7 Best practices for transparent reporting on biomedical research
In the exciting world of biomedical research, transparency is key to ensuring the integrity and credibility of scientific advancements. It’s not surprising that new guidelines continue to be developed to encourage transparent reporting across biomedical fields, for example, RANCARE in nursing and TRIPOD for prediction models that are used for individual prognosis or diagnosis.
To help you navigate the realm of transparent reporting, we’ve put together a handy listicle of best practices. From study design to statistical reporting, we've got you covered. So, let’s dive in and discover the top seven practices that will empower you to communicate your biomedical research with utmost clarity and openness.
1. Study Design and Methodology:
Clearly describe the study design, including the research question, hypothesis, and objectives. Provide detailed information about the methods used, such as sampling strategy, data collection techniques, instruments or equipment used, and any experimental procedures. The key is this: Provide enough detail so that others can replicate your work.
2. Sample:
Justify the sample size used in the study based on power calculations or other appropriate statistical considerations. Report any deviations from the planned sample size. Transparent reporting of sample size helps evaluate the validity and generalizability of the study findings. Provide all key details about the sample, including gender, age, race/ethnicity, health status, important comorbidities, etc. Describe your inclusion and exclusion criteria in full.
3. Data Cleaning:
Transparently report the process of data cleaning, which involves identifying and correcting errors, outliers, and inconsistencies in the dataset. Clearly define the criteria used for data exclusion and explain any imputations or transformations performed. This helps ensure data integrity and reproducibility.
4. Missing Data:
Describe the approach taken to handle missing data, whether it be through complete case analysis, imputation methods, or sensitivity analyses. Justify the chosen method and report any assumptions made. Transparently reporting missing data helps readers understand the potential impact on the results and interpretation.
5. Statistical Analysis:
Clearly state the statistical tests used and justify their selection. Report how you’ve tested for key assumptions, such as normality or independence, and any adjustments made for multiple comparisons. Provide enough details for readers to independently verify the statistical analyses.
6. Results:
Report all results obtained, including both positive and negative findings. Include relevant statistical measures, effect sizes, confidence intervals, and p-values. Avoid selective reporting, which can lead to biased interpretations of the data.
7. Compliance with Reporting Guidelines:
Adhere to established reporting guidelines specific to your research domain, such as CONSORT (for clinical trials) or STROBE (for observational studies). These guidelines provide standardized recommendations for reporting key elements of research and may include some/all of the previous items on this list.
Bonus Tip: Open Data and Code Sharing:
Whenever possible, share your raw data, analysis code, and supplementary materials openly. This practice promotes transparency and allows other researchers to validate and build upon your work. Consider using data repositories and version control systems for efficient and accessible sharing.
By following these best practices, you can enhance the transparency and reproducibility of your study, contributing to the overall quality of biomedical research and trustworthiness of scientific knowledge in the field.
Ensure you've reported statistical data fully and clearly in your research manuscript, in line with best practices in your field. Consult an expert biostatistician under Editage’s Statistical Analysis & Review Services.
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