotropium alone. FUNDING: Boehringer Ingelheim International GmbH.

Differences in outcomes between GOLD groups in patients with COPD in the TIOSPIR(®) trial.

BACKGROUND: The aim of this study was to evaluate whether Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification could predict mortality risk factors and whether baseline treatment intensity would relate to mortality within each group, using data from TIOSPIR(®), the largest randomized clinical trial in COPD performed to date. METHODS: A total of 17,135 patients from TIOSPIR(®) were pooled and grouped by GOLD grading (A-D) according to baseline Medical Research Council breathlessness score, exacerbation history, and spirometry. All-cause mortality and adjudicated cardiovascular (CV) and respiratory mortality were assessed. RESULTS: Of the 16,326 patients classified, 1,248 died on treatment. Group B patients received proportionally more CV treatment at baseline. CV mortality risk, but not all-cause mortality risk, was significantly higher in Group B than Group C patients (CV mortality - hazard ratio [HR] =1.74, P=0.004; all-cause mortality - HR =1.18, P=0.11). Group D patients had a higher incidence of all-cause mortality than Group B patients (10.9% vs 6.6%). Similar trends were observed regardless of respiratory or CV medication at baseline. In contrast, respiratory deaths increased consistently from Groups A-D (0.3%, 0.8%, 1.6%, and 4.2% of patients, respectively). CONCLUSION: The data obtained from the TIOSPIR(®) trial, supporting earlier studies, suggest that proportionally more CV medication and CV deaths occur in GOLD Group B COPD patients, although deaths attributed to respiratory causes are more prevalent in Groups C and D.