Objectives: The life event of retirement may be associated with changes in levels of depressive symptoms. The use of polynomial group-based trajectory modelling allows any changes to vary between different groups in a sample. A new approach, estimating these models using B-splines rather than polynomials, may improve modelling of complex changes in depressive symptoms at retirement.Methods: The sample contained 1497 participants from the Swedish Longitudinal Occupational Survey of Health (SLOSH). Polynomial and B-spline approaches to estimating group-based trajectory models were compared.Results: Polynomial group-based trajectory models produced unexpected changes in direction of trajectories unsupported by the data. In contrast, B-splines provided improved insights into trajectory shapes and more homogeneous groups. While retirement was associated with reductions in depressive symptoms in the sample as a whole, the nature of changes at retirement varied between groups.Conclusions: Depressive symptoms trajectories around old age retirement changed in complex ways that were modelled more accurately by the use of B-splines. We recommend estimation of group-based trajectory models with B-splines, particularly where abrupt changes might occur. Improved trajectory modelling may support research into risk factors and consequences of major depressive disorder, ultimately assisting with identification of groups which may benefit from treatment.

中文翻译：

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基于B样条群的轨迹模型与基于多项式群的轨迹模型的比较，用于识别老年退休后抑郁症状的轨迹。

目的：退休的生活事件可能与抑郁症状水平的改变有关。基于多项式组的轨迹模型的使用允许样本中不同组之间的任何变化都可以变化。一种使用B样条而不是多项式来估计这些模型的新方法可能会改善退休时抑郁症状的复杂变化建模。方法：该样本包含1497名来自瑞典纵向职业卫生调查（SLOSH）的参与者。结果：基于多项式组的轨迹模型产生了轨迹方向的意外变化，这些变化不受数据的支持。相反，B样条提供了对轨迹形状和更均质组的更好的洞察力。虽然退休与减少整个样本中的抑郁症状有关，但退休后变化的性质在各组之间各不相同。结论：老年退休前后的抑郁症状轨迹以复杂的方式改变，通过使用B-花键。我们建议使用B样条估计基于组的轨迹模型，尤其是在可能发生突然变化的地方。改进的轨迹模型可以支持对重度抑郁症的危险因素和后果的研究，最终帮助确定可能受益于治疗的人群。老年退休后的抑郁症状轨迹以复杂的方式发生了变化，通过使用B样条可以更准确地建模。我们建议使用B样条估计基于组的轨迹模型，尤其是在可能发生突然变化的地方。改进的轨迹模型可以支持对重度抑郁症的危险因素和后果的研究，最终帮助确定可能受益于治疗的人群。老年退休后的抑郁症状轨迹以复杂的方式发生了变化，通过使用B样条可以更准确地建模。我们建议使用B样条估计基于组的轨迹模型，尤其是在可能发生突然变化的地方。改进的轨迹模型可以支持对重度抑郁症的危险因素和后果的研究，最终有助于识别可能受益于治疗的人群。