Multiple linear regression model.

Figshare2017-12-23 更新2026-04-29 收录

疾病预后建模

有氧能力预测

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Variables in early disease associated with aerobic capacity (ml/kg/min) at follow-up.

创建时间：

2017-12-23

Logistic regression model predicting severe or very severe/lethal COVID-19 syndrome (grouped together, Model C) or very severe/lethal disease only (Model D), in the total sample (n = 1603).

COVID-19严重程度

疾病预后建模

Logistic regression model predicting severe or very severe/lethal COVID-19 syndrome (grouped together, Model C) or very severe/lethal disease only (Model D), in the total sample (n = 1603).

NIAID Data Ecosystem50

Model based on four independent variables.

疾病预后建模

临床风险预测

Objective To identify survival-related risk factors in patients with advanced schistosomiasis, develop a predictive model using integrative approach of LASSO-Cox regression, and construct a nomogram f

NIAID Data Ecosystem40

Multivariable Cox proportional hazard model of the association between T2D status and mortality, overall and by diabetes medication.

疾病预后建模

治疗效果评估

Multivariable Cox proportional hazard model of the association between T2D status and mortality, overall and by diabetes medication.

NIAID Data Ecosystem30

Should we predict maximum oxygen uptake using fat-free mass or body mass in a non-athletic population

身体成分分析

有氧能力预测

Background : Our purpose was to assess whether fat-free mass (FFM) or body mass (M) is the more appropriate body-size variable to predict maximum oxygen uptake (VO 2max ) (L⋅min −</

DataCite Commons2025-10-14 更新40

Univariate and multivariate analysis for predicting relapse-free survival.

生存分析

疾病预后建模

Univariate and multivariate analysis for predicting relapse-free survival.

Figshare2022-10-04 更新30