Cross-Validated Bagged Learning.

Many applications aim to learn a high dimensional parameter of a data generating distribution based on a sample of independent and identically distributed observations. For example, the goal might be to estimate the conditional mean of an outcome given a list of input variables. In this prediction context, bootstrap aggregating (bagging) has been introduced as a method to reduce the variance of a given estimator at little cost to bias.

Biomarker discovery using targeted maximum-likelihood estimation: application to the treatment of antiretroviral-resistant HIV infection.

Researchers in clinical science and bioinformatics frequently aim to learn which of a set of candidate biomarkers is important in determining a given outcome, and to rank the contributions of the candidates accordingly. This article introduces a new approach to research questions of this type, based on targeted maximum-likelihood estimation of variable importance measures.The methodology is illustrated using an example drawn from the treatment of HIV infection.

Super learning: an application to the prediction of HIV-1 drug resistance.

Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data-generating distribution.

Cross-validated bagged prediction of survival.

In this article, we show how to apply our previously proposed Deletion/Substitution/Addition algorithm in the context of right-censoring for the prediction of survival. Furthermore, we introduce how to incorporate bagging into the algorithm to obtain a cross-validated bagged estimator. The method is used for predicting the survival time of patients with diffuse large B-cell lymphoma based on gene expression variables.

Multiple testing and data adaptive regression: an application to HIV-1 sequence data.

Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological insights regarding the replication ability of HIV-1. Determining specific target codons on the viral strand will facilitate the manufacturing of target-specific antiretrovirals. Various algorithmic and analysis techniques can be applied to this application.

Deletion/substitution/addition algorithm in learning with applications in genomics.

van der Laan and Dudoit (2003) provide a road map for estimation and performance assessment where a parameter of interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using a loss function. After briefly reviewing this approach, this article proposes a general deletion/substitution/addition algorithm for minimizing, over subsets of variables (e.g.

Estimation of direct causal effects.

Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure on an outcome while blocking the exposure's effect on an intermediate variable. Effects of this kind are termed direct effects. Estimation of direct effects is typically the goal of research aimed at understanding mechanistic pathways by which an exposure acts to cause or prevent disease, as well as in many other settings.

Diagnoses based on the Research Diagnostic Criteria for Temporomandibular Disorders in a biracial population of young women.

Aims: To compare the clinical characteristics of diagnostic subtypes of temporomandibular disorders (TMD) based on the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) in terms of physical findings (Axis I) and psychosocial findings (Axis II) among Caucasian and African American young women. An ancillary goal was to assess the value of using self-reported TMD pain as a screening tool compared to RDC/TMD examinations.

Methods: A biracial community sample (n = 830) of young women 19 to 23 years old was screened for facial pain with the Chronic Pain Grade questionnaire.