Using the half normal distribution to quantify covariate balance in cluster-randomized pragmatic trials

Background

Pragmatic trials often consist of cluster-randomized controlled trials (C-RCTs), where staff of existing clinics or sites deliver interventions and randomization occurs at the site level. Covariate-constrained randomization (CCR) methods are often recommended to minimize imbalance on important site characteristics across intervention and control arms because sizable imbalances can occur by chance in simple randomizations when the number of units to be randomized is relatively small. CCR methods involve multiple random assignments initially, an assessment of balance achieved on site-level covariates from each randomization, and the final selection of an allocation that produces acceptable balance. However, no clear consensus exists on how to assess imbalance or identify allocations with sufficient balance. In this article, we describe an overall imbalance index (I) that is based on the mean of the absolute value of the standardized differences in means on the site characteristics.

Methods

We derive the theoretical distribution of I, then conduct simulation studies to examine its empirical properties under the varying covariate distributions and inter-correlations.

Results

I has an expected value of 0.798 and, assuming independent site characteristics, a variance of 0.363/k, where k is the number of site characteristics being balanced. Simulations indicated that the properties of I are robust under varying covariate circumstances as long as k is greater than 3 and the covariates are not too highly inter-correlated.

Conclusions

We recommend that values of I below the 10th percentile indicate sufficient overall site balance in CCRs. Definitions of acceptable randomizations might also include individual covariate criteria specified in advance, in addition to overall balance criteria.

Blocking and re-arrangement of pots in greenhouse experiments: which approach is more effective?

BACKGROUND

Observations measured in field and greenhouse experiments always contain errors. These errors can arise from measurement error, local or positional conditions of the experimental units, or from the randomization of experimental units. In statistical analysis errors can be modelled as independent effects or as spatially correlated effects with an appropriate variance-covariance structure. Using a suitable experimental design, a part of the variance can be captured through blocking of the experimental units. If experimental units (e.g. pots within a greenhouse) are mobile, they can be re-arranged during the experiment. This re-arrangement enables a separation of variation due to time-invariant position effects and variation due to the experimental units. If re-arrangement is successful, the time-invariant positional effect can average out for experimental units moved between different positions during the experiment. While re-arrangement is commonly done in greenhouse experiments, data to quantify its usefulness is limited.

RESULTS

A uniformity greenhouse experiment with barley (Hordeum vulgare L.) to compare re-arrangement of pots with a range of designs under fixed-position arrangement showed that both methods can reduce the residual variance and the average standard error of a difference. All designs with fixed-position arrangement, which accounted for the known north-south gradient in the greenhouse, outperformed re-arrangement. An α-design with block size four performed best across seven plant growth traits.

CONCLUSION

Blocking with a fixed-position arrangement was more efficient in improving precision of greenhouse experiments than re-arrangement of pots and hence can be recommended for comparable greenhouse experiments.

Re-randomization increased recruitment and provided similar treatment estimates as parallel designs in trials of febrile neutropenia

Objective

Re-randomization trials allow patients to be re-enrolled for multiple treatment episodes. However, it remains uncertain to what extent re-randomization improves recruitment compared to parallel group designs or whether treatment estimates might be affected.

Study Design and Setting

We evaluated trials included in a recent Cochrane review of granulocyte colony-stimulating factors for patients with febrile neutropenia. We assessed the recruitment benefits of re-randomization trials; compared treatment effect estimates between re-randomization and parallel group designs; and assessed whether re-randomization led to higher rates of non-compliance and loss to follow-up in subsequent episodes.

Results

We included 14 trials (5 re-randomization and 9 parallel group). The re-randomization trials recruited a median of 25% (range 16–66%) more episodes on average than they would have under a parallel-group design. Treatment effect estimates were similar between re-randomization and parallel group trials across all outcomes, though confidence intervals were wide. The re-randomization trials in this review reported no loss to follow-up and low rates of non-compliance (median 1.7%, range 0–8.9%).

Conclusions

In the setting of febrile neutropenia, re-randomization increased recruitment while providing similar estimates of treatment effect to parallel group trials, with minimal loss to follow-up or non-compliance. It appears to be safe and efficient alternative to parallel group designs in this setting.

Using re-randomization to increase the recruitment rate in clinical trials - an assessment of three clinical areas

Background

Patient recruitment in clinical trials is often challenging, and as a result, many trials are stopped early due to insufficient recruitment. The re-randomization design allows patients to be re-enrolled and re-randomized for each new treatment episode that they experience. Because it allows multiple enrollments for each patient, this design has been proposed as a way to increase the recruitment rate in clinical trials. However, it is unknown to what extent recruitment could be increased in practice.

Methods

We modelled the expected recruitment rate for parallel-group and re-randomization trials in different settings based on estimates from real trials and datasets. We considered three clinical areas: in vitro fertilization, severe asthma exacerbations, and acute sickle cell pain crises. We compared the two designs in terms of the expected time to complete recruitment, and the sample size recruited over a fixed recruitment period.

Results

Across the different scenarios we considered, we estimated that re-randomization could reduce the expected time to complete recruitment by between 4 and 22 months (relative reductions of 19% and 45%), or increase the sample size recruited over a fixed recruitment period by between 29% and 171%. Re-randomization can increase recruitment most for trials with a short follow-up period, a long trial recruitment duration, and patients with high rates of treatment episodes.

Conclusions

Re-randomization has the potential to increase the recruitment rate in certain settings, and could lead to quicker and more efficient trials in these scenarios.

A small n sequential multiple assignment randomized trial design for use in rare disease research

BACKGROUND

Clinical trials in rare diseases are difficult to conduct due to the limited number of patients available with each disorder. We developed a Phase 2 trial which is a small n sequential multiple assignment randomized trial (snSMART) design to test several treatments for a rare disease for which no standard therapy exists.

PURPOSE

This paper illustrates the design, sample size estimation and operating characteristics of an snSMART.

METHODS

We investigate the performance of a class of weighted Z statistics via computer simulations.

RESULTS

We demonstrate the increase in power over traditional single stage designs, and indicate how the power changes as a function of the weight given to each stage.

CONCLUSION

The snSMART design is promising in a rare disease setting where several alternative treatments are under consideration and small sample sizes are necessary.