Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

Instrument Calibration

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Estimating Population Mean with Unknown Standard Deviation

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Mechanistic modelling of highly pathogenic avian influenza: A scoping review revealing critical gaps in cross-species transmission models.

PloS one·2026

Same author

A novel side-to-side triple-row repair combined with biceps tendon augmentation for massive rotator cuff tears: a case series demonstrating satisfactory short-term outcomes.

BMC musculoskeletal disorders·2025

Same author

Tree-based latent variable model for assessing differential item functioning in patient-reported outcome measures: a simulation study.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2025

Same author

Shaping the future of cell and gene therapy workforce development: training of cell therapy processing personnel - perspectives from the International Society for Cell & Gene Therapy Lab Practices Committee.

Cytotherapy·2025

Same author

Non-TNFi biologic and targeted synthetic DMARDs in rheumatoid arthritis-associated interstitial lung disease: A propensity score-matched, active-comparator, new-user study.

Seminars in arthritis and rheumatism·2025

Same author

Use of non-qualifying umbilical cord blood units to support research and quality assurance by the Canadian Blood Services Cord Blood Bank.

Transfusion·2025

Same journal

Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Biometrics·2026

Same journal

Acknowledgment of Referees 2025.

Biometrics·2026

Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026

Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026

Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026

Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 31, 2026

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

A Bayesian adjustment for multiplicative measurement errors for a calibration problem with application to a stem cell

Peng Zhang¹, Juxin Liu, Jianghu Dong

¹Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada. pengz@ualberta.ca

|June 22, 2011

Summary

This summary is machine-generated.

This study introduces a Bayesian method to address measurement errors in stem cell dose calibration. The findings help establish recommended minimum stem cell doses for transplantation, improving patient safety.

More Related Videos

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Published on: July 17, 2018

Related Experiment Videos

Last Updated: May 31, 2026

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Published on: July 17, 2018

Area of Science:

Biostatistics
Hematology
Transplantation Science

Background:

Stem cell transplantation requires accurate dosage determination.
Measurement errors, specifically postcryopreservation recovery rates, complicate dose calibration.
Existing methods may not adequately account for these errors.

Purpose of the Study:

To develop a Bayesian approach for calibration problems with multiplicative measurement errors.
To quantify the impact of ignoring measurement errors on regression coefficients.
To establish recommended minimum stem cell doses for engraftment.

Main Methods:

A Bayesian calibration approach was developed for a covariate with multiplicative measurement errors.
A two-stage Bayesian method was proposed for model estimation using R2WinBUGS.
The method was illustrated using a stem cell study example.

Main Results:

The study examined the asymptotic bias introduced by ignoring measurement errors.
The proposed Bayesian method provides a robust approach to model estimation.
Results facilitate the establishment of evidence-based stem cell dosage recommendations.

Conclusions:

The developed Bayesian method effectively handles multiplicative measurement errors in calibration.
This approach supports peripheral blood stem cell laboratories in setting minimum dose recommendations.
It provides a systematic framework for deciding on the necessity of post-thaw analysis.