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Related Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Critical Values01:31

Critical Values

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A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
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Econometric Views (EViews)01:29

Econometric Views (EViews)

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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Critical Region, Critical Values and Significance Level01:16

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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
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The use of the E-value for sensitivity analysis.

William T Chung1, Kevin C Chung2

  • 1Clinical Research Assistant, Section of Plastic Surgery, Department of Surgery, University of Michigan Hospital, Ann Arbor, MI, USA.

Journal of Clinical Epidemiology
|October 2, 2023
PubMed
Summary
This summary is machine-generated.

Observational studies can be prone to bias from unmeasured confounders. The E-value quantifies the necessary strength of such confounding to alter study findings, enhancing the robustness of clinical research.

Keywords:
ConfoundingE-valueObservational researchSensitivity analysis

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Area of Science:

  • Clinical research methodology
  • Epidemiology
  • Biostatistics

Background:

  • Observational studies are crucial for clinical research when randomized-controlled trials are infeasible.
  • Lack of randomization increases susceptibility to confounding bias.
  • Existing methods like propensity score matching and regression adjust for measured confounders.

Purpose of the Study:

  • To introduce the E-value as a tool for assessing unmeasured confounding in observational studies.
  • To explain the evaluation and presentation of the E-value in clinical research.

Main Methods:

  • Discusses analytical methods to reduce confounding, including propensity score matching and regression analysis.
  • Introduces sensitivity analyses to evaluate the impact of unmeasured confounding.
  • Focuses on the E-value as a specific approach for quantifying unmeasured confounding.

Main Results:

  • The E-value provides a quantitative measure of the strength of unmeasured confounding.
  • It helps assess the robustness of results from observational studies.
  • The article serves as an introductory guide to its application.

Conclusions:

  • The E-value is a valuable heuristic for evaluating the impact of unmeasured confounding.
  • It aids in interpreting the reliability of findings from observational clinical research.
  • Understanding and applying the E-value can strengthen the conclusions drawn from observational data.