Sentence examples for variable analysis or from inspiring English sources

This determination is typically referred to as instrumental variable analysis or Mendelian randomisation [ 18, 19].

Only if this is accomplished will epidemiologists be in a position to credibly advise regulators, clinicians, and patients about whether instrumental variable analysis or conventional multivariable regression gives the least biased indication of causal treatment effects.

We did not evaluate other popular approaches that do not use information on the source of unwanted variation, the exemplar being surrogate variable analysis, or simpler methods as regressing with the principal components, as the main focus here was highlighting the requirement for batch effect correction, rather than evaluating methods.

To overcome this limitation we collect data on the type of ICS (fluticasone or fluticasone/salmeterol) as a co-variable, which enables us to include it as a confounder in the multi-variable analysis or to perform stratified analysis.

Similarly, femorotibial bone marrow oedema was the only statistically significant variable in multivariate analysis (OR 5.38 (95% CI 1.67 17.3), p = 0.005).

In addition, higher MBL levels were identified as risk factor for a cystatin C increase >10% when MBL levels were entered as continuous variable into the analysis (OR 1.32 (95% CI 1.10-1.58) for every 1000 ng/mL increase, p = 0.003).

The data on various biochemical and pathological parameters as well as Tiam1 expression were analysed by Cox proportional hazard method, using single variable analysis (univariate analysis) or step-wise selection (multivariate) analysis.

*MLVA, multiple locus variable-number tandem repeat analysis; OR, odds ratio.

Therefore, the key question for empirical researchers, regulators, and clinicians is: which is more biased conventional multivariable adjusted regression or instrumental variable analysis?

Our probit regression analysis showed higher BMI was associated with major depressive disorder but no association was found in instrumental variable analysis, suggesting either reverse causality or the existence of important unmeasured confounders not entered in our conventional regression analysis.

The method is designed to be effective for relatively small studies and robust to outliers and has been shown to be more effective than other commonly used algorithms such as distance-weighted discrimination or surrogate variable analysis [ 31].