Missing Data: Understanding the pattern of missingness is crucial. Linear interpolation can be an effective imputation method for continuous data like weight, especially if the data points are time series.
Selection Bias: Using the entire dataset is ideal if the dataset is well-collected and representative. In cases where representativeness is a concern, proportional sampling helps ensure each subgroup is adequately represented, reducing the risk of bias.
Data Analysis and Training: Class imbalance is a significant challenge. Techniques like SMOTE (Synthetic Minority Over-sampling Technique) or adjusting class weights in model training can help. Ensuring balanced representation in your training data is key to building robust models.
Interpretation and Translational Applicability of Results: Collaborating with frontline stakeholders who understand the local context is vital for meaningful interpretation. For translational applicability, especially in app development, employing user-centered design principles ensures the end product meets its users’ actual needs and preferences.
Privacy and Ethical Issues: Hashing identifiers like names and phone numbers is a great way to protect individual privacy. Ensure that the hashing method is robust and irreversible. Always align with data privacy laws and ethical guidelines in handling sensitive information.