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Counterfactual Thinking01:19

Counterfactual Thinking

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Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in...
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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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In 1865, August Kekule suggested the structure of benzene according to the structural theory of organic chemistry based on the three assertions—formula of benzene is C6H6, all the hydrogens of benzene are equivalent, and each carbon must have four bonds due to its tetravalency.
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Lipids include a diverse group of compounds that are largely nonpolar in nature. This is because they are hydrocarbons that include mostly nonpolar carbon-carbon or carbon-hydrogen bonds. Non-polar molecules are hydrophobic (“water fearing”), or insoluble in water. Lipids perform many different functions in a cell. Cells store energy for long-term use in the form of fats. Lipids also provide insulation from the environment for plants and animals. For example, they help keep aquatic...
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Marginal Structural Models with Counterfactual Effect Modifiers.

Wenjing Zheng1,2, Zhehui Luo3, Mark J van der Laan1,2

  • 1Division of Biostatistics, University of California, Berkeley, USA.

The International Journal of Biostatistics
|June 9, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a robust method for estimating treatment effects modified by patient characteristics over time. The projected Targeted Maximum Likelihood Estimation (TMLE) offers an efficient and practical approach for complex health data analysis.

Keywords:
causal inferenceeffect modificationepidemiologymachine learning

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Area of Science:

  • Causal inference in health and social sciences.
  • Statistical modeling of treatment effects.
  • Longitudinal data analysis.

Background:

  • Treatment effect modification by patient characteristics is crucial in health research.
  • Time-varying effect modifiers require advanced statistical methods.
  • Existing methods may face challenges with high-dimensional data.

Purpose of the Study:

  • To investigate robust and efficient estimation of Counterfactual-History-Adjusted Marginal Structural Models.
  • To develop a semiparametric efficient and doubly robust estimator using Targeted Maximum Likelihood Estimation (TMLE).
  • To introduce a projected TMLE estimator for high-dimensional effect modifiers.

Main Methods:

  • Established semiparametric efficiency theory for these models.
  • Developed a substitution-based, semiparametric efficient, and doubly robust TMLE estimator.
  • Proposed a projected influence function and projected TMLE estimator for high-dimensional data.

Main Results:

  • The projected TMLE estimator retains robustness and is easier to implement for high-dimensional modifiers.
  • Comparative performance assessed against Inverse Probability of Treatment Weighted and G-computation estimators.
  • Demonstrated utility in a secondary analysis of the STAR*D trial with missing data.

Conclusions:

  • The projected TMLE provides a practical and robust solution for estimating causal effects with complex effect modification.
  • This methodology enhances the analysis of longitudinal data in health and social sciences.
  • The approach is particularly valuable when dealing with high-dimensional or missing effect modifiers.