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Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Regulated Protein Degradation02:58

Regulated Protein Degradation

It is vital to regulate the activity of enzymatic as well as non-enzymatic proteins inside the cell. This can be achieved either through creating a balance between their rate of synthesis and degradation or regulating the intrinsic activity of the protein. Both these regulation mechanisms play an essential role in the normal functioning of cells.
Protein degradation plays two important roles in the cells. It helps to protect cells from misfolded or damaged proteins before they lead to a...
Regulated Protein Degradation02:58

Regulated Protein Degradation

It is vital to regulate the activity of enzymatic as well as non-enzymatic proteins inside the cell. This can be achieved either through creating a balance between their rate of synthesis and degradation or regulating the intrinsic activity of the protein. Both these regulation mechanisms play an essential role in the normal functioning of cells.
Protein degradation plays two important roles in the cells. It helps to protect cells from misfolded or damaged proteins before they lead to a...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis
06:59

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis

Published on: August 11, 2010

Penalized Functional Regression.

Jeff Goldsmith1, Jennifer Bobb, Ciprian M Crainiceanu

  • 1Johns Hopkins Bloomberg School of Public Health Biostatistics, Baltimore, MD 21205.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|February 28, 2012
PubMed
Summary
This summary is machine-generated.

We present fast statistical methods for analyzing functional data, applicable to diverse scientific research, including brain imaging studies for multiple sclerosis (MS). These techniques efficiently handle complex data structures for robust analysis.

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Area of Science:

  • Statistics
  • Biostatistics
  • Neuroimaging Analysis

Background:

  • Generalized functional linear models are crucial for analyzing data where predictors are functions.
  • Existing methods can be computationally intensive and limited in handling complex functional data structures.

Purpose of the Study:

  • To develop computationally efficient and flexible methods for fitting generalized functional linear models.
  • To provide a robust framework applicable to various functional data scenarios, including neuroimaging.

Main Methods:

  • Projecting functional predictors onto smooth eigenvectors.
  • Estimating coefficient functions using penalized spline regression.
  • Obtaining confidence intervals via a mixed model framework.

Main Results:

  • The proposed methods are computationally fast and can be implemented using standard mixed-effects software.
  • The approach accommodates various data complexities, such as functions with errors, sparse/dense sampling, and multiple/multilevel functional predictors.
  • Demonstrated applicability in analyzing diffusion tensor imaging (DTI) data for multiple sclerosis (MS) research.

Conclusions:

  • The developed methods offer a significant advancement in analyzing functional data across diverse scientific fields.
  • The methodology provides a versatile and efficient tool for researchers, particularly in neuroimaging and clinical studies.
  • The R implementation facilitates widespread adoption and application of these advanced statistical techniques.