J Geriatr Oncol. 2013 Apr;4(2):157-65. doi: 10.1016/j.jgo.2012.12.005.
To develop and provide initial validation for amultivariate, claims-based prediction model for disability status (DS), a proxymeasure of performance status (PS), among older adults. The model was designed to augment information on health status at the point of cancer diagnosis in studies using insurance claims to examine cancer treatment and outcomes.
We used data from the 2001–2005 Medicare Current Beneficiary Survey (MCBS), with observations randomly split into estimation and validation subsamples. We developed an algorithm linking self-reported functional status measures to a DS scale, a proxy for the Eastern Cooperative Oncology Group (ECOG) PS scale. The DS measure was dichotomized to focus on good [ECOG 0–2] versus poor [ECOG 3–4] PS. We identified potential claims-based predictors, and estimated multivariate logistic regression models, with poor DS as the dependent measure, using a stepwise approach to select the optimal model. Construct validity was tested by determining whether the predicted DS measure generated by the model was a significant predictor of survival within a validation sample from the MCBS.
One-tenth of the beneficiaries met the definition for poor DS. The base model yielded high sensitivity (0.79) and specificity (0.92); positive predictive value=48.3% and negative predictive value=97.8%, c-statistic=0.92 and good model calibration. Adjusted poor claims-based DS was associated with an increased hazard of death (HR=3.53, 95% CI 3.18, 3.92). The ability to assess DS should improve covariate control and reduce indication bias in observational studies of cancer treatment and outcomes based on insurance claims.
开发并提供一种基于多元索赔数据的、针对老年人残疾状态（DS）的预测模型，并初步验证其有效性，DS 是一种绩效状态（PS）的替代指标。该模型旨在增加癌症诊断时健康状况的信息，以便在使用保险索赔数据研究癌症治疗和结果的研究中使用。
我们使用了来自 2001-2005 年医疗保险当前受益人调查（MCBS）的数据，观察结果随机分为估计和验证子样本。我们开发了一种算法，将自我报告的功能状态测量值与 DS 量表（ECOG PS 量表的替代指标）联系起来。DS 量表被二分为关注良好[ECOG 0-2]与不良[ECOG 3-4]PS。我们确定了潜在的基于索赔的预测因素，并使用逐步方法选择最优模型，对不良 DS 作为因变量的多变量逻辑回归模型进行了估计。使用 MCBS 中的验证样本，通过确定模型预测的 DS 测量值是否是生存的显著预测因素来测试结构有效性。
十分之一的受益符合不良 DS 的定义。基础模型具有较高的灵敏度（0.79）和特异性（0.92）；阳性预测值=48.3%，阴性预测值=97.8%，c 统计量=0.92，模型校准良好。调整后的不良索赔 DS 与死亡风险增加相关（HR=3.53，95%CI 3.18，3.92）。在基于保险索赔的癌症治疗和结果的观察性研究中，评估 DS 的能力应该改善协变量控制并减少指示偏差。
