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History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimens (2005)

van der Laan, Mark J. ; Petersen, Maya L ; Joffe, Marshall M

Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)

The @international journal of biostatistics 1 (2005), S. 4

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ISSN: 1557-4679

Source: Berkeley Electronic Press Academic Journals

Topics: Biology , Mathematics , Medicine

Notes: Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a treatment. These models, introduced by Robins, model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject's treatment and covariate history are measured over time, and an outcome is recorded at a final time point. However, the utility of these models for some applications has been limited by their inability to incorporate modification of the causal effect of treatment by time-varying covariates. Particularly in the context of clinical decision making, such time-varying effect modifiers are often of considerable or even primary interest, as they are used in practice to guide treatment decisions for an individual. In this article we propose a generalization of marginal structural models, which we call history-adjusted marginal structural models (HA-MSM). These models allow estimation of adjusted causal effects of treatment, given the observed past, and are therefore more suitable for making treatment decisions at the individual level and for identification of time-dependent effect modifiers. Specifically, a HA-MSM models the conditional distribution of treatment-specific counterfactual outcomes, conditional on the whole or a subset of the observed past up till a time-point, simultaneously for all time-points. Double robust inverse probability of treatment weighted estimators have been developed and studied in detail for standard MSM. We extend these results by proposing a class of double robust inverse probability of treatment weighted estimators for the unknown parameters of the HA-MSM. In addition, we show that HA-MSM provide a natural approach to identifying the dynamic treatment regimen which follows, at each time-point, the history-adjusted (up till the most recent time point) optimal static treatment regimen. We illustrate our results using an example drawn from the treatment of HIV infection.

Type of Medium: Electronic Resource

URL: http://www.bepress.com/ijb/vol1/iss1/4

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2

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Super Learning: An Application to the Prediction of HIV-1 Drug Resistance (2007)

Sinisi, Sandra E. ; Polley, Eric C ; Petersen, Maya L ; [et al.]

Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)

Statistical applications in genetics and molecular biology 6.2007, 1, art7

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ISSN: 1544-6115

Source: Berkeley Electronic Press Academic Journals

Topics: Biology

Notes: 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. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners and uses cross-validation to select between them. Theory shows that asymptotically the super learner performs essentially as well as or better than any of the candidate learners. In this article we present the theory behind the super learner, and illustrate its performance using simulations. We further apply the super learner to a data example, in which we predict the phenotypic antiretroviral susceptibility of HIV based on viral genotype. Specifically, we apply the super learner to predict susceptibility to a specific protease inhibitor, nelfinavir, using a set of database-derived non-polymorphic treatment-selected mutations.

Type of Medium: Electronic Resource

URL: http://www.bepress.com/sagmb/vol6/iss1/art7

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Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules (2007)

van der Laan, Mark J. ; Petersen, Maya L

Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)

The @international journal of biostatistics 3 (2007), S. 3

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ISSN: 1557-4679

Source: Berkeley Electronic Press Academic Journals

Topics: Biology , Mathematics , Medicine

Notes: Marginal structural models (MSM) are an important class of models in causal inference. Given a longitudinal data structure observed on a sample of n independent and identically distributed experimental units, MSM model the counterfactual outcome distribution corresponding with a static treatment intervention, conditional on user-supplied baseline covariates. Identification of a static treatment regimen-specific outcome distribution based on observational data requires, beyond the standard sequential randomization assumption, the assumption that each experimental unit has positive probability of following the static treatment regimen. The latter assumption is called the experimental treatment assignment (ETA) assumption, and is parameter-specific. In many studies the ETA is violated because some of the static treatment interventions to be compared cannot be followed by all experimental units, due either to baseline characteristics or to the occurrence of certain events over time. For example, the development of adverse effects or contraindications can force a subject to stop an assigned treatment regimen.In this article we propose causal effect models for a user-supplied set of realistic individualized treatment rules. Realistic individualized treatment rules are defined as treatment rules which always map into the set of possible treatment options. Thus, causal effect models for realistic treatment rules do not rely on the ETA assumption and are fully identifiable from the data. Further, these models can be chosen to generalize marginal structural models for static treatment interventions. The estimating function methodology of Robins and Rotnitzky (1992) (analogue to its application in Murphy, et. al. (2001) for a single treatment rule) provides us with the corresponding locally efficient double robust inverse probability of treatment weighted estimator. In addition, we define causal effect models for "intention-to-treat" regimens. The proposed intention-to-treat interventions enforce a static intervention until the time point at which the next treatment does not belong to the set of possible treatment options, at which point the intervention is stopped. We provide locally efficient estimators of such intention-to-treat causal effects.

Type of Medium: Electronic Resource

URL: http://www.bepress.com/ijb/vol3/iss1/3

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4

Electronic Resource

Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules (2007)

van der Laan, Mark J. ; Petersen, Maya L

Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)

The @international journal of biostatistics 3 (2007), S. 6

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ISSN: 1557-4679

Source: Berkeley Electronic Press Academic Journals

Topics: Biology , Mathematics , Medicine

Notes: Consider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. A statically optimal individualized treatment rule (as introduced in van der Laan et. al. (2005), Petersen et. al. (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. In particular, Petersen et. al. (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. Petersen et. al. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. The current article provides an important advance on Petersen et. al. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. However, failure to capture all confounding comes at a price; the static optimality of the resulting rules becomes origin-specific. We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. We further present the results of a data analysis in which we estimate a statically optimal rule for switching antiretroviral therapy among patients infected with resistant HIV virus.

Type of Medium: Electronic Resource

URL: http://www.bepress.com/ijb/vol3/iss1/6

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Partitioning of Polybrominated Diphenyl Ethers to Dissolved Organic Matter Isolated from Arctic Surface Waters (2014)

Maya L. Wei-Haas, Kimberly J. Hageman and Yu-Ping Chin

American Chemical Society (ACS)

In: Environmental Science & Technology

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Publication Date: 2014-04-17

Description: Environmental Science & Technology DOI: 10.1021/es405453m

Print ISSN: 0013-936X

Electronic ISSN: 1520-5851

Topics: Chemistry and Pharmacology , Energy, Environment Protection, Nuclear Power Engineering

Published by American Chemical Society (ACS)

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Larval exposure to predator cues alters immune function and response to a fungal pathogen in post-metamorphic wood frogs (2013)

onlinepublishing@allenpress.com (Maya L. Groner et al)

Wiley

In: Ecological Applications

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Publication Date: 2013-09-07

Description: Ecological Applications, Volume 23, Issue 6, Page 1443-1454, September 2013. For the past several decades, amphibian populations have been decreasing around the globe at an unprecedented rate. Batrachochytrium dendrobatidis (Bd), the fungal pathogen that causes chytridiomycosis in amphibians, is contributing to amphibian declines. Natural and anthropogenic environmental factors are hypothesized to contribute to these declines by reducing the immunocompetence of amphibian hosts, making them more susceptible to infection. Antimicrobial peptides (AMPs) produced in the granular glands of a frog's skin are thought to be a key defense against Bd infection. These peptides may be a critical immune defense during metamorphosis because many acquired immune functions are suppressed during this time. To test if stressors alter AMP production and survival of frogs exposed to Bd, we exposed wood frog (Lithobates sylvaticus) tadpoles to the presence or absence of dragonfly predator cues crossed with a single exposure to three nominal concentrations of the insecticide malathion (0, 10, or 100 parts per billion [ppb]). We then exposed a subset of post-metamorphic frogs to the presence or absence of Bd zoospores and measured frog survival. Although predator cues and malathion had no effect on survival or size at metamorphosis, predator cues increased the time to metamorphosis by 1.5 days and caused a trend of a 20% decrease in hydrophobic skin peptides. Despite this decrease in peptides determined shortly after metamorphosis, previous exposure to predator cues increased survival in both Bd-exposed and unexposed frogs several weeks after metamorphosis. These results suggest that exposing tadpoles to predator cues confers fitness benefits later in life.

Print ISSN: 1051-0761

Electronic ISSN: 1939-5582

Topics: Biology