Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them

被引：38

作者：

Wolkewitz, Martin ^{[1
]}

Lambert, Jerome ^{[1
]}

von Cube, Maja ^{[1
]}

Bugiera, Lars ^{[1
]}

Grodd, Marlon ^{[1
]}

Hazard, Derek ^{[1
]}

White, Nicole ^{[2
]}

Barnett, Adrian ^{[2
]}

Kaier, Klaus ^{[1
]}

机构：

[1] Univ Freiburg, Med Ctr, Fac Med, Inst Med Biometry & Stat, Freiburg, Germany

[2] Queensland Univ Technol, Sch Publ Hlth & Social Work, Brisbane, Qld, Australia

来源：

CLINICAL EPIDEMIOLOGY | 2020年 / 12卷

关键词：

competing risk bias; immortal-time bias; competing events; time-dependent bias; time-varying exposure; time-to-event analysis; COMPETING RISKS; TIME; INFECTIONS; HAZARDS;

D O I：

10.2147/CLEP.S256735

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing inhospital COVID-19 data.

引用

页码：925 / 928

页数：4

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