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.

Influence of common polymorphisms in the SLC5A2 gene on metabolic traits in subjects at increased risk of diabetes and on response to empagliflozin treatment in patients with diabetes.

OBJECTIVE: Inhibition of the renal sodium-glucose cotransporter 2 (SGLT2) is a novel concept in the therapy of diabetes mellitus. In this study, we first assessed whether common single nucleotide polymorphisms (SNPs) in the SGLT2-encoding gene SLC5A2 affect diabetes-related metabolic traits in subjects at risk for type 2 diabetes and, second, whether these have pharmacogenetic relevance by interfering with the response to empagliflozin treatment in patients with type 2 diabetes. PATIENTS AND METHODS: Samples from a metabolically well-phenotyped cross-sectional study population (total N=2600) at increased risk for type 2 diabetes and pooled pharmacogenetic samples from patients from four phase III trials of empagliflozin (in total: 603 receiving empagliflozin, 305 receiving placebo) were genotyped for five common SNPs (minor allele frequencies ≥5%) present in the SLC5A2 gene locus. RESULTS: In the cross-sectional study, none of the SLC5A2 SNPs significantly influenced metabolic traits such as body fat, insulin sensitivity/resistance, insulin release, HbA1c, plasma glucose, or systolic blood pressure when multiple testing was taken into account (all P≥0.0083). Further, no relevant effect on response to treatment with empagliflozin on HbA1c, fasting glucose, weight, or systolic blood pressure was observed for the SNPs tested in the pharmacogenetic study. CONCLUSION: Common genetic variants in the SLC5A2 gene neither affects diabetes-related metabolic traits nor have a clinically relevant impact on response to treatment with the SGLT2 inhibitor empagliflozin.