Sentence examples for bias in the analytic from inspiring English sources

We linked an extensive array of possible risk factors (both traditional and novel) obtained from the baseline interview data for 5,511 of the AHEAD participants to their Medicare claims for up to 12 years of post-baseline surveillance, and we used propensity score methods to adjust for potential selection bias in the analytic sample.

The Study DIAD approach might be subjective, but offers the possibility to calculate quality ratings for published and unpublished studies, which is very important to prevent the publication bias in the meta-analytic results.

Furthermore, regarding the pooled estimates, the combination of results obtained using different exposure indicators increases heterogeneity and the risk of bias in the meta-analytic estimates, in particular when thresholds or exposure-response curves are combined.

Figure 2 shows a comparison between the simulated average bias and the analytic approximate bias of Equation 15 derived before.

The direction and strength of the impact of survivor bias on the analytic results are not clear a priori.

Therefore, the selection procedures at inception for CONSTANCES participants should generate minimal bias in analytic studies, although the observed exposure effect relationships may be affected if the highest exposures are underrepresented, which often happens for factors like alcohol or tobacco consumption.

We will then use the inverse probability weighting method to assess and adjust for selection bias in our analytic models.

Although we cannot control the former situation, we will try to minimize the bias in the latter through sampling and analytic methods.

The absolute mean difference was calculated to get an indication of the potential bias in meta-analytic outcomes due to the incongruence.

One advantage of including several researchers in the analytic process is to reduce individual biases (Patton, 1990).

To assess selection bias, we used inverse probability weighting (IPW), which assigns a weight to each subject in the analytic sample so each subject accounts for himself or herself in the analysis as well as those with similar characteristics who were not selected into our final analytic sample (19).