The phrase "ascertain bias" is correct and usable in written English.
It can be used when discussing the process of identifying or determining bias in a particular context, such as research, media, or personal opinions.
Example: "In order to evaluate the credibility of the study, we must first ascertain bias in the methodology used."
Alternatives: "identify bias" or "determine bias".
This may result in effect-size estimates that are upward biased (referred to as "ascertainment bias"); this is caused by ascertaining only those samples that result in genetic loci with evidence of significant association.
We stressed the importance of differentiating between LD within and LD between duplicates because they do not respond in the same way to the action of IGC and crossover and might prove to be useful in ascertaining biases such as donor-acceptor bias.
4 Cohort studies may be biased by ascertaining more severely affected cases, although this would be ameliorated by a highly ascertained cohort. 2 Although death certificate studies might be expected to overcome the ascertainment bias of cohort studies, they are dependent on NF1 being accurately recorded on the death certificate.
As they ascertained carriers from a population cancer registry, the study is less likely to have ascertainment bias because of family history.
Both meta-analyses used funnel plot methods to ascertain publication bias and concluded there was some evidence for publication bias, but not enough to negate the overall conclusion of increased risk.
A funnel plot was used to ascertain publication bias.
In order to ascertain the bias introduced by duplicate lines (an unintended consequence of the DH production process), all analyses were also conducted with a population size of n = 93 [ 20].
The main efficacy and safety outcomes (prevention of embolism and major bleeding) observed in the comparison between dabigatran and warfarin are displayed in figure 2 and table 4. The funnel plot to ascertain publication bias is shown in figure 3.
The primary intent of our biomarker validation study is to ascertain to what extent the classification performance of a biomarker can be attributed to disease associated response rather than to ascertainment biases in sample collection.
Recently, EPRI developed a set of benchmarks based on a large set of power distribution measurements to ascertain reactivity biases.
