Density strips: visualisation of uncertainty in clinical data summaries and research findings

Christopher J Weir*, Adrian Bowman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The disproportionate focus on statistical significance in reporting and interpreting clinical research studies contributes to publication bias and encourages selective reporting. This highlights a need for alternative approaches that clearly communicate the uncertainty in the data, enabling researchers to provide a more nuanced interpretation of clinical research findings.

Our purpose in this article is to introduce the density strip method as one potential approach that might act as a bridge between data visualisation for descriptive purposes and formal statistical inference. We build on existing theory, translating it to the applied research context to illustrate its utility to clinical researchers.

We achieve this by considering an exemplar clinical trial, Multiple Sclerosis-Secondary Progressive Multi-Arm Randomisation Trial (MS-SMART). MS-SMART was a multiarm randomised placebo-controlled trial of three potentially neuroprotective drugs in secondary progressive MS. We illustrate through MS-SMART the potential of the density strip as an effective visualisation of the distribution of clinical trial outcomes and as a complementary approach to aid the interpretation of formal, inferential, statistical analysis.

We conclude by summarising the advantages and disadvantages of the density strip methodology and provide suggestions for its potential extensions and possible further uses.
Original languageEnglish
JournalBMJ Evidence-Based Medicine
Early online date21 Dec 2021
DOIs
Publication statusE-pub ahead of print - 21 Dec 2021

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