Women and Kids Theme, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia.
Stat Med. 2024 May 20;43(11):2083-2095. doi: 10.1002/sim.10060. Epub 2024 Mar 15.
To obtain valid inference following stratified randomisation, treatment effects should be estimated with adjustment for stratification variables. Stratification sometimes requires categorisation of a continuous prognostic variable (eg, age), which raises the question: should adjustment be based on randomisation categories or underlying continuous values? In practice, adjustment for randomisation categories is more common. We reviewed trials published in general medical journals and found none of the 32 trials that stratified randomisation based on a continuous variable adjusted for continuous values in the primary analysis. Using data simulation, this article evaluates the performance of different adjustment strategies for continuous and binary outcomes where the covariate-outcome relationship (via the link function) was either linear or non-linear. Given the utility of covariate adjustment for addressing missing data, we also considered settings with complete or missing outcome data. Analysis methods included linear or logistic regression with no adjustment for the stratification variable, adjustment for randomisation categories, or adjustment for continuous values assuming a linear covariate-outcome relationship or allowing for non-linearity using fractional polynomials or restricted cubic splines. Unadjusted analysis performed poorly throughout. Adjustment approaches that misspecified the underlying covariate-outcome relationship were less powerful and, alarmingly, biased in settings where the stratification variable predicted missing outcome data. Adjustment for randomisation categories tends to involve the highest degree of misspecification, and so should be avoided in practice. To guard against misspecification, we recommend use of flexible approaches such as fractional polynomials and restricted cubic splines when adjusting for continuous stratification variables in randomised trials.
为了在分层随机化后获得有效的推断，应该根据分层变量调整治疗效果。分层有时需要对连续预后变量（例如年龄）进行分类，这就提出了一个问题：调整应该基于随机化分类还是潜在的连续值？在实践中，基于随机化分类进行调整更为常见。我们回顾了发表在普通医学期刊上的试验，发现没有一篇基于连续变量进行分层随机化的 32 项试验在主要分析中调整了连续值。本文使用数据模拟，评估了不同调整策略在连续和二分类结局下的表现，其中协变量-结局关系（通过链接函数）为线性或非线性。鉴于协变量调整在处理缺失数据方面的实用性，我们还考虑了完全或缺失结局数据的情况。分析方法包括线性或逻辑回归，未对分层变量进行调整、调整随机化分类或假设线性协变量-结局关系进行连续值调整，或使用分数多项式或限制立方样条允许非线性。未经调整的分析在整个过程中表现不佳。未指定潜在协变量-结局关系的调整方法的效能较低，而且在分层变量预测缺失结局数据的情况下，结果令人震惊地存在偏差。调整随机化分类往往涉及最大程度的误判，因此在实践中应避免。为了防止误判，我们建议在随机试验中调整连续分层变量时使用灵活的方法，如分数多项式和限制立方样条。
