Posts Tagged ‘ Confounding ’

Example 9.20: visualizing Simpson’s paradox

February 7, 2012
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Example 9.20: visualizing Simpson’s paradox

Simpson's paradox is always amazing to explain to students. What's bad for one group, and bad for another group is good for everyone, if you just collapse over the grouping variable. Unlike many mathematical paradoxes, this arises in a number of real...

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Example 7.36: Propensity score stratification

May 10, 2010
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Example 7.36: Propensity score stratification

In examples 7.34 and 7.35 we described methods using propensity scores to account for possible confounding factors in an observational study.In addition to adjusting for the propensity score in a multiple regression and matching on the propensity score...

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Example 7.35: Propensity score matching

May 3, 2010
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Example 7.35: Propensity score matching

As discussed in example 7.34, it's sometimes preferable to match on propensity scores, rather than adjust for them as a covariate.SASWe use a suite of macros written by Jon Kosanke and Erik Bergstralh at the Mayo Clinic. The dist macro calculates the ...

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Example 7.34: Propensity scores and causal inference from observational studies

April 26, 2010
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Example 7.34: Propensity scores and causal inference from observational studies

Propensity scores can be used to help make causal interpretation of observational data more plausible, by adjusting for other factors that may responsible for differences between groups. Heuristically, we estimate the probability of exposure, rather t...

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Design of Experiments – Blocking and Full Factorial Experimental Design Plans

December 6, 2009
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When considering using a full factorial experimental design there may be constraints on the number of experiments that can be run during a particular session, or there may be other practical constraints that introduce systematic differences into an experiment that can be handled during the design and analysis of the data collected during the experiment. Blocking

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