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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
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Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Data Collection by Observations01:08

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Random Sampling Method

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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. Among the various sampling methods used by...
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An R-Based Landscape Validation of a Competing Risk Model
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Quasi-rerandomization for observational studies.

Hengtao Zhang1, Wen Su1, Guosheng Yin2

  • 1Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China.

BMC Medical Research Methodology
|June 30, 2023
PubMed
Summary
This summary is machine-generated.

We introduce quasi-rerandomization, a new method for observational studies that improves covariate balance and treatment effect estimation. This approach mimics rerandomization, offering advantages over existing balancing techniques.

Keywords:
Causal inferenceCovariate balanceObservational dataRerandomizationTreatment effect

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Area of Science:

  • Causal inference
  • Observational studies
  • Statistical methodology

Background:

  • Covariate balance is crucial for approximating randomized experiments in observational studies.
  • Existing balancing methods lack clarity on the type of randomized experiments they emulate, hindering synthesis.
  • Rerandomization shows promise for covariate balance but hasn't been applied to observational data.

Purpose of the Study:

  • To propose a novel reweighting method, quasi-rerandomization, for improving covariate balance in observational studies.
  • To integrate the principles of rerandomization into observational data analysis.
  • To enable the reconstruction of rerandomized covariate balance using weighted data.

Main Methods:

  • Developed quasi-rerandomization, a reweighting technique for observational studies.
  • Used observational covariates as anchors for rerandomization in the reweighting process.
  • Aimed to reconstruct the covariate balance achieved through rerandomization via weighted data.

Main Results:

  • Quasi-rerandomization demonstrated comparable covariate balance and treatment effect estimation precision to rerandomization.
  • The proposed method showed advantages over other balancing techniques in treatment effect inference.
  • Numerical studies confirmed the effectiveness of the quasi-rerandomization approach.

Conclusions:

  • Quasi-rerandomization effectively approximates rerandomized experiments for improved covariate balance and precise treatment effect estimation.
  • The method performs competitively against existing weighting and matching techniques.
  • Open-source code is available for reproducibility and further research.