1. Missing data
The reasons are due to omitted by clinicians, refused by patients, not attending for data collection. Statistical methods have limitations for analyzing missing data and make it difficult to produce plausible results.
Suggestions: data collection protocol should be standardized for the system, not be subjective by clinicians or others. Essential variables should be collected to avoid big volume without meanings. Data Management Trainings should be conducted regularly to reduce entry error to database. Advanced statistical methods can be used to get meaningful data analysis results.
2. Selection bias
The reasons are due to variations in subjects’ geographic, medical history profiles and insurance etc. Exploited statistical analysis can have confounding implications. Large volume is not advantage for representative sample.
Suggestions: sample selection should be following statistical methods/calculations such as weighting, randomization in extracting from big data. Blinding and masking of researcher and clinician can also help to reduce this bias. Appropriate statistical analysis should be chosen for comparing data.
3. Data analysis and training
Big data analysis requires multiple analysis to establish hypothesis and significant correlations. So, the researcher’s skill to use statistical and methodological tools is important.
Suggestions: the clinicians/researchers should collaborate with informaticians, statistician and data scientists in big data analysis. They also need to attend required basic methodological trainings.
4. Interpretation and translational applicability of results
Important to integrate the analysis output to daily clinical practice.
Suggestions: Improve transparency and documentation of datasets, including standardize disease definitions to ensure interpretability and reproducibility between clinicians and researchers. Advocacy and sharing practices with research findings should be carried out as event for raising application of results.
5. Privacy and ethical issues
Data protection policy and regulations must be set up for all big dada storage in server. Data encryption, data authorization and access should be strictly maintained. Data security protocols, safeguarding practice and trainings should be updated regularly.