Linear mixed models are widely used for pharmaceutical stability trending when sufficient lots are available. Expiry support is typically based on whether lot-specific conditional-mean confidence limits remain within specification through a proposed expiry. 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 for conditional-mean predictions can collapse, inflating $t$ critical values and producing unnecessarily wide and sometimes nonmonotone pointwise confidence limits on scheduled time grids. In contrast, containment DDF yields stable degrees of freedom and avoids sharp discontinuities as variance components approach the boundary. Using a worked example and simulation studies, we show that DDF choice can materially change pass/fail conclusions even when observed data comfortably meet specifications. Containment-based inference with the full random-effects model provides a single modeling framework that avoids the discontinuities introduced by data-dependent model reduction at arbitrary cutoffs. When containment is unavailable, a 10\% variance-contribution reduction workflow mitigates extreme Satterthwaite behavior by simplifying the random-effects structure only when fitted contributions at the proposed expiry are negligible. An AICc step-down is also evaluated but is best treated as a sensitivity analysis, as it can be liberal when the margin between the mean trend and the specification limit at the proposed expiry is small.

翻译：当可获得足够批次时，线性混合模型被广泛应用于药品稳定性趋势分析。有效期支持通常基于批次特异性条件均值置信限是否在拟议有效期内持续符合规范。这些限值取决于用于基于$t$分布的推断所采用的分母自由度方法。我们记录了一个具有重要操作意义的边界邻近现象：当拟合的随机效应方差分量接近零时，用于条件均值预测的Satterthwaite自由度可能发生坍缩，从而放大$t$临界值，并在预定时间网格上产生不必要的宽泛且有时非单调的点态置信限。相比之下，包含法自由度能产生稳定的自由度，并在方差分量接近边界时避免剧烈的不连续性。通过实例分析和模拟研究，我们证明即使观测数据完全符合规范，自由度选择仍可能实质性改变合格/不合格的判定结论。基于包含法的完整随机效应模型推断提供了一个统一的建模框架，避免了在任意截断点进行数据依赖性模型简化所引入的不连续性。当包含法不可用时，采用10%方差贡献缩减工作流程可缓解极端Satterthwaite行为——该流程仅在拟议有效期处的拟合贡献可忽略时简化随机效应结构。本文同时评估了AICc逐步降阶法，但建议将其视为敏感性分析工具，因为在拟议有效期处均值趋势与规范限值间差异较小时，该方法可能产生过于宽松的判定。