Implementing embedded methods involves several steps:
1. Study Design Careful planning and design of the study are essential. Researchers must define the population, choose appropriate controls, and determine the timing of data collection.
2. Data Collection Accurate and comprehensive data collection is critical. This includes gathering information on exposures, outcomes, and potential confounders.
3. Data Analysis Advanced statistical techniques are often required to analyze the data. Software like R or SAS can be used to perform complex analyses such as multivariable regression or propensity score matching.
4. Interpretation The findings must be interpreted in the context of the study design and the limitations of the data. Sensitivity analyses are often conducted to assess the robustness of the results.
