Network Meta-Analysis of survival outcomes with non-proportional hazards using flexible M-splines
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利用灵活 M 样条处理非比例风险的生存结局网络荟萃分析
- 研究问题
- 当不同治疗的风险函数随时间复杂变化、比例风险假设不成立时，如何灵活综合生存结局证据，同时控制过拟合与模型选择负担？
- 方法与思路
- 作者以 M 样条描述基准风险，设置加权随机游走收缩先验，并通过治疗分层或样条系数上的治疗效应刻画非比例风险。
- 材料陈述的贡献
- 摘要称，所提先验对结点与时间尺度选择具有不变性，方法支持汇总、个体及混合数据，并在 multinma 中实现和用于肺癌治疗比较。
- 阅读关注点
- 应核对先验不变性的具体含义、治疗比较与不同数据来源整合的假设，以及结点设置、后验计算和长期生存外推的敏感性。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未给出完整证据整合条件、比较结果或计算性能，灵活性不保证外推可靠。
原文摘要
Network meta-analysis (NMA) is widely used in healthcare decision-making, where estimates of the effect of multiple treatments on outcomes are required. For time-to-event outcomes such as survival or disease progression the most common approach is to model log hazard ratios; however, this relies on the proportional hazards assumption. Novel treatments such as immunotherapies are expected to display complex hazard functions that cannot be captured by standard parametric models, which results in non-proportional hazards when comparing treatments from different classes. As a result, alternative models such as fractional polynomials or restricted cubic splines are often used. These allow substantial flexibility on the shape of the baseline hazard, but require time-consuming model selection or are intractable for Bayesian analysis. We propose a flexible NMA model using M-splines on the baseline hazard, with a novel weighted random walk prior distribution that provides shrinkage to avoid overfitting and is invariant to the choice of knots and timescale. Non-proportional hazards are modelled either by stratifying by treatment or by introducing treatment effects on the spline coefficients, and covariates may be included on the log hazard rate and spline coefficients. Treatment and covariate effects on the spline coefficients are given random walk prior distributions to smoothly model departures from proportionality over time. The methods are implemented in the user-friendly R package multinma, which supports analyses with aggregate data, individual participant data, or mixtures of both. We apply the methods to a NMA of progression-free survival with treatments for non-small cell lung cancer.