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Substituents on the benzene ring that direct an incoming electrophile to undergo substitution at the meta position are called meta directors. All meta directors either have a positive charge on the atom directly bonded to the ring or a partial positive charge. These groups function by withdrawing electrons from the ring through inductive and resonance effects. Consider the carbocation intermediates formed upon the addition of an electrophile on nitrobenzene at the...
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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meta-Directing Deactivators: –NO2, –CN, –CHO, –⁠CO2R, –COR, –CO2H01:13

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All meta-directing substituents are deactivating groups. These substituents withdraw electrons from the aromatic ring, making the ring less reactive toward electrophilic substitution. For example, the nitration of nitrobenzene is 100,000 times slower than that of benzene because of the deactivating effect of the nitro group. The first step in an electrophilic aromatic substitution is the addition of an electrophile to form a resonance-stabilized carbocation. The energy diagrams for...
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Bivariate network meta-analysis for surrogate endpoint evaluation.

Sylwia Bujkiewicz1, Dan Jackson2, John R Thompson3

  • 1Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.

Statistics in Medicine
|May 28, 2019
PubMed
Summary
This summary is machine-generated.

New bivariate network meta-analysis (bvNMA) methods improve healthcare decision-making by evaluating imperfect surrogate endpoints. These methods predict treatment effects on final outcomes more accurately, even when surrogacy varies across treatments.

Keywords:
Bayesian analysismultivariate meta-analysisnetwork meta-analysissurrogate endpoints

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Health Economics

Background:

  • Surrogate endpoints are crucial for regulatory decisions, offering early insights into long-term clinical outcomes.
  • Existing bivariate meta-analysis methods struggle with imperfect surrogate endpoints where treatment associations vary.
  • A need exists for methods that can differentiate treatment effects and model varying surrogacy levels.

Purpose of the Study:

  • To develop and evaluate novel bivariate network meta-analysis (bvNMA) methods.
  • To enhance the prediction of treatment effects on final clinical outcomes using surrogate endpoints.
  • To model treatment-specific surrogacy and identify reliable surrogate relationships.

Main Methods:

  • Developed bivariate network meta-analysis (bvNMA) integrating surrogate and final outcome data from multiple trials.
  • Estimated individual treatment contrast effects for both surrogate and final outcomes.
  • Modeled trial-level and treatment-level surrogacy patterns to assess prediction accuracy.

Main Results:

  • bvNMA methods effectively estimate treatment effects on both surrogate and final outcomes simultaneously.
  • The methods allow for modeling of varying surrogacy strength across different treatment comparisons.
  • bvNMA demonstrated improved prediction of final outcome treatment effects, particularly when surrogacy varied.

Conclusions:

  • Bivariate network meta-analysis (bvNMA) offers a robust framework for evaluating surrogate endpoints in healthcare.
  • bvNMA enhances prediction accuracy by accounting for treatment-specific surrogacy variations.
  • These methods support more reliable regulatory decision-making and treatment effect predictions.