Royle Kara-Louise, Cairns David A
Clinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK.
Diagn Progn Res. 2021 Aug 4;5(1):14. doi: 10.1186/s41512-021-00103-9.
The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology.
Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin's rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin's rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin's rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin's rules.
The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716-0.964) in the training dataset and 0.654 (95% CI 0.497-0.811) in the test dataset and the corrected D-Statistic was 0.801.
The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model.
Both trials were registered; Myeloma IX- ISRCTN68454111 , registered 21 September 2000. Myeloma XI- ISRCTN49407852 , registered 24 June 2009.
英国骨髓瘤研究联盟（UK-MRA）骨髓瘤风险概况是一种总体生存预后模型。它在临床试验数据上进行了训练和测试，旨在改善新诊断的多发性骨髓瘤移植不适合（TNE）患者的分层。缺失数据是一个常见问题，会影响预后模型的开发和验证，如何处理缺失数据的决策会对方法的选择产生影响。
模型构建 训练和测试数据集来自两项大型随机多中心III期临床试验的TNE路径。通过专家意见确定潜在的预后因素。训练数据集中的缺失数据使用链式方程多重填补法进行填补。单变量分析在每个填补数据集中拟合Cox比例风险模型，并根据鲁宾法则合并估计值。多变量分析应用惩罚Cox回归模型，在各个填补数据集中使用固定的惩罚项。通过鲁宾法则合并每个填补数据集的估计值和自抽样标准误，以定义预后模型。模型评估 通过可视化各个填补数据集中观察到的和预测的概率来评估校准。通过鲁宾法则合并每个填补数据集的预后分离D统计量来评估区分度。模型验证 D统计量应用于训练数据集的自抽样内部验证过程和测试数据集的外部验证过程，其中预先指定了可接受的性能。风险组的制定 使用组合预后指数的三分位数定义风险组，该指数通过鲁宾法则合并每个填补数据集的预后指数获得。
训练数据集包括1852例患者，其中1268例（68.47%）有完整病例数据。生成了10个填补数据集。测试数据集包括520例患者。训练数据集中预后模型的D统计量为0.840（95%CI 0.716 - 0.964），测试数据集中为0.654（95%CI 0.497 - 0.811），校正后的D统计量为0.801。
在训练数据集中填补缺失协变量数据的决策影响了用于训练和测试模型的方法。为了扩展现有文献并帮助未来的研究人员，我们提供了一种方法的详细示例。虽然我们的示例并非没有局限性，但一个好处是训练数据集中所有可用的患者信息都被用于开发模型。
两项试验均已注册；骨髓瘤IX - ISRCTN68454111，于2000年9月21日注册。骨髓瘤XI - ISRCTN49407852，于2009年6月24日注册。
