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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Estimating Time to ESRD in Children With CKD.

Susan L Furth1, Chris Pierce2, Wun Fung Hui3

  • 1Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.

American Journal of Kidney Diseases : the Official Journal of the National Kidney Foundation
|April 15, 2018
PubMed
Summary

This study developed a 6-stage risk system for pediatric chronic kidney disease (CKD) progression using estimated glomerular filtration rate (eGFR) and proteinuria (UPCR). The system accurately predicts CKD timelines in children, aiding clinical management.

Keywords:
Pediatricchildrenchronic kidney disease (CKD)disease progressiondisease stagingend-stage renal disease (ESRD)estimated glomerular filtration rate (eGFR)proteinuriarisk patternurinary protein-creatinine ratio (UPCR)

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Area of Science:

  • Pediatric Nephrology
  • Chronic Kidney Disease Research
  • Clinical Risk Stratification

Background:

  • The Kidney Disease: Improving Global Outcomes (KDIGO) guideline for chronic kidney disease (CKD) lacks pediatric data for risk stratification.
  • Existing CKD progression models are not well-established for pediatric populations.

Purpose of the Study:

  • To develop and validate a risk classification system for CKD progression in children.
  • To inform clinical management strategies by characterizing CKD progression timelines in pediatric patients.

Main Methods:

  • An observational cohort study involving 1,232 children from the CKiD and ESCAPE studies.
  • Accelerated failure time models were used to analyze the composite outcome of renal replacement therapy, 50% eGFR reduction, or eGFR < 15 mL/min/1.73m².
  • Risk stratification was based on combinations of estimated glomerular filtration rate (eGFR) and urine protein-creatinine ratio (UPCR) levels.

Main Results:

  • Six ordered stages were defined using eGFR and UPCR categories, describing the risk continuum for CKD progression.
  • Median time to an adverse event ranged from over 10 years (eGFR 45-90, UPCR <0.5) to 0.8 years (eGFR 15-30, UPCR >2.0).
  • Children with glomerular disease had a 43% shorter time to event compared to those with non-glomerular disease.

Conclusions:

  • A CKD staging system based on eGFR and proteinuria effectively characterizes progression timelines in children.
  • This risk stratification can guide management strategies for pediatric CKD.
  • The findings address a critical gap in evidence for pediatric CKD risk assessment.