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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Maximum Deflection01:13

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When analyzing beams under unsymmetrical loads, such as a train moving on a bridge, it is crucial to accurately determine the points of maximum stress and deflection. The process involves identifying the maximum deflection of the beam, which may not always occur at its midpoint due to the uneven distribution of the load.
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Maximum Power Transfer01:16

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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Maximum Size of Aggregate01:12

Maximum Size of Aggregate

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Nursing Interventions I: Taxonomy of Nursing Interventions01:03

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Nursing interventions are chosen as part of the planning process to achieve patient outcomes. Once nursing diagnoses are determined, the goals and outcomes are specified, then the nursing interventions are selected and individualized according to the patient's situation.
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The Maximum Power Transfer Theorem01:20

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Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
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Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow
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Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions.

M Schomaker1,2, M A Luque-Fernandez3,4,5, V Leroy6

  • 1Centre for Infectious Disease Epidemiology & Research, University of Cape Town, Cape Town, South Africa.

Statistics in Medicine
|August 23, 2019
PubMed
Summary
This summary is machine-generated.

Longitudinal targeted maximum likelihood estimation (LTMLE) effectively estimates dynamic treatment effects despite complex confounding. Machine learning integration enhances stability, offering a practical alternative for epidemiological research.

Keywords:
HIV treatmentTMLEcausal inferenceg-methods

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Longitudinal targeted maximum likelihood estimation (LTMLE) is rarely applied to dynamic treatment effects with time-dependent confounding.
  • Complexities include long follow-up, multiple confounders, and challenging modeling.

Purpose of the Study:

  • Demonstrate LTMLE's successful application in realistic, complex epidemiological settings.
  • Compare LTMLE with competing estimators for dynamic treatment effects.

Main Methods:

  • Utilized LTMLE with machine learning for time-dependent confounding in HIV treatment research.
  • Addressed challenges: long follow-up, declining sample size, limited intervention support, high-dimensional data, and collider bias.

Main Results:

  • LTMLE provided stable estimates even with small samples and limited modeling options.
  • Machine learning with simple learners outperformed complex single models.
  • Intervention performance varied, highlighting the need for quality checks.

Conclusions:

  • LTMLE is a viable and robust method for estimating dynamic treatment effects in complex epidemiological studies.
  • Practical guidance is provided for LTMLE implementation, emphasizing machine learning integration and quality assessment.