The Experts below are selected from a list of 353856 Experts worldwide ranked by ideXlab platform
Irina Mahlstein - One of the best experts on this subject based on the ideXlab platform.
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Emerging local warming signals in Observational Data
2012Co-Authors: Irina Mahlstein, Gabriele C. Hegerl, Susan SolomonAbstract:[1] The global average temperature of the Earth has increased, but year-to-year variability in local climates impedes the identification of clear changes in observations and human experience. For a signal to become obvious in Data records or in a human lifetime it needs to be greater than the noise of variability and thereby lead to a significant shift in the distribution of temperature. We show that locations with the largest amount of warming may not display a clear shift in temperature distributions if the local variability is also large. Based on Observational Data only we demonstrate that large parts of the Earth have experienced a significant local shift towards warmer temperatures in the summer season, particularly at lower latitudes. We also show that these regions are similar to those that are found to be significant in standard detection methods, thus providing an approach to link locally significant changes more closely to impacts. Citation: Mahlstein, I., G. Hegerl, and S. Solomon (2012), Emerging local warming signals in Observational Data, Geophys. Res. Lett., 39, L21711, doi:10.1029/2012GL053952.
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emerging local warming signals in Observational Data
Geophysical Research Letters, 2012Co-Authors: Irina Mahlstein, Gabriele C. Hegerl, Susan SolomonAbstract:[1] The global average temperature of the Earth has increased, but year-to-year variability in local climates impedes the identification of clear changes in observations and human experience. For a signal to become obvious in Data records or in a human lifetime it needs to be greater than the noise of variability and thereby lead to a significant shift in the distribution of temperature. We show that locations with the largest amount of warming may not display a clear shift in temperature distributions if the local variability is also large. Based on Observational Data only we demonstrate that large parts of the Earth have experienced a significant local shift towards warmer temperatures in the summer season, particularly at lower latitudes. We also show that these regions are similar to those that are found to be significant in standard detection methods, thus providing an approach to link locally significant changes more closely to impacts.
Susan Solomon - One of the best experts on this subject based on the ideXlab platform.
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Emerging local warming signals in Observational Data
2012Co-Authors: Irina Mahlstein, Gabriele C. Hegerl, Susan SolomonAbstract:[1] The global average temperature of the Earth has increased, but year-to-year variability in local climates impedes the identification of clear changes in observations and human experience. For a signal to become obvious in Data records or in a human lifetime it needs to be greater than the noise of variability and thereby lead to a significant shift in the distribution of temperature. We show that locations with the largest amount of warming may not display a clear shift in temperature distributions if the local variability is also large. Based on Observational Data only we demonstrate that large parts of the Earth have experienced a significant local shift towards warmer temperatures in the summer season, particularly at lower latitudes. We also show that these regions are similar to those that are found to be significant in standard detection methods, thus providing an approach to link locally significant changes more closely to impacts. Citation: Mahlstein, I., G. Hegerl, and S. Solomon (2012), Emerging local warming signals in Observational Data, Geophys. Res. Lett., 39, L21711, doi:10.1029/2012GL053952.
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emerging local warming signals in Observational Data
Geophysical Research Letters, 2012Co-Authors: Irina Mahlstein, Gabriele C. Hegerl, Susan SolomonAbstract:[1] The global average temperature of the Earth has increased, but year-to-year variability in local climates impedes the identification of clear changes in observations and human experience. For a signal to become obvious in Data records or in a human lifetime it needs to be greater than the noise of variability and thereby lead to a significant shift in the distribution of temperature. We show that locations with the largest amount of warming may not display a clear shift in temperature distributions if the local variability is also large. Based on Observational Data only we demonstrate that large parts of the Earth have experienced a significant local shift towards warmer temperatures in the summer season, particularly at lower latitudes. We also show that these regions are similar to those that are found to be significant in standard detection methods, thus providing an approach to link locally significant changes more closely to impacts.
Miguel A Hernan - One of the best experts on this subject based on the ideXlab platform.
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how to estimate the effect of treatment duration on survival outcomes using Observational Data
BMJ, 2018Co-Authors: Miguel A HernanAbstract:When using Observational Data, quantifying the effect of treatment duration on survival outcomes is not straightforward because only people who live for a long time can receive treatment for a long time. This problem doesn’t apply to randomised trials because people are classified based on the treatment duration they are assigned, rather than the treatment duration that they achieve. This approach accepts that dead people do not deviate from their assigned treatment strategy. By transferring this insight to the analysis of Observational Data, we can follow three steps to estimate the effect of treatment duration from Observational Data without the bias of naive comparisons between long term and short term users. The first step is cloning people to assign them to multiple treatment strategies. The second step is censoring clones when they deviate from their assigned treatment strategy. The third step is performing inverse probability weighting to adjust for the potential selection bias introduced by censoring. The procedure can be used to compare any treatment strategies that are sustained over time. Cloning, censoring, and weighting eliminates immortal time bias in the estimates of absolute and relative risk, which helps researchers focus their attention on other biases that may be present in Observational analyses and are not so easily eliminated.
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comparative effectiveness research using Observational Data active comparators to emulate target trials with inactive comparators
eGEMs (Generating Evidence & Methods to improve patient outcomes), 2016Co-Authors: Anders Huitfeldt, Miguel A Hernan, Mette Kalager, James M RobinsAbstract:Introduction: Because a comparison of non-initiators and initiators of treatment may be hopelessly confounded, guidelines for the conduct of Observational research often recommend using an “active” comparator group consisting of people who initiate a treatment other than the medication of interest. In this paper, we discuss the conditions under which this approach is valid if the goal is to emulate a trial with an inactive comparator. Identification of Effects: We provide conditions under which a target trial in a subpopulation can be validly emulated from Observational Data, using an active comparator that is known or believed to be inactive for the outcome of interest. The average treatment effect in the population as a whole is not identified, but under certain conditions this approach can be used to emulate a trial either in the subset of individuals who were treated with the treatment of interest, in the subset of individuals who were treated with the treatment of interest but not with the comparator, or in the subset of individuals who were treated with both the treatment of interest and the active comparator. The Plausibility of the Comparability Conditions: We discuss whether the required conditions can be expected to hold in pharmacoepidemiologic research, with a particular focus on whether the conditions are plausible in situations where the standard analysis fails due to unmeasured confounding by access to health care or health seeking behaviors. Discussion: The conditions discussed in this paper may at best be approximately true. Investigators using active comparator designs to emulate trials with inactive comparators should exercise caution.
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Observational Data for comparative effectiveness research an emulation of randomised trials of statins and primary prevention of coronary heart disease
Statistical Methods in Medical Research, 2013Co-Authors: Goodarz Danaei, Miguel A Hernan, Roger Logan, Luis Garcia A Rodriguez, Oscar Fernandez CanteroAbstract:This article reviews methods for comparative effectiveness research using Observational Data. The basic idea is using an Observational study to emulate a hypothetical randomised trial by comparing initiators versus non-initiators of treatment. After adjustment for measured baseline confounders, one can then conduct the Observational analogue of an intention-to-treat analysis. We also explain two approaches to conduct the analogues of per-protocol and as-treated analyses after further adjusting for measured time-varying confounding and selection bias using inverse-probability weighting. As an example, we implemented these methods to estimate the effect of statins for primary prevention of coronary heart disease (CHD) using Data from electronic medical records in the UK. Despite strong confounding by indication, our approach detected a potential benefit of statin therapy. The analogue of the intention-to-treat hazard ratio (HR) of CHD was 0.89 (0.73, 1.09) for statin initiators versus non-initiators. The HR of CHD was 0.84 (0.54, 1.30) in the per-protocol analysis and 0.79 (0.41, 1.41) in the as-treated analysis for 2 years of use versus no use. In contrast, a conventional comparison of current users versus never users of statin therapy resulted in a HR of 1.31 (1.04, 1.66). We provide a flexible and annotated SAS program to implement the proposed analyses.
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when to start treatment a systematic approach to the comparison of dynamic regimes using Observational Data
The International Journal of Biostatistics, 2010Co-Authors: Lauren E Cain, James M Robins, Emilie Lanoy, Roger Logan, Dominique Costagliola, Miguel A HernanAbstract:Dynamic treatment regimes are the type of regime most commonly used in clinical practice. For example, physicians may initiate combined antiretroviral therapy the first time an individual's recorded CD4 cell count drops below either 500 cells/mm3 or 350 cells/mm3. This paper describes an approach for using Observational Data to emulate randomized clinical trials that compare dynamic regimes of the form initiate treatment within a certain time period of some time-varying covariate first crossing a particular threshold." We applied this method to Data from the French Hospital Database on HIV (FHDH-ANRS CO4), an Observational study of HIV-infected patients, in order to compare dynamic regimes of the form initiate treatment within m months after the recorded CD4 cell count first drops below x cells/mm3" where x takes values from 200 to 500 in increments of 10 and m takes values 0 or 3. We describe the method in the context of this example and discuss some complications that arise in emulating a randomized experiment using Observational Data.
Ben Jann - One of the best experts on this subject based on the ideXlab platform.
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estimating heterogeneous treatment effects with Observational Data
Sociological Methodology, 2012Co-Authors: Yu Xie, Jennie E Brand, Ben JannAbstract:Individuals differ not only in their background characteristics, but also in how they respond to a particular treatment, intervention, or stimulation. In particular, treatment effects may vary systematically by the propensity for treatment. In this paper, we discuss a practical approach to studying heterogeneous treatment effects as a function of the treatment propensity, under the same assumption commonly underlying regression analysis: ignorability. We describe one parametric method and two non-parametric methods for estimating interactions between treatment and the propensity for treatment. For the first method, we begin by estimating propensity scores for the probability of treatment given a set of observed covariates for each unit and construct balanced propensity score strata; we then estimate propensity score stratum-specific average treatment effects and evaluate a trend across them. For the second method, we match control units to treated units based on the propensity score and transform the Data into treatment-control comparisons at the most elementary level at which such comparisons can be constructed; we then estimate treatment effects as a function of the propensity score by fitting a non-parametric model as a smoothing device. For the third method, we first estimate non-parametric regressions of the outcome variable as a function of the propensity score separately for treated units and for control units and then take the difference between the two non-parametric regressions. We illustrate the application of these methods with an empirical example of the effects of college attendance on womens fertility.
Changwon Yoo - One of the best experts on this subject based on the ideXlab platform.
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Causal Discovery from a Mixture of Experimental and Observational Data
arXiv: Artificial Intelligence, 2013Co-Authors: Gregory F. Cooper, Changwon YooAbstract:This paper describes a Bayesian method for combining an arbitrary mixture of Observational and experimental Data in order to learn causal Bayesian networks. Observational Data are passively observed. Experimental Data, such as that produced by randomized controlled trials, result from the experimenter manipulating one or more variables (typically randomly) and observing the states of other variables. The paper presents a Bayesian method for learning the causal structure and parameters of the underlying causal process that is generating the Data, given that (1) the Data contains a mixture of Observational and experimental case records, and (2) the causal process is modeled as a causal Bayesian network. This learning method was applied using as input various mixtures of experimental and Observational Data that were generated from the ALARM causal Bayesian network. In these experiments, the absolute and relative quantities of experimental and Observational Data were varied systematically. For each of these training Datasets, the learning method was applied to predict the causal structure and to estimate the causal parameters that exist among randomly selected pairs of nodes in ALARM that are not confounded. The paper reports how these structure predictions and parameter estimates compare with the true causal structures and parameters as given by the ALARM network.
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causal discovery from a mixture of experimental and Observational Data
Uncertainty in Artificial Intelligence, 1999Co-Authors: Gregory F. Cooper, Changwon YooAbstract:This paper describes a Bayesian method for combining an arbitrary mixture of Observational and experimental Data in order to learn causal Bayesian networks. Observational Data are passively observed. Experimental Data, such as that produced by randomized controlled trials, result from the exoerimenter manioulatine one or more variables (tipically randomiy) and observing the states of other variables. The paper presents a Bayesian method for learning the causal structure and parameters of the underlying causal process that is generating the Data, given that (1) the Data contains a mixture of Observational and experimental case records, and (2) the causal process is modeled as a causal Bayesian network. This learning method was applied using as input various mixtures of experimental and Observational Data that were generated from the ALARM causal Bayesian network. In these experiments, the absolute and relative quantities of experimental and Observational Data were varied systematically. For each of these training Datasets, the learning method was applied to predict the causal structure and to estimate the causal parameters that exist among randomly selected pairs of nodes in ALARM that are not confounded. The paper reports how these structure predictions and parameter estimates compare with the true causal structures and parameters as given by the ALARM network.
