P17 Missing Data in Clinical Trials: Responses Missing Not at Random (MNAR) in a Regression Setup

Randomization provides a fair comparison between treatment and control groups, balancing out, distributions of known and unknown factors among the participants. With missing data, we tend to lose this advantage and end up with biased results. Approaches to analyze data with a large number of missing values tend to be ad hoc and variable. While dealing with missing data, missing at random and missing completely at random are more popular assumptions, and easier to handle compared to missing not at random.
We propose some approaches for response values missing not at random scenarios. If a response value is missing not at random, then the probability that the response value is missing depends on the response value itself, and unbiased estimate of the response value is not possible with the help of observed data only. For example, Quality of Life response values may be missing because patients may become too sick to participate and cannot be estimated without bias.
In this work, we show usage of additional samples to estimate the response values more efficiently. This study shows how external data borrowing techniques can be used to serve this purpose, and how many extra samples will be needed to match the performance of an 'oracle' estimator (a hypothetical estimator with no missing data).

Decision Analysis (e.g., Go/No-Go, Benefit:Risk Determination, Patient-Preference)

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2024