PT  - JOURNAL ARTICLE
AU  - Christopher J Weir
AU  - Adrian W Bowman
TI  - Density strips: visualisation of uncertainty in clinical data summaries and research findings
AID  - 10.1136/bmjebm-2021-111746
DP  - 2022 Dec 01
TA  - BMJ Evidence-Based Medicine
PG  - 373--377
VI  - 27
IP  - 6
4099  - http://ebm.bmj.com/content/27/6/373.short
4100  - http://ebm.bmj.com/content/27/6/373.full
SO  - BMJ EBM2022 Dec 01; 27
AB  - 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.