School of Population Health and Clinical Practice, Discipline of Public Health, University of Adelaide, Australia.
Acta Orthop. 2011 Oct;82(5):513-20. doi: 10.3109/17453674.2011.618918. Epub 2011 Sep 6.
Here we describe some available statistical models and illustrate their use for analysis of arthroplasty registry data in the presence of the competing risk of death, when the influence of covariates on the revision rate may be different to the influence on the probability (that is, risk) of the occurrence of revision.
Records of 12,525 patients aged 75-84 years who had received hemiarthroplasty for fractured neck of femur were obtained from the Australian Orthopaedic Association National Joint Replacement Registry. The covariates whose effects we investigated were: age, sex, type of prosthesis, and type of fixation (cementless or cemented). Extensions of competing risk regression models were implemented, allowing the effects of some covariates to vary with time.
The revision rate was significantly higher for patients with unipolar than bipolar prostheses (HR = 1.38, 95% CI: 1.01-1.89) or with monoblock than bipolar prostheses (HR = 1.45, 95% CI: 1.08-1.94). It was significantly higher for the younger age group (75-79 years) than for the older one (80-84 years) (HR = 1.28, 95% CI: 1.05-1.56) and higher for males than for females (HR = 1.37, 95% CI: 1.09-1.71). The probability of revision, after correction for the competing risk of death, was only significantly higher for unipolar prostheses than for bipolar prostheses, and higher for the younger age group. The effect of fixation type varied with time; initially, there was a higher probability of revision for cementless prostheses than for cemented prostheses, which disappeared after approximately 1.5 years.
When accounting for the competing risk of death, the covariates type of prosthesis and sex influenced the rate of revision differently to the probability of revision. We advocate the use of appropriate analysis tools in the presence of competing risks and when covariates have time-dependent effects.
本文介绍了一些可用的统计模型，并举例说明了如何在存在死亡竞争风险的情况下，对关节置换登记数据进行分析，此时协变量对翻修率的影响可能与对翻修概率（即风险）的影响不同。
从澳大利亚骨科协会全国关节置换登记处获得了 12525 名 75-84 岁接受人工股骨头置换术的患者记录。我们研究了以下协变量的影响：年龄、性别、假体类型和固定类型（骨水泥型或非骨水泥型）。实施了竞争风险回归模型的扩展，允许一些协变量的影响随时间变化。
与双极假体相比，单极假体（HR=1.38，95%CI：1.01-1.89）或单块假体（HR=1.45，95%CI：1.08-1.94）的患者翻修率显著更高。与年龄较大的组（80-84 岁）相比，年龄较小的组（75-79 岁）翻修率更高（HR=1.28，95%CI：1.05-1.56），男性翻修率高于女性（HR=1.37，95%CI：1.09-1.71）。在纠正死亡竞争风险后，只有单极假体的翻修概率显著高于双极假体，且年龄较小的组更高。固定类型的影响随时间而变化；最初，非骨水泥假体的翻修概率高于骨水泥假体，大约 1.5 年后这种差异消失。
当考虑死亡竞争风险时，假体类型和性别这两个协变量对翻修率的影响与翻修概率不同。我们主张在存在竞争风险和协变量具有时间依赖性影响的情况下，使用适当的分析工具。
