Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility

Meid, Andreas D.; Gerharz, Alexander; Groll, Andreas

doi:10.1002/psp4.12761

Cited by 4 publications

(10 citation statements)

References 9 publications

(18 reference statements)

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confidence: 99%

“…1 After eventually obtaining access to the clinical trial data through a data-sharing platform (www.vivli. org), Meid et al 2 developed an alternative model to predict OS based on the conditional average treatment effect of each patient. To increase the interpretability of the contribution of the covariates in predicting the outcome, Meid et al also investigated the following two additional methods: variable importance and partial dependence plots.…”

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confidence: 99%

“…To increase the interpretability of the contribution of the covariates in predicting the outcome, Meid et al also investigated the following two additional methods: variable importance and partial dependence plots. 2 During our various prior communications with the primary author of Meid et al 2 to facilitate data access and analysis data set construction, we regret the hurdles that the authors experienced in submitting a research proposal to Vivli (as a simple publication reproduction was not a valid objective in the data request) and constructing an analysis data set from Study Data Tabulation Model (STDM) databases. Dataset building for pharmacometric analyses is not a straightforward process, 3 and the attempt by Meid et al 2 was also complicated by the lack of readily available TGI metrics in the STDM database, as the individual TGI metrics were model-derived parameters based on modeling of longitudinal tumor size data.…”

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confidence: 99%

“…In the commentary by Meid et al, 2 the authors originally set out to reproduce our previously published work that explored four ML methods to support the validity of TGI metrics in predicting OS using data from a phase III clinical trial. 1 After eventually obtaining access to the clinical trial data through a data‐sharing platform ( www.vivli.org ), Meid et al 2 developed an alternative model to predict OS based on the conditional average treatment effect of each patient. To increase the interpretability of the contribution of the covariates in predicting the outcome, Meid et al also investigated the following two additional methods: variable importance and partial dependence plots.…”

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confidence: 99%

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confidence: 99%

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Update to improve reproducibility and interpretability: A response to “Machine Learning for Tumor Growth Inhibition”

Chan

Bruno

et al. 2022

CPT Pharmacom & Syst Pharma

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Update to improve reproducibility and interpretability: A response to “Machine Learning for Tumor Growth Inhibition”

Chan

Bruno

et al. 2022

CPT Pharmacom & Syst Pharma

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Machine learning‐guided covariate selection for time‐to‐event models developed from a small sample of real‐world patients receiving bevacizumab treatment

Karatza

Papachristos

Sivolapenko

et al. 2022

CPT Pharmacom & Syst Pharma

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Therapeutic outcomes in patients with metastatic colorectal cancer (mCRC) receiving bevacizumab treatment are highly variable, and a reliable predictive factor is not available. Progression‐free survival (PFS) and overall survival (OS) were recorded from an observational, prospective study after 5 years of follow‐up, including 46 patients with mCRC receiving bevacizumab treatment. Three vascular endothelial growth factor (VEGF)‐A and two intercellular adhesion molecule‐1 genes polymorphisms, age, gender, weight, dosing scheme, and co‐treatments were collected. Given the relatively small number of events (37 [80%] for the PFS and 26 [57%] for the OS), to study the effect of these covariates on PFS and OS, a covariate analysis was performed using statistical and supervised machine learning techniques, including Cox regression, penalized Cox regression techniques (least absolute shrinkage and selection operator [LASSO], ridge regression, and elastic net), survival trees, and survival forest. The predictive performance of each method was evaluated in bootstrapped samples, using prediction error curves and the area under the curve of the receiver operating characteristic. The LASSO penalized Cox‐regression model showed the best overall performance. Nonlinear mixed effects (NLME) models were developed, and a conventional stepwise covariate search was performed. Then, covariates identified as important by the LASSO model were included in the base NLME models developed for PFS and OS, resulting in improved models as compared to those obtained with the stepwise covariate search. It was shown that having gene polymorphisms in VEGFA (rs699947 and rs1570360) and ICAM1 (rs1799969) are associated with a favorable clinical outcome in patients with mCRC receiving bevacizumab treatment.

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Covariance regression with random forests

2023

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Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine. We propose a new method called Covariance Regression with Random Forests (CovRegRF) to estimate the covariance matrix of a multivariate response given a set of covariates, using a random forest framework. Random forest trees are built with a splitting rule specially designed to maximize the difference between the sample covariance matrix estimates of the child nodes. We also propose a significance test for the partial effect of a subset of covariates. We evaluate the performance of the proposed method and significance test through a simulation study which shows that the proposed method provides accurate covariance matrix estimates and that the Type-1 error is well controlled. An application of the proposed method to thyroid disease data is also presented. is implemented in a freely available R package on CRAN.

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Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility

Cited by 4 publications

References 9 publications

Update to improve reproducibility and interpretability: A response to “Machine Learning for Tumor Growth Inhibition”

Update to improve reproducibility and interpretability: A response to “Machine Learning for Tumor Growth Inhibition”

Machine learning‐guided covariate selection for time‐to‐event models developed from a small sample of real‐world patients receiving bevacizumab treatment

Covariance regression with random forests

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