Topic 8. 临床预测模型-Lasso回归

Topic 8: Lasso Regression: A Powerhouse in Clinical Prognostic Models

Lasso regression, a game-changer in biomarker selection, is a versatile tool for constructing generalized linear models with glmnet, catering to various linear and logistic regression scenarios. Its prowess lies in handling both continuous and categorical variables, with the lambda parameter delicately controlling model complexity. In the realm of bioinformatics, Lasso regression has revolutionized survival prediction, significantly enhancing diagnostic accuracy.

Let's dive into its essence:

1. Conceptual Framework

Introducing Lasso as a sparse regression technique for fitting linear and non-linear models, it excels in managing data with a single continuous response or multiple variables of differing nature – from linear to polynomial, Poisson, and Cox. Its adaptability extends to non-negative integer counts, binary and multi-level categorical responses.

Now, let's examine a practical application:

1. Real-World Example: Prostate Cancer Prognosis

The glmnet package, in the R environment, has been deployed to analyze prostate cancer data, predicting post-operative PSA levels. This case study showcases the power of regularization in enhancing clinical decision-making. The dataset features a wealth of variables, including tumor volume, prostate weight, age, benign prostatic hyperplasia (BPH) severity, prostate invasion, capsular penetration, Gleason scores, and PSA values.

Data preprocessing involves dividing the dataset into training (70%) and testing (30%) sets. A step-by-step process unfolds:

1. Feature extraction and response: x <- as.matrix(train[, 1:8]) y <- train[, 9]


2. Running cross-validated model: lasso <- glmnet(x, y, family = "gaussian", alpha = 1)


3. Model results: print(lasso)

The model reveals the Lambda values and coefficient dynamics, revealing the significance of parameters like Intercept, lcavol, lweight, lbph, svi, and gleason.

To leverage the model, we predict with a specific lambda value: lasso.coef = predict(lasso, s = 0.045, type = "coefficients")

Notably, six parameters emerge as key contributors: Intercept and the rest mentioned above.

And then, the crucial step of cross-validation: lasso.cv = cv.glmnet(x, y, nlambda = 200, alpha = 1). This technique ensures model robustness, helping identify the optimal lambda value (lasso.cv$lambda.min).

With these insights, Lasso regression offers a powerful solution for clinicians to tackle complex prognostic challenges. For further exploration, consult the following references:

• Friedman, Hastie, & Tibshirani (2008, 2010, 2011)


• Tibshirani, Saunders, et al. (2012)


• Hastie, Tibshirani, & Friedman (2017)


• Huang, et al. (2020)


• Chen, et al. (2019)

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