Degrees-of-Freedom Approximations for Conditional-Mean Inference in Random-Lot Stability Analysis

Linear mixed models are widely used for pharmaceutical stability trending when sufficient lots are available. Expiry support is often based on whether lot-specific conditional-mean confidence limits remain within specification through a proposed expiry, and these limits depend on the denominator degrees-of-freedom (DDF) method used for t-based inference. We document an operationally important boundary-proximal phenomenon: when a fitted random-effect variance component is close to zero, Satterthwaite DDF-and, in sensitivity analyses, Kenward-Roger DDF-for conditional-mean predictions can collapse, inflating $t$ critical values and producing very wide and sometimes nonmonotone pointwise confidence limits on scheduled time grids. In the settings studied here, containment DDF yields stable degrees of freedom and avoids sharp discontinuities as variance components approach the boundary. In a balanced random-intercept design, a closed-form calculation gives the conditional prediction variance and delta-method Satterthwaite DDF, showing why DDF can be extremely small near the boundary when the prediction point is near the design centroid. A worked example and simulation studies show that DDF choice can materially change pass/fail conclusions even when the observed measurements themselves are not close to specification limits. Containment-based inference with the full random-effects model provides a single framework without model switching. When containment is unavailable, a 10% variance-contribution reduction procedure mitigates extreme Satterthwaite behavior by simplifying only when fitted contributions at the proposed expiry are negligible. AICc step-down is best treated as a sensitivity analysis because it can be too aggressive when the proposed-expiry margin is small.