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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.
A...
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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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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.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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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Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Reliability and Validity01:29

Reliability and Validity

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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How to obtain valid tests and confidence intervals after propensity score variable selection?

Oliver Dukes1, Stijn Vansteelandt1,2

  • 1Department of Applied Mathematics, Computer Sciences and Statistics, Ghent University, Belgium.

Statistical Methods in Medical Research
|August 7, 2019
PubMed
Summary
This summary is machine-generated.

Selecting confounders for observational studies is challenging. This research introduces a novel doubly robust g-estimator using propensity scores, improving treatment effect estimation by addressing model selection uncertainty.

Keywords:
Causal inferencedouble robustnesshigh-dimensional statisticsmodel uncertaintyvariable selection

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Observational Studies

Background:

  • Variable selection for confounding adjustment in observational studies is a critical challenge.
  • Existing methods like stepwise selection, change-in-estimate, and lasso are criticized for inadequate confounder prioritization and ignoring model selection uncertainty.
  • Newer methods aim to prioritize confounders but lack guaranteed finite-sample performance due to estimator instability and ignored uncertainty.

Purpose of the Study:

  • To evaluate the finite-sample distribution of exposure effect estimators under various confounder selection procedures.
  • To develop a generic and simple solution that overcomes the limitations of existing confounder selection methods.
  • To propose a method that incorporates uncertainty induced by variable selection into confidence intervals.

Main Methods:

  • Evaluation of the finite-sample distribution of the exposure effect estimator in linear regression.
  • Development of a doubly robust 'g-estimator' using propensity scores within generalized linear models.
  • Utilizing separate regularized regressions for outcome and propensity score models.

Main Results:

  • The proposed g-estimator provides a generic solution within generalized linear models, overcoming limitations of existing procedures.
  • The method achieves adequate performance under weaker conditions compared to competing proposals.
  • When outcome and propensity score models are sparse and correctly specified, standard confidence intervals for the g-estimator implicitly account for model selection uncertainty.

Conclusions:

  • The proposed doubly robust g-estimator offers a robust and statistically sound approach to confounding adjustment in observational studies.
  • This method effectively addresses the critical issue of model selection uncertainty, leading to more reliable treatment effect estimates.
  • The approach provides a significant advancement in handling confounding in observational research, particularly within generalized linear models.