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Shells justify assess calculatestratifybivariable designtestingtableunivariableMeasures of associationvariablesDescriptive analysisanalysisPARTICIPANT WORKBOOKconfoundingstatistical software confidence
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How to fill out confounding

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Start by identifying the variables that may act as confounders in your study.
02
Collect data on these confounding variables along with your main exposure and outcome variables.
03
Analyze the data to determine the potential confounding effects.
04
Use statistical techniques such as stratification, regression modeling, or propensity score matching to address confounding.
05
Interpret the results by considering the adjusted estimates and confidence intervals obtained after accounting for confounding.
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Report your findings and discuss the implications of confounding in your study.

Who needs confounding?

01
Researchers conducting observational studies or epidemiological research often need to consider confounding.
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Any study aiming to establish causality or draw valid conclusions about the relationship between an exposure and an outcome would benefit from addressing confounding.
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Healthcare professionals, policymakers, and public health experts who rely on scientific evidence to inform decision-making also need to understand and account for confounding.
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Confounding is the presence of a third variable that influences the relationship between the independent and dependent variables.
Researchers conducting studies or experiments that involve multiple variables are required to consider and account for confounding.
Confounding can be addressed by using statistical techniques such as multivariable regression analysis or stratification.
The purpose of addressing confounding is to ensure that the observed relationship between variables is not influenced by third variables.
Researchers must report on the potential confounding variables identified, the methods used to address confounding, and the results of the analysis.
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