Comment pouvez-vous imputer les données manquantes dans l’analyse de régression?

Excluding missing values in regression analysis can bias results and distort conclusions. Data absence is often not random and may be linked to key variables, leading to lack of representativeness and precision in the model. Complete case analysis excludes relevant information, resulting in an incomplete and potentially biased view of the studied phenomenon. Furthermore, it's crucial to avoid single-value imputation, as it introduces biases. Methods like multiple imputation, such as MICE, capture uncertainty associated with missing data, generating multiple estimates and providing a more comprehensive and accurate view of the analysis.

By deleting the rows that have missing data, we could loose some valuable information in other variables too. Also this solution is effective wher we don't have any other way to determine the missing data or the number of missing values are a few.

In regression analysis, I'd treat missing data with care. I'd start by assessing its pattern and potential impact. Depending on the situation, I might impute missing values using methods like mean, median, or regression-based imputation. Transparency and documentation are crucial, ensuring my approach aligns with the data's nature and the analysis's integrity.

Detrmining the method to retreive/guess/find the missing values depends a lot on the data and the feature with missing values. The meaning of the feature in the dataset and what it represents could help a lot in deciding the method to fill in the missing values. Also the type of data itself is very imortant. If the varibale is categorical we need to use Mode instead of mean to replace the missing values.

Regression imputation, while considering variable relationships, exhibits a strong limitation in handling variance. By imputing a single value, this method fails to reflect inherent data variability, potentially distorting analysis precision. Despite being an improvement over the poor practice of mean imputation, its limitations make it an unreliable method. Similar to mean imputation, it overlooks the entire data distribution, introducing significant biases. Acknowledging these shortcomings is crucial, as they may compromise the validity of conclusions drawn from regression-imputed data analysis.

Although this could be a good method but not in all of the cases: 1. if our variables are binary or categorical this method can't be used. 2. If we have highly imbalanced existing data this method might not be so effective. 3. Other variables shouldn't have many missing values too.

Multiple imputation emerges as the preferred method due to its ability to address the limitations of other techniques. By generating multiple estimates for missing values, it better reflects the inherent uncertainty in the data, preserving variance and reducing biases. This approach involves creating multiple complete datasets, where missing values are estimated using statistical models. Then, the analysis results from each complete dataset are combined using specific methods, such as Rubin's rules or pooling methods, ensuring accurate parameter estimation and proper assessment of associated uncertainty. Its flexible and robust nature makes it the ideal choice to ensure the integrity and validity of data analysis with missing values.

One of the most prominent libraries in R for conducting multiple imputation using the MICE (Multiple Imputation by Chained Equations) method is the `mice` library. This library provides a wide range of functions for performing multiple imputation effectively and efficiently. Additionally, `mice` offers flexibility in specifying models for data imputation, allowing users to tailor the process to the specific needs of the analysis. Other useful libraries for multiple imputation in R include `Amelia`, `mi`, and `missForest`, each with its own features and advantages. Depending on the analysis requirements and user preferences, these libraries offer various options for confidently and accurately performing multiple imputation in R.