Sentence examples for multivariate analysis variables from inspiring English sources

For the multivariate analysis, variables with P-values <0.25 were initially introduced into the model and removed following a backwards-stepwise selection procedure to leave only those with a P-value <0.05 in the final model.

In the multivariate analysis, variables with P-values <0.25 were initially introduced into the model (prevalence of anti-PfMSP-1 antibodies, gender, age, haemoglobin concentration at day 0, multiplicity of infection, presence of Pfmdr-1 codon mutation at position 86 in isolates from day 0 and day of recurrence and at position 184 in isolates from day of recurrence).

For multivariate analysis, variables were incorporated into the model in a stepwise manner.

In multivariate analysis, variables with P < 0.05 in the univariate analysis were included.

For multivariate analysis, variables with a p value <0.1 were retained in the models.

In the multivariate analysis, variables that were associated with better survival were age younger than 65 years, female gender, and thoracoscopy.

When we performed a multivariate analysis (variable introduced in the model: shock at admission, acute renal failure and 2007 IDSA/ATS agreement) the association was still significantly present (OR 0.39, 95%% CI 0.17 0.91).

Candidate variables for multivariate analysis were variables with a significant result from the univariate analysis.

For univariate and multivariate analysis, quantitative variables were expressed as dichotomous variables using either the median or a clinically relevant threshold.

All variables with P <0.1 in the univariate analysis were included in the multivariate analysis, and variables with P <0.05 were selected in the final model.

However, when controlling for other variables in the multivariate analysis this variable did not remain significant.