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Our application of an a priori protocol for selecting and appraising evidence reduces selection bias.
However, using broad inclusion criteria reduces selection bias and, arguably, represents the true clinical population admitted to an emergency setting with acute headache.
Randomisation reduces selection bias, increases the validity of the findings and, in principle, is always an appropriate and desirable aspect of good experimental design when two or more treatments are compared [3].
This reduces selection bias, increases power, and increases the external validity.
This reduces selection bias due to non-random missing in the covariates.
Propensity score matching reduces selection bias that might arise when comparing two different treatment options.
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The study's population-based design within the setting of a tax supported universal healthcare system reduces selection biases.
The use of such designs reduces selection biases by ensuring that control individuals truly represent the population from which the cases came from [ 23, 24].
To reduce selection bias, patients were randomly selected from the hospital database.
To reduce selection bias, the sample was selected using a multistage technique.
In order to reduce selection bias, outpatients were randomly selected from site medical information rosters of active clients.
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