9.4.22 Intention to Treat Analysis

Knows What is Meant by Intention to Treat Analysis and Understand Different Ways of Handling Missing Data: Last Observation Carried Forward Sensitivity Analysis, Multiple Imputation, Best Case Analysis and Worst Case Analysis

Intention to treat (ITT) analysis is a method used in clinical trials to evaluate the efficacy of a treatment. It involves analyzing the data from all participants according to their assigned treatment group, regardless of whether they actually received the treatment or not. This approach is preferred because it preserves the randomization process, which is essential for minimizing selection bias and obtaining unbiased estimates of treatment effects.

However, missing data can pose a challenge in ITT analysis. Incomplete follow-up, dropouts, and missing data can occur for various reasons such as adverse events, withdrawal of consent, or loss to follow-up. Ignoring missing data or excluding participants with missing data can lead to biased results and loss of statistical power. Therefore, various methods have been proposed to handle missing data, including:

Last observation carried forward (LOCF):

This method imputes missing data with the last observed value. It assumes that the participant’s response remains constant over time, which may not always be valid.

Multiple imputation (MI):

MI creates several plausible imputed datasets based on statistical models that account for the uncertainty associated with missing data. The results from each imputed dataset are combined using standard rules to obtain a single estimate.

Best and worst case analysis:

Best case analysis assumes that all participants with missing data have the best possible outcome, while worst-case analysis assumes that they have the worst possible outcome. These methods can provide insight into the robustness of the findings of missing data.

Method	Description	Advantages	Disadvantages
Last Observation Carried Forward (LOCF)	Use the last available data point for the missing data	Simple and easy to use	Assumes that the missing data is similar to the last observed data
Sensitivity Analysis	Reanalyze the data using different assumptions about the missing data	Provides a range of possible results	Can be subjective and difficult to interpret
Multiple Imputation	Impute the missing data using statistical models and combine the results	Reduces bias and provides more accurate results	Requires assumptions about the data and can be computationally intensive
Best Case Analysis	Assume that all missing data represents the best possible outcome	Can provide an optimistic estimate of the treatment effect	Ignores the potential negative outcomes of missing data
Worst Case Analysis	Assume that all missing data represents the worst possible outcome	Can provide a conservative estimate of the treatment effect	Ignores the potential positive outcomes of missing data

It is important to conduct sensitivity analyses to assess the impact of missing data on the results. Sensitivity analysis involves testing the robustness of the findings to different assumptions about the missing data mechanism.

For example, suppose a randomized controlled trial is conducted to evaluate the efficacy of a new medication for hypertension. The ITT analysis shows a significant reduction in blood pressure in the treatment group compared to the control group. However, a large proportion of participants dropped out of the study, and their data are missing. To handle missing data, the study authors conduct a sensitivity analysis using multiple imputations and find that the results are consistent with the ITT analysis.

References:

1. Little, R. J. A., & Rubin, D. B. (2019). Statistical Analysis with Missing Data (3rd ed.). John Wiley & Sons.
2. White IR, Horton NJ, Carpenter J, Pocock SJ. Strategy for intention to treat analysis in randomised trials with missing outcome data. BMJ. 2011;342:d40. doi: 10.1136/bmj.d40.