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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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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Dense connective tissue contains more collagen fibers than loose connective tissue. As a consequence, it displays greater resistance to stretching. There are two major categories of dense connective tissue— regular and irregular.
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Longitudinal Studies01:26

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Homogeneous Equilibria for Gaseous Reactions02:15

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Homogeneous Equilibria for Gaseous Reactions
For gas-phase reactions, the equilibrium constant may be expressed in terms of either the molar concentrations (Kc) or partial pressures (Kp) of the reactants and products. A relation between these two K values may be simply derived from the ideal gas equation and the definition of molarity. According to the ideal gas equation:
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Tissue Homogenization and Cell Lysis01:32

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Tissue homogenization involves disintegrating tissue architecture and lysing cells, and is an early step in isolating and analyzing cellular components. The method used for homogenization depends on the sample type, the amount of sample available, the analyte to be obtained, and the sensitivity of the method. These methods are broadly classified as mechanical and non-mechanical methods.
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Subgroup identification via homogeneity pursuit for dense longitudinal/spatial data.

Jialiang Li1,2,3, Mu Yue4, Wenyang Zhang5

  • 1Department of Statistics and Applied Probability, National University of Singapore, Singapore.

Statistics in Medicine
|May 9, 2019
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Summary

Identifying patient subgroups with varied treatment responses is key for personalized medicine. Our new method efficiently finds these groups in complex biomedical data, improving treatment efficacy.

Keywords:
binary segmentationchange point detectiondense longitudinal datahomogeneity pursuitpersonalized medicinetreatment recommendation

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Area of Science:

  • Biostatistics
  • Personalized Medicine
  • Econometrics

Background:

  • Treatment efficacy varies significantly across patient subgroups, hindering personalized medicine.
  • Identifying these subgroups is crucial for developing tailored therapies and improving outcomes.

Purpose of the Study:

  • To develop a general framework for identifying patient subgroups with differential treatment effects.
  • To adapt econometric homogeneity pursuit methods for biomedical data analysis.

Main Methods:

  • Utilized homogeneity pursuit methods from econometric time series analysis.
  • Employed a change point detection algorithm suitable for dense longitudinal and spatial biomedical data.
  • Validated the method through extensive numerical simulations.

Main Results:

  • The proposed method demonstrates speed and accuracy in subgroup identification.
  • Successfully applied the method to analyze diffusion tensor imaging data.
  • The approach is effective for complex, high-density biomedical datasets.

Conclusions:

  • The developed framework offers a robust and efficient approach for subgroup identification.
  • This method facilitates the advancement of personalized medicine by uncovering treatment effect heterogeneity.
  • The technique is particularly well-suited for modern dense longitudinal and spatial biomedical data analysis.