Randomization in substance abuse clinical trials

Abstract

Background: A well designed randomized clinical trial rates as the highest level of evidence for a particular intervention's efficacy. Randomization, a fundamental feature of clinical trials design, is a process invoking the use of probability to assign treatment interventions to patients. In general, randomization techniques pursue the goal of providing objectivity to the assignment of treatments, while at the same time balancing for treatment assignment totals and covariate distributions. Numerous randomization techniques, each with varying properties of randomness and balance, are suggested in the statistical literature. This paper reviews common randomization techniques often used in substance abuse research and an application from a National Institute on Drug Abuse (NIDA)-funded clinical trial in substance abuse is used to illustrate several choices an investigator faces when designing a clinical trial.

Results: Comparisons and contrasts of randomization schemes are provided with respect to deterministic and balancing properties. Specifically, Monte Carlo simulation is used to explore the balancing nature of randomization techniques for moderately sized clinical trials. Results demonstrate large treatment imbalance for complete randomization with less imbalance for the urn or adaptive scheme. The urn and adaptive randomization methods display smaller treatment imbalance as demonstrated by the low variability of treatment allocation imbalance. For all randomization schemes, covariate imbalance between treatment arms was small with little variation between adaptive schemes, stratified schemes and unstratified schemes given that sample sizes were moderate to large.

Conclusion: We develop this paper with the goal of reminding substance abuse researchers of the broad array of randomization options available for clinical trial designs. There may be too quick a tendency for substance abuse researchers to implement the fashionable urn randomization schemes and other highly adaptive designs. In many instances, simple or blocked randomization with stratification on a major covariate or two will accomplish the same objectives as an urn or adaptive design, and it can do so with more simply implemented schedules and without the dangers of overmatching. Furthermore, the proper analysis, fully accounting for the stratified design, can be conducted.

Figure 1

Adaptive (2 covariates), urn and complete randomization (n = 264, treatments = 3, simulations = 1000).

Figure 2

Complete and adaptive randomization: cocaine covariate imbalance (treatments = 3 simulations = 1000).

Figure 3

Complete and adaptive randomization: gender covariate imbalance (treatments = 3 simulations = 1000).

Figure 4

Non-stratifying factors: stratified simple vs. adaptive (5 covariates) for n = 66 and n = 264.

Figure 5

Non-stratifying factors: stratified simple vs. adaptive (5 covariates) for n = 66 and n = 264.

Figure 6

Non-stratifying factors: stratified simple vs. adaptive (5 covariates) for n = 66 and n = 264.