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Issa J. Dahabreh - One of the best experts on this subject based on the ideXlab platform.

  • on the Causal Interpretation of rate change methods the prior event rate ratio and rate difference
    American Journal of Epidemiology, 2021
    Co-Authors: Robertus Van Aalst, Edward W. Thommes, Maarten J. Postma, Ayman Chit, Issa J. Dahabreh
    Abstract:

    A growing number of studies use data before and after treatment initiation in groups exposed to different treatment strategies to estimate "Causal effects" using a ratio measure called the prior event rate ratio (PERR). Here, we offer a Causal Interpretation for PERR and its additive scale analog, the prior event rate difference (PERD). We show that Causal Interpretation of these measures requires untestable rate-change assumptions about the relationship between 1) the change of the counterfactual rate before and after treatment initiation in the treated group under hypothetical intervention to implement the control strategy; and 2) the change of the factual rate before and after treatment initiation in the control group. The rate-change assumption is on the multiplicative scale for PERR but on the additive scale for PERD; the 2 assumptions hold simultaneously under testable, but unlikely, conditions. Even if investigators can pick the most appropriate scale, the relevant rate-change assumption might not hold exactly, so we describe sensitivity analysis methods to examine how assumption violations of different magnitudes would affect study results. We illustrate the methods using data from a published study of proton pump inhibitors and pneumonia.

  • On the Causal Interpretation of rate-change methods: the prior event rate ratio and rate difference.
    American journal of epidemiology, 2020
    Co-Authors: Robertus Van Aalst, Edward W. Thommes, Maarten J. Postma, Ayman Chit, Issa J. Dahabreh
    Abstract:

    A growing number of studies use data before and after treatment initiation in groups exposed to different treatment strategies to estimate "Causal effects" using a ratio measure called the prior event rate ratio (PERR). Here, we offer a Causal Interpretation for PERR and its additive scale analog, the prior event rate difference (PERD). We show that Causal Interpretation of these measures requires untestable rate-change assumptions about the relationship between (1) the change of the counterfactual ratebefore and after treatment initiation in the treated group under hypothetical intervention to implement the control treatment; and (2) the change of the factual rate before and after treatment initiation in the control group. The rate-change assumption is on the multiplicative scale for PERR, but on the additive scale for PERD; the two assumptions hold simultaneously under testable, but unlikely, conditions. Even if investigators can pick the most appropriate scale, the relevant rate-change assumption may not hold exactly, so we describe sensitivity analysis methods to examine how assumption violations of different magnitudes would affect study results. We illustrate the methods using data from a published study of proton pump inhibitors and pneumonia.

M. A. Sagioro-leal - One of the best experts on this subject based on the ideXlab platform.

  • The Causal Interpretation of Conformally Coupled Scalar Field Quantum Cosmology
    General Relativity and Gravitation, 2000
    Co-Authors: J. Acacio De Barros, Nelson Pinto-neto, M. A. Sagioro-leal
    Abstract:

    We apply the Causal Interpretation of quantum mechanics to homogeneous and isotropic quantum cosmology, where the source of the gravitational field is a conformally coupled scalar field, and the maximally symmetric hypersurfaces have positive curvature. In order to simplify the system of coupled equations studied and study the quantum behavior near the singularity, we restricted ourselves to the cases where the scale factor is small. In this case, the general solution of the Wheeler–DeWitt equation is a discrete superposition of Hermitian polynomials multiplied by complex exponentials. Superpositions with up to two parcels are studied, and the phase diagrams of their corresponding Bohmian trajectories are analyzed in detail. Nonsingular periodic quantum solutions are found. We also find that singular quantum solutions present an inflationary era in the begining of the Universe. Numerical calculations indicates that these results remain valid for general superpositions.

  • The Causal Interpretation of Dust and Radiation Fluids Non-Singular Quantum Cosmologies
    1999
    Co-Authors: J. Acacio De Barrosa, N. Pinto-neto, M. A. Sagioro-leal
    Abstract:

    We apply the Causal Interpretation of quantum mechanics to homogeneous and isotropic quantum cosmology where the sources of the gravitational field are either dust or radiation perfect fluids. We find non-singular quantum trajectories which tends to the classical one when the scale factor becomes much larger then the Planck length. In this situation, the quantum potential becomes negligible. There are no particle horizons. As radiation is a good approximation for the matter content of the early universe, this result suggests that the universe can be eternal due to quantum effects.

  • The Causal Interpretation of dust and radiation fluid non-singular quantum cosmologies
    Physics Letters A, 1998
    Co-Authors: J. Acacio De Barros, Nelson Pinto-neto, M. A. Sagioro-leal
    Abstract:

    Abstract We apply the Causal Interpretation of quantum mechanics to homogeneous and isotropic quantum cosmology where the sources of the gravitational field are either dust or radiation perfect fluids. We find non-singular quantum trajectories which tend to the classical one when the scale factor becomes much larger than its minimum size. There are no particle horizons.

Nelson Pinto-neto - One of the best experts on this subject based on the ideXlab platform.

J. Acacio De Barros - One of the best experts on this subject based on the ideXlab platform.

Daniel Commenges - One of the best experts on this subject based on the ideXlab platform.

  • a general dynamical statistical model with Causal Interpretation
    Journal of The Royal Statistical Society Series B-statistical Methodology, 2009
    Co-Authors: Daniel Commenges, Anne Gegoutpetit
    Abstract:

    Summary.  We develop a general dynamical model as a framework for Causal Interpretation. We first state a criterion of local independence in terms of measurability of processes that are involved in the Doob–Meyer decomposition of stochastic processes; then we define direct and indirect influence. We propose a definition of Causal influence using the concepts of a ‘physical system’. This framework makes it possible to link descriptive and explicative statistical models, and encompasses quantitative processes and events. One of the features of the paper is the clear distinction between the model for the system and the model for the observation. We give a dynamical representation of a conventional joint model for human immunodeficiency virus load and CD4 cell counts. We show its inadequacy to capture Causal influences whereas in contrast known mechanisms of infection by the human immunodeficiency virus can be expressed directly through a system of differential equations.

  • A general dynamical statistical model with Causal Interpretation
    Journal of the Royal Statistical Society: Series B, 2009
    Co-Authors: Daniel Commenges, Anne Gégout-petit
    Abstract:

    We develop a general dynamical model as a framework for possible Causal Interpretation. We first state a criterion of local independence in terms of measurability of processes involved in the Doob-Meyer decomposition of stochastic processes, as in Aalen (1987); then we define direct and indirect influence. We propose a definition of Causal influence using the concepts of ``physical system''. This framework makes it possible to link descriptive and explicative statistical models, and encompasses quantitative processes and events. One of the features of this paper is the clear distinction between the model for the system and the model for the observation. We give a dynamical representation of a conventional joint model for HIV load and CD4 counts. We show its inadequacy to capture Causal influences while on the contrary known mechanisms of HIV infection can be expressed directly through a system of differential equations.

  • A general dynamical statistical model with Causal Interpretation
    Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2009
    Co-Authors: Daniel Commenges, Anne Gégout-petit
    Abstract:

    We develop a general dynamical model as a framework for Causal Interpretation. We first state a criterion of local independence in terms of measurability of processes that are involved in the Doob-Meyer decomposition of stochastic processes; then we define direct and indirect influence. We propose a definition of Causal influence using the concepts of a 'physical system'. This framework makes it possible to link descriptive and explicative statistical models, and encompasses quantitative processes and events. One of the features of the paper is the clear distinction between the model for the system and the model for the observation. We give a dynamical representation of a conventional joint model for human immunodeficiency virus load and CD4 cell counts. We show its inadequacy to capture Causal influences whereas in contrast known mechanisms of infection by the human immunodeficiency virus can be expressed directly through a system of differential equations. Copyright (c) 2009 Royal Statistical Society.

  • A general dynamical statistical model with possible Causal Interpretation
    arXiv: Statistics Theory, 2007
    Co-Authors: Daniel Commenges, Anne Gégout-petit
    Abstract:

    Summary. We develop a general dynamical model as a framework for possible Causal Interpretation. We first state a criterion of local independence in terms of measurability of processes involved in the Doob-Meyer decomposition of stochastic processes, as in Aalen (1987); then we define direct and indirect influence. We propose a definition of Causal influence using the concepts of “physical system”. This framework makes it possible to link descriptive and explicative statistical models, and encompasses quantitative processes and events. One of the features of this paper is the clear distinction between the model for the system and the model for the observation. We give a dynamical representation of a conventional joint model for HIV load and CD4 counts. We show its inadequacy to capture Causal influences