IPF-LASSO: Integrative<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn fontstyle="italic">1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

Boulesteix, Anne‐Laure; Bin, Riccardo De; Jiang, Xiaoyu; Fuchs, Mathias

doi:10.1155/2017/7691937

Cited by 82 publications

(90 citation statements)

References 29 publications

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“…IPF‐Lasso The IPF‐Lasso, applied to the present context, would minimize

L false(λ, ϕ_{1}, ϕ_{2}, bold-italicβ false) = false‖ boldy - bold-italicX bold-italicβ {false‖}_{2}^{2} + λ false(ϕ_{1} false‖ {bold-italicβ}_{bold1} {false‖}_{1} + ϕ_{2} false‖ {bold-italicβ}_{bold2} {false‖}_{1} false) .

…”

Section: Methodsmentioning

confidence: 99%

“…For comparison, we also present the penalties for the IPF-Lasso, sparse group lasso (SGL), and group lasso (GLASSO). 27 The IPF-Lasso, applied to the present context, would minimize…”

Section: Methodsmentioning

confidence: 99%

“…Some formal approaches for incorporating varying credibility between predictors have already been proposed . Of these, the most relevant to our work is the work of Boulesteix et al where they developed the Integrative Lasso with Penalty Factors (IPF‐Lasso).…”

Section: Introductionmentioning

confidence: 99%

“…Some formal approaches for incorporating varying credibility between predictors have already been proposed. 26,27 Of these, the most relevant to our work is the work of Boulesteix et al where they developed the Integrative Lasso with Penalty Factors (IPF-Lasso). The IPF-Lasso was created for the "omics" data setting, in which investigators have several categories of predictors with varying levels of credibility (ie, clinical predictors, genetic data, metabalomics, proteomics, etc).…”

mentioning

confidence: 99%

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Accounting for established predictors with the multistep elastic net

Chase

Boonstra

2019

Statistics in Medicine

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Multivariable models for prediction or estimating associations with an outcome are rarely built in isolation. Instead, they are based upon a mixture of covariates that have been evaluated in earlier studies (eg, age, sex, or common biomarkers) and covariates that were collected specifically for the current study (eg, a panel of novel biomarkers or other hypothesized risk factors). For that context, we present the multistep elastic net (MSN), which considers penalized regression with variables that can be qualitatively grouped based upon their degree of prior research support: established predictors vs unestablished predictors. The MSN chooses between uniform penalization of all predictors (the standard elastic net) and weaker penalization of the established predictors in a cross‐validated framework and includes the option to impose zero penalty on the established predictors. In simulation studies that reflect the motivating context, we show the comparability or superiority of the MSN over the standard elastic net, the Integrative LASSO with Penalty Factors, the sparse group lasso, and the group lasso, and we investigate the importance of not penalizing the established predictors at all. We demonstrate the MSN to update a prediction model for pediatric ECMO patient mortality.

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“…IPF‐Lasso The IPF‐Lasso, applied to the present context, would minimize

L false(λ, ϕ_{1}, ϕ_{2}, bold-italicβ false) = false‖ boldy - bold-italicX bold-italicβ {false‖}_{2}^{2} + λ false(ϕ_{1} false‖ {bold-italicβ}_{bold1} {false‖}_{1} + ϕ_{2} false‖ {bold-italicβ}_{bold2} {false‖}_{1} false) .

…”

Section: Methodsmentioning

confidence: 99%

“…For comparison, we also present the penalties for the IPF-Lasso, sparse group lasso (SGL), and group lasso (GLASSO). 27 The IPF-Lasso, applied to the present context, would minimize…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Accounting for established predictors with the multistep elastic net

Chase

Boonstra

2019

Statistics in Medicine

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“…Sometimes, it may be worthwhile to combine EB with CV. For example, if one wishes to apply different penalties

λ_{g}

for groups of variables (Boulesteix, De Bin, Jiang, & Fuchs, ; van de Wiel et al, ), one may reparameterize

λ_{g} = λ λ_{sans-serifg}^{'}

and optimize the global parameter λ by CV with respect to predictive performance while estimating the multipliers

λ_{sans-serifg}^{'}

by EB. Alternatively, CV or similar out‐of‐bag approaches may be used to tune the initial EB estimates to improve predictive performance or to implement parameter thresholding.…”

Section: Eb and Cross‐validation For Multiple Hyperparametersmentioning

confidence: 99%

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Wiel

Beest

Münch

2018

Scandinavian J Statistics

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Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p , the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.

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Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection

et al. 2019

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Uncertainty is a crucial issue in statistics which can be considered from different points of view. One type of uncertainty, typically referred to as sampling uncertainty, arises through the variability of results obtained when the same analysis strategy is applied to different samples. Another type of uncertainty arises through the variability of results obtained when using the same sample but different analysis strategies addressing the same research question. We denote this latter type of uncertainty as method uncertainty. It results from all the choices to be made for an analysis, for example, decisions related to data preparation, method choice, or model selection. In medical sciences, a large part of omics research is focused on the identification of molecular biomarkers, which can either be performed through ranking or by selection from among a large number of candidates. In this paper, we introduce a general resampling-based framework to quantify and compare sampling and method uncertainty. For illustration, we apply this framework to different scenarios related to the selection and ranking of omics biomarkers in the context of acute myeloid leukemia: variable selection in multivariable regression using different types of omics markers, the ranking of biomarkers according to their predictive performance, and the identification of differentially expressed genes from RNA-seq data. For all three scenarios, our findings suggest highly unstable results when the same analysis strategy is applied to two independent samples, indicating high sampling uncertainty and a comparatively smaller, but non-negligible method uncertainty, which strongly depends on the methods being compared. K E Y W O R D Shigh-dimensional data, resampling, stability, variable ranking, variable selection INTRODUCTIONStatistical results are variable. On the one hand, results are affected by sampling uncertainty: if we draw different samples from a considered distribution, we obtain different results on each of these samples. Most researchers are familiar with this kind of variability and classical probability models account for it.On the other hand, one also obtains different results when applying different analysis strategies to address the same research question. These possible analysis strategies result from the combination of every choice that has to be made for an analysis, 670

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IPF-LASSO: IntegrativeL1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

Cited by 82 publications

References 29 publications

Accounting for established predictors with the multistep elastic net

Accounting for established predictors with the multistep elastic net

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection

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