Replacing bar graphs of continuous data with more informative graphics: Are we making progress?

Randomised controlled trial of HOYA one-day multifocal contact lenses: The HOMCL trial

Trial design

Double-masked crossover RCT (Research Registry: #8136) comparison of a new HOYA one-day disposable multifocal contact lens (HOMCL) with Alcon DAILIES TOTAL1® Multifocal (ADT1).

Methods

Sixty presbyopic participants from three countries attended for baseline measurements and fitting of both lens types and then for a fortnight completed daily diaries of symptoms with habitual optical correction, and VF-14 questionnaire. Participants collected either HOMCL or ADT1, when the vision was measured again, and they wore this product for a fortnight, completing daily diaries and VF-14. Participants then collected the other type and had vision and symptoms recorded in the same way.

Results

There were no serious adverse events. Primary outcomes were no significant differences between the lens types in willingness to purchase nor stated preference; no significant differences in the daily symptom ratings of comfort; statistically significant findings with the daily symptom ratings and VF-14 of better near vision with HOMCL and better distance vision with ADT1. Secondary outcomes were better high contrast distance visual acuity with ADT1 (<1 line), better low contrast distance visual acuity with ADT1; faster Wilkins rate of reading test with HOMCL; no significant differences in near visual acuity, wearing time, or number of lenses required during fitting process. Exploratory analyses were better handling scores with ADT1; and some, but not all of the dry eye data indicating better acceptance of HOMCL by patients with relatively dry eyes. For all measures, there were some participants who preferred/performed better with each lens type.

Conclusions

The differences between the performance of the two products were small. There was a trend in some clinical measurements and the daily diary data and VF-14 questionnaire, for HOMCL to outperform ADT1 for near vision and vice versa for distance vision.

Sea stack plots: Replacing bar charts with histograms

Graphs in research articles can increase the comprehension of statistical data but may mislead readers if poorly designed. We propose a new plot type, the sea stack plot, which combines vertical histograms and summary statistics to represent large univariate datasets accurately, usefully, and efficiently. We compare five commonly used plot types (dot and whisker plots, boxplots, density plots, univariate scatter plots, and dot plots) to assess their relative strengths and weaknesses when representing distributions of data commonly observed in biological studies. We find the assessed plot types are either difficult to read at large sample sizes or have the potential to misrepresent certain distributions of data, showing the need for an improved method of data visualisation. We present an analysis of the plot types used in four ecology and conservation journals covering multiple areas of these research fields, finding widespread use of uninformative bar charts and dot and whisker plots (60% of all panels showing univariate data from multiple groups for the purpose of comparison). Some articles presented more informative figures by combining plot types (16% of panels), generally boxplots and a second layer such as a flat density plot, to better display the data. This shows an appetite for more effective plot types within conservation and ecology, which may further increase if accurate and user-friendly plot types were made available. Finally, we describe sea stack plots and explain how they overcome the weaknesses associated with other alternatives to uninformative plots when used for large and/or unevenly distributed data. We provide a tool to create sea stack plots with our R package 'seastackplot', available through GitHub.

Dynamite plots in surgical research over 10 years: a meta-study using machine-learning analysis

PURPOSE

Bar charts of numerical data, often known as dynamite plots, are unnecessary and misleading. Their tendency to alter the perception of mean's position through the within-the-bar bias and their lack of information on the distribution of the data are two of numerous reasons. The machine learning tool, Barzooka, can be used to rapidly screen for different graph types in journal articles.We aim to determine the proportion of original research articles using dynamite plots to visualize data, and whether there has been a change in their use over time.

METHODS

Original research articles in nine surgical fields of research were sampled based on MeSH terms and then harvested using the Python-based biblio-glutton-harvester tool. After harvesting, they were analysed using Barzooka. Over 40 000 original research articles were included in the final analysis. The results were adjusted based on previous validation data with 95% confidence bounds. Kendall τ coefficient with the Mann-Kendall test for significance was used to determine the trend of dynamite plot use over time.

RESULTS

Eight surgical fields of research showed a statistically significant decrease in use of dynamite plots over 10 years. Oral and maxillofacial surgery showed no significant trend in either direction. In 2022, use of dynamite plots, dependent on field and 95% confidence bounds, ranges from ~30% to 70%.

CONCLUSION

Our results show that the use of dynamite plots in surgical research has decreased over time; however, use remains high. More must be done to understand this phenomenon and educate surgical researchers on data visualization practices.

Reporting and transparent research practices in sports medicine and orthopaedic clinical trials: a meta-research study

OBJECTIVES

Transparent reporting of clinical trials is essential to assess the risk of bias and translate research findings into clinical practice. While existing studies have shown that deficiencies are common, detailed empirical and field-specific data are scarce. Therefore, this study aimed to examine current clinical trial reporting and transparent research practices in sports medicine and orthopaedics.

SETTING

Exploratory meta-research study on reporting quality and transparent research practices in orthopaedics and sports medicine clinical trials.

PARTICIPANTS

The sample included clinical trials published in the top 25% of sports medicine and orthopaedics journals over 9 months.

PRIMARY AND SECONDARY OUTCOME MEASURES

Two independent reviewers assessed pre-registration, open data and criteria related to scientific rigour, like randomisation, blinding, and sample size calculations, as well as the study sample, and data analysis.

RESULTS

The sample included 163 clinical trials from 27 journals. While the majority of trials mentioned rigour criteria, essential details were often missing. Sixty per cent (95% confidence interval (CI) 53% to 68%) of trials reported sample size calculations, but only 32% (95% CI 25% to 39%) justified the expected effect size. Few trials indicated the blinding status of all main stakeholders (4%; 95% CI 1% to 7%). Only 18% (95% CI 12% to 24%) included information on randomisation type, method and concealed allocation. Most trials reported participants' sex/gender (95%; 95% CI 92% to 98%) and information on inclusion and exclusion criteria (78%; 95% CI 72% to 84%). Only 20% (95% CI 14% to 26%) of trials were pre-registered. No trials deposited data in open repositories.

CONCLUSIONS

These results will aid the sports medicine and orthopaedics community in developing tailored interventions to improve reporting. While authors typically mention blinding, randomisation and other factors, essential details are often missing. Greater acceptance of open science practices, like pre-registration and open data, is needed. As these practices have been widely encouraged, we discuss systemic interventions that may improve clinical trial reporting.