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Related Concept Videos

Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Confidence Interval for Estimating Population Mean01:25

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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Nonlinear Pharmacokinetics: Dependence of Elimination Half-Life and Dose Clearance01:23

Nonlinear Pharmacokinetics: Dependence of Elimination Half-Life and Dose Clearance

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The elimination half-life and drug clearance of drugs following nonlinear kinetics can vary with dosage. The Michaelis-Menten parameters and drug concentration influence these factors. As the dose increases, the elimination half-life tends to lengthen, resulting in a reduction in clearance and a disproportionately larger area under the curve. The total clearance can be derived from the Michaelis-Menten equation for drugs following a one-compartment model.
A study on guinea pigs examined the...
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Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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An algorithm for computing profile likelihood based pointwise confidence intervals for nonlinear dose-response

Xiaowei Ren1,2, Jielai Xia1

  • 1Department of Health Statistics, Fourth Military Medical University, Xi'an, Shaanxi, China.

Plos One
|January 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method for estimating confidence intervals in nonlinear dose-response models, crucial for clinical trials. The developed algorithm efficiently calculates these intervals across the entire dose-response curve.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Estimating pointwise confidence intervals (CIs) for nonlinear dose-response models is essential in dose-finding clinical trials.
  • Profile likelihood-based CIs are recommended but challenging to compute explicitly for entire dose-response curves.
  • Existing research primarily compares CI methods, lacking focus on computing profile likelihood CIs for a full curve.

Purpose of the Study:

  • To propose and evaluate a method for calculating profile likelihood-based pointwise CIs for nonlinear dose-response models.
  • To address the computational gap in applying profile likelihood CIs to entire dose-response curves in dose-finding studies.

Main Methods:

  • Developed an algorithm using the bisection method with a specific calculation order for doses.
  • Incorporated a crude search for expected responses near boundaries to enhance accuracy.
  • Optimized convergence by using appropriate starting values and straightforward programming techniques.

Main Results:

  • The proposed algorithm successfully calculated profile likelihood-based pointwise CIs for a nonlinear dose-response curve.
  • Simulation studies indicated the algorithm performed well in most scenarios.
  • The method demonstrated potential for broader application to various dose-response models and data types.

Conclusions:

  • The developed computational approach enables routine use of profile likelihood methods for pointwise CIs in nonlinear dose-response modeling.
  • This advancement facilitates more reliable dose estimation in clinical trials.
  • The algorithm is adaptable for different statistical models and response data types.