Sentence examples for trends in variables from inspiring English sources

Table 2 lists trends in variables relating to thyroid hormone transport across the control group and case subgroups that had either normal or relatively low RBP concentrations using polynomial contrasts.

Table  2 shows the estimated hazard ratios (HRs) for the primary and secondary endpoints for trends in variables that were significant after adjusting for multiple testing in individual variable analyses and after inclusion in multivariate analyses.> -wrap-foot> aConfidenceence interval; GCS, Glasgow Coma Scale; HR, Hazard ratio; ICU, Intensive care unit; SOFA, Sequential Organ Failure Assessment.

The trends in variables over the first 7 days of the ICU stay that remained significantly and independently associated with 6-month outcome were worsening thrombocytopaenia and renal function (total daily urine output and renal component of the SOFA score), highest recorded level of bilirubin and GCS component of the SOFA score.

The proportion of primiparous women aged more than 35 years was 15.1%% in 2000 and 21.4 % in 2010 (data not shown).> -wrap-foot> alive births of women 35 years and over/women population 35 years and over Source: Ministry of Health of Brazil Table  2 shows the correlation matrix among the time trends in variables studied.

Trends in variables were correlated to each other and to the trends in preterm births.

†χ tests for categorical variables (χ for trend in variables with more than two categories) and Wilcoxon rank sum test for continuous variables.

The trends in these variables and in intermediate variables concerned with their mechanisms of action were then compared to the trends in MMR in order to qualitatively and logically assess whether they might have contributed to changes in MMR in Malawi.

We have found applying Hill's criteria [ 35] useful in aiding our understanding of the potential causal relationships between trends in predictor variables and trends in outcome variables.

The overlap of values representing different clinically relevant sedation levels, as well as the high intra-individual variability, especially in Entropy®, calls into question even the use of trends in these variables to support clinical decisions or as therapeutic targets.

These last scenarios characterize the possible trends in the variables of change previously identified.

Incorporating month and interaction terms into the model helps to separate the effect of the intervention from general trends in outcome variables (Table 5, Model 2).