Powerful sequential designs using Bayesian estimation: A power analysis tutorial using brms, the tidyverse, and furrr

Abstract

Producing compelling and trustworthy results relies upon performing well-powered studies with low rates of misleading evidence. Yet, resources are limited, and maximum sample sizes required to achieve acceptable power in typical fixed N designs may be disconcerting. ‘Sequential’, ‘optional stopping’, or ‘interim’ designs – in which results may be checked at interim periods and a decision made as to whether to continue data collection or not – provide one means by which researchers may be able to achieve high power and low false positive rates with less of a resource burden. Sequential analyses have received considerable attention from both frequentist and Bayesian hypothesis testing approaches, but fewer approachable resources are available for those wishing to use Bayesian estimation. In this tutorial, we cover a general process for performing power analyses of fixed and sequential designs using Bayesian estimation – simulating data, performing regressions in parallel to reduce time requirements, choosing different stopping criteria and data collection sequences, and calculating observed power and rates of misleading evidence. We conclude with a discussion of some limitations and possible extensions of the presented approach.

Effectiveness and mechanisms of a multimodal treatment for low back pain: a pragmatic matched cohort study

“…We conducted a Bayesian, sequential analysis [9] using a simple change score and a baseline-adjusted model without additional covariates for the outcomes pain and disability. This was done for all 8 matching scenarios.…”