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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.

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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Multiple-stage sampling procedure for covariate-adjusted response-adaptive designs.

Eunsik Park1, Yuan-Chin Ivan Chang2

  • 1Department of Statistics, Chonnam National University, Gwangju, Korea espark02@gmail.com.

Statistical Methods in Medical Research
|June 1, 2013
PubMed
Summary
This summary is machine-generated.

Covariate-adjusted response-adaptive (CARA) designs use sequential sampling to precisely estimate treatment effects. This method enhances sample size control for adaptive therapies, improving statistical efficiency in clinical research.

Keywords:
confidence setcovaraite adjustmentmultiple-stageresponse-adaptive designstopping rule

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Adaptive therapies and targeted medicine necessitate advanced statistical tools for treatment evaluation.
  • Covariate-adjusted response-adaptive (CARA) designs are crucial for comparing treatment performance in complex therapeutic strategies.
  • Sequential control of sample size is vital for achieving precise treatment effect estimates in adaptive clinical trials.

Purpose of the Study:

  • To introduce a multiple-stage sequential sampling method for CARA designs.
  • To enhance the feasibility of sample size control in adaptive therapy evaluations.
  • To investigate the theoretical properties of sequential sampling within CARA frameworks.

Main Methods:

  • Application of a multiple-stage sequential sampling procedure to CARA designs.
  • Theoretical analysis of regression parameter estimates under the proposed sampling method.
  • Examination of allocation probabilities in a randomly stopped sampling context.

Main Results:

  • Demonstration of feasible sample size control through the proposed sequential method.
  • Discussion of theoretical properties, including parameter estimation and allocation probabilities.
  • Validation of the method using synthesized data and a real-world clinical example.

Conclusions:

  • The multiple-stage sequential sampling method offers practical sample size control for CARA designs.
  • The proposed approach maintains desirable statistical properties for treatment effect estimation.
  • This methodology supports efficient evaluation of adaptive therapies in clinical research.