纵向缺失数据变量重要性与预测方法

数据集获取（Figshare）

• 源站标题：Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables
• DOI：10.1371/journal.pone.0120031
• 发布日期：2015-03-27
• 文件数：2 个
• 文件总体积：180.51 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_Variable_Importance_and_Prediction_Methods_for_Longitudinal_Problems_with_Missing_Variables_/1359118

命令行下载

curl -L -o "S1_Table.pdf" "https://ndownloader.figshare.com/files/1992854"
curl -L -o "S1_TMLE_Algorithm.pdf" "https://ndownloader.figshare.com/files/1992855"

文件清单

• S1_Table.pdf（50.39 KB）
• S1_TMLE_Algorithm.pdf（130.12 KB）

文件总体积 180.51 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

We present prediction and variable importance (VIM) methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient’s physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed). Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner), which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO). In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC) for a receiver-operator curve (ROC). Thus, given that 1) our VIM applies to any model fitting procedure, 2) under assumptions has meaningful clinical (causal) interpretations and 3) has asymptotic (influence-curve) based robust inference, it provides a compelling alternative to ex

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0120031（source: https://plos.figshare.com/articles/dataset/_Variable_Importance_and_Prediction_Methods_for_Longitudinal_Problems_with_Missing_Variables_/1359118）。