The followings are my suggestions to cope with those challenges.
Missing Data
Missing data can be minimized by strengthening data entry processes through regular training, robust standard operating procedures (SOPs), and built-in data validation mechanisms at the point of entry. When missing values occur, they can be addressed by validating and supplementing information from alternative data sources, such as laboratory machines, laboratory registers, and linked records across multiple systems. In addition, unstructured data can be transformed into usable formats through the application of natural language processing (NLP) tools, enabling the recovery or inference of otherwise missing information.
Selection Bias
Selection bias can be mitigated through the use of advanced analytical techniques, including propensity score analysis, instrumental variable analysis, and Mendelian randomization. Large-scale datasets should primarily be used for hypothesis generation rather than direct clinical decision-making. Findings derived from big data analyses should be carefully validated through randomized controlled trials (RCTs) or triangulated with evidence from multiple independent studies before being translated into clinical practice.
Data Analysis and Training
Effective data analysis requires the establishment of a multidisciplinary team comprising clinicians, researchers, health informaticians, data scientists, statisticians, and other relevant experts. Such teams can collaboratively develop comprehensive training materials and innovative training approaches tailored to diverse skill levels. The creation of standardized templates and analytical tools can further reduce entry barriers, shorten learning curves, and promote consistent and high-quality data analysis practices.
Interpretation and Translational Applicability of Results
Early and continuous involvement of key stakeholders—particularly clinicians—throughout the research process, from study design to interpretation of results, is essential to ensure clinical relevance. Studies should be designed with a backward approach, starting from the intended clinical application to ensure the results are actionable. Emphasis should be placed on clinically meaningful outcomes rather than solely on statistical significance, and analytical models should be communicated in clinician-friendly terms, such as risk, benefit, and potential harm.
Privacy and Ethical Issues
Privacy and ethical considerations should be addressed through a privacy-by-design approach, including data de-identification and strict access control mechanisms. Broad consent models can be adopted, provided they are accompanied by clear and transparent communication with participants. Researchers should explicitly articulate both the potential risks and the anticipated societal benefits associated with data use to maintain trust and ethical integrity.