A sensitivity analysis examines how conclusions change when assumptions, tuning choices, unidentified parameters, or plausible data perturbations are varied.
Specify distributions of measurements separately by observation pattern and vary assumptions about unseen values that the observed data do not identify. For example, shift the imputed binary-response log odds for nonresponders away from its missing at random prediction and refit the substantive analysis. This investigates missing not at random departures rather than testing them from observed data.
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Sensitivity analysis is a quantitative method used to determine how the different values of an independent variable (or input) will impact a particular dependent variable (or output) under a given set of assumptions. It assesses how sensitive the output of a model is to changes in input values, allowing researchers and decision-makers to understand the robustness and reliability of their results or predictions.