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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Introduction to z Scores01:06

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A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
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Introduction to z Scores01:05

Introduction to z Scores

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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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z Scores and Area Under the Curve01:17

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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Correlation01:09

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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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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Propensity score analysis for correlated subgroup effects.

Shan-Yu Liu1, Chunyan Liu2, Eddie Nehus3

  • 1Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, USA.

Statistical Methods in Medical Research
|May 31, 2019
PubMed
Summary
This summary is machine-generated.

Estimating subgroup treatment effects requires careful control of confounding variables. Jointly analyzing multiple identifiers in propensity score models is crucial for accurate subgroup effect estimation, especially in observational studies.

Keywords:
Effect modificationdoubly robust estimationpropensity scoresubgroup analysis

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Estimating treatment effects within specific subgroups is vital for personalized medicine.
  • Correlated factors defining subgroups complicate effect estimation, particularly in observational studies requiring confounding control.
  • Propensity score methods are commonly used for confounding control but face challenges with subgroup analysis.

Purpose of the Study:

  • To address the complexities of estimating subgroup effects when propensity score methods are used for confounding control.
  • To evaluate the common practice of analyzing subgroup identifiers individually versus jointly.
  • To investigate the performance of a whole-cohort approach with interaction terms for propensity score estimation.

Main Methods:

  • The study examines the limitations of analyzing subgroup identifiers one at a time.
  • It proposes and evaluates a whole-cohort approach for propensity score estimation, incorporating interaction terms.
  • The performance of this approach is assessed using simulations and real-world data analysis, with and without variable selection on interaction terms.

Main Results:

  • Analyzing subgroup identifiers individually can yield misleading results if true effect modifiers are correlated.
  • Jointly analyzing multiple identifiers requires propensity scores that adequately balance covariates within strata, which is practically challenging.
  • The whole-cohort approach, particularly with high-order interactions in the propensity score model, is effective in ensuring covariate balance across strata.

Conclusions:

  • The common practice of evaluating subgroup identifiers one at a time without adjustment can be misleading.
  • The whole-cohort approach for propensity score estimation, including high-order interactions, is recommended for accurate subgroup effect estimation.
  • Variable selection on interaction terms offers limited utility in improving covariate balance within the whole-cohort approach.