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Predictors of refraction prediction error after cataract surgery: a shared parameter model to account for missing post-operative measurements
Statistical Methods & Applications ( IF 1 ) Pub Date : 2021-06-22 , DOI: 10.1007/s10260-021-00570-w
D. Claire Miller , Samantha MaWhinney , Jennifer L. Patnaik , Karen L. Christopher , Anne M. Lynch , Brandie D. Wagner

Cataract surgery is a common procedure that involves removing the cataractous lens of the eye and implanting a new clear plastic lens, with a goal of improving vision and often changing the refraction of the eye. Unexpected refractive outcomes, or refraction prediction error (PE), may be more likely to occur among patients with certain preexisting ocular conditions. However, longitudinal refractive measurements are often missing after surgery, making it difficult to accurately assess which ocular comorbidities lead to increased PE. Moreover, patients with ideal refractive outcomes in one or both eyes may be less likely to return to the clinic, thus more likely to have missing measurements that are missing not at random (MNAR). Despite this potential complication to data analysis, very few studies evaluating PE address the missing data mechanism and the effect it may have on the results. We propose the application of a shared parameter model to reduce bias in situations of MNAR data and compare this to a linear mixed model that is not able to account for this mechanism. We also conduct a simulation study to better understand the most plausible missing mechanism in our study and to characterize situations in which the shared parameter model may be necessary. Applied to the cataract surgery data, the shared parameter model gives similar results as the linear mixed model, finding that a history of LASIK, PRK, RK, high myopia, hyperopia, astigmatism, or combined surgery lead to increased PE. In addition, the simulations confirm that under certain scenarios, a shared parameter model will greatly reduce bias in situations of MNAR.



中文翻译:

白内障手术后屈光预测误差的预测因素:一个共享参数模型,以解决术后测量丢失的问题

白内障手术是一种常见的手术,包括去除眼睛的白内障晶状体并植入新的透明塑料晶状体,目的是改善视力并经常改变眼睛的屈光度。意外的屈光结果或屈光预测误差 (PE) 可能更可能发生在具有某些预先存在的眼部疾病的患者中。然而,手术后往往缺少纵向屈光测量,因此很难准确评估哪些眼部合并症导致 PE 增加。此外,单眼或双眼屈光结果理想的患者可能不太可能返回诊所,因此更有可能丢失非随机丢失的测量值 (MNAR)。尽管数据分析存在这种潜在的复杂性,评估 PE 的研究很少涉及缺失数据机制及其可能对结果的影响。我们建议应用共享参数模型来减少 MNAR 数据情况下的偏差,并将其与无法解释这种机制的线性混合模型进行比较。我们还进行了模拟研究,以更好地理解我们研究中最合理的缺失机制,并描述可能需要共享参数模型的情况。应用于白内障手术数据,共享参数模型给出与线性混合模型相似的结果,发现 LASIK、PRK、RK、高度近视、远视、散光或联合手术的历史导致 PE 增加。此外,模拟证实,在某些情况下,

更新日期:2021-06-23
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