Missing data is an inevitable challenge in clinical trials, threatening the validity and reliability of results. Whether due to patient dropouts, incomplete records, or unforeseen circumstances, these gaps in information can introduce bias and uncertainty[5]. Fortunately, machine learning (ML) offers a range of sophisticated techniques to address this issue, allowing researchers to salvage valuable insights from incomplete datasets[2].
The Challenge of Missing Data
Missing data can be categorized into three main types[5]:
- Missing Completely at Random (MCAR): The missingness is unrelated to any variable in the study.
- Missing at Random (MAR): The missingness is related to some observed variables, but not to the outcome of interest.
- Missing Not at Random (MNAR): The missingness is related to the outcome or some unobserved variables.
The approach to handling missing data depends on the type of missingness and the potential impact on the analysis. Traditional methods, such as simply removing incomplete records (list-wise deletion), can lead to biased results and a loss of statistical power[2]. ML provides more nuanced and effective solutions.
Machine Learning Approaches to Handling Missing Data
ML algorithms offer several strategies for dealing with missing data[2]:
- Imputation: This involves replacing missing values with estimated values based on the available data[2]. Several ML-based imputation methods exist[2]:
- K-Nearest Neighbors (KNN): Imputes missing values based on the values of the nearest neighbors in the dataset[2].
- Sequential KNN (SKNN): An improvement on KNN, SKNN is more time-efficient and yields more accurate results[2].
- Multiple Imputation (MI): Creates multiple complete datasets by filling in values for the missing data, then analyzes each dataset and combines the results [4, 6]. MI imputes missing entries based on statistical characteristics of the data, such as associations among variables[6]. There are two types of MI techniques: improper and proper imputation[4].
- Multivariate Imputation by Chained Equations (MICE): An imputation method that is considered one of the popular methods in the literature[3].
- MissForest: Another popular imputation method[3].
- Generative Adversarial Imputation Networks (GAIN): A method that is used in handling missing data.[3].
- Missing Data Importance-Weighted Autoencoder (MIWAE): A method that is used in handling missing data[3].
- Model-Based Methods: Some ML algorithms can handle missing data directly without requiring imputation[1]. For example, decision tree algorithms can use missing values as a separate category during training[1].
- Handling Missing Values as a Category: A unique “unknown” category (e.g., NA) can be imputed for all missing variables to introduce a sparsity pattern[1]. This seeks to use the default method for handling missing values of XGBoost or RF[1].
Evaluating Imputation Quality
It is crucial to assess the quality of imputation to ensure that the imputed values do not introduce bias or distort the results[3]. Methods for assessing imputation quality include[3]:
- Comparing the distribution of imputed values with the distribution of true values.
- Assessing the stability of the imputations.
- Evaluating the interpretability of models built on the imputed data.
Best Practices for Handling Missing Data
- Understand the mechanism of missingness[5].
- Choose an appropriate imputation method based on the data and the type of missingness[2].
- Evaluate the quality of imputation[3].
- Consider using model-based methods that can handle missing data directly[1].
- Document the methods used to handle missing data and their potential impact on the results.
By employing these strategies, researchers can minimize the impact of missing data and maximize the validity of their findings. Machine learning empowers us to navigate the void of incomplete information, bringing greater clarity and confidence to clinical trial outcomes.
Citations:
[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC9117456/
[2] https://www.tandfonline.com/doi/full/10.1080/01969722.2023.2247257
[3] https://www.nature.com/articles/s43856-023-00356-z
[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC3948388/
[5] https://www.linkedin.com/advice/0/how-do-you-handle-missing-data-clinical-trials-skills-statistics
[6] https://pmc.ncbi.nlm.nih.gov/articles/PMC4638176/
[7] https://www.researchgate.net/publication/360589754_Systematic_Review_on_Missing_Data_Imputation_Techniques_with_Machine_Learning_Algorithms_for_Healthcare
[8] https://www.mastersindatascience.org/learning/how-to-deal-with-missing-data/
[9] https://arxiv.org/html/2404.04905v1
