Rampe: Randomization allocation method performance evaluation

Alan Montgomery, Reuben Ogollah, Christopher Partlett and Cydney Bruce
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Alan Montgomery: University of Nottingham
Reuben Ogollah: University of Nottingham
Christopher Partlett: University of Nottingham
Cydney Bruce: University of Nottingham

UK Stata Conference 2025 from Stata Users Group

Abstract: When designing and conducting a randomized controlled trial, there are a variety of randomization methods to choose from, but limited evidence on the performance of the methods under speciRc study designs. The rampe package contains 12 metrics designed to measure the balance and predictability of randomization sequences in Stata. This will allow researchers to easily compare method performance using data that mirrors the speciRc trial that is being designed. Balance metrics: Measured both as the greatest imbalance observed throughout recruitment and as the Rnal imbalance once the target sample size is achieved. groupimbalance: Measures the imbalance between the expected and observed ratio of participants in each treatment group. charimbalance: Measures the greatest imbalance observed across a set of covariates and the average imbalance across covariates. Predictability metrics: Measured as the proportion of correct guesses for a variety of prediction strategies. This is calculated for the whole sequence and assuming that recruiting sites have information only about previous allocations at their own site. Alternation recruiter assumes the next allocation is the one least recently allocated. backtheloser: Recruiter assumes the next allocation is the one with the fewest previous allocations. predbalance: Recruiter assumes the next allocation is the group with the smallest marginal total across randomization covariates. In this talk, I will describe each of the developed metrics in more detail, discuss the interpretation of each metric, and demonstrate with an example how this package can be used in practice.

Date: 2025-09-04
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