Sentence examples for many important variables from inspiring English sources

Many important variables, including patients' HIV status, were inconsistently reported between studies.

In these reactors many important variables, which are related to end-use polymer properties, cannot be measured on-line or can only be measured at low sampling frequencies.

Another possible explanation is that the successfully transplanted kidneys with AKI were of otherwise higher quality than the rejected kidneys with AKI, though the study did adjust for many important variables like donor age and comorbidity.

Buying the charger and having it installed will average $2,000 (Nissan says $2,200), but there are many important variables that could raise the price quite a bit from there.

The disconnect comes because such studies contain too many important variables to measure.

Many important variables influencing the transition into vocational education and training can be integrated into the resource approach.

The response of deep-sea species to high habitat heterogeneity is poorly known and slope can be used as a proxy for many important ecological variables, such as current flow, sedimentation and substratum type.

Because many important clinical variables for our specific research question are not available in the MCBS, we collected medical record data from a subsample of patients to assess the validity of assumptions and to aid in the interpretation of our estimates.

Third, although many important confounding variables have been controlled in the analysis, further adjustment of other confounders such as genetic susceptibility may alter the associations.

Based on the gene expression profile from the entire breast cancer genome, CMTC also provides a molecular portfolio of 14 known prognostic gene signatures and 19 oncogenic pathways with the association of many important clinicopathological variables.

3 In a systematic review of 1084 studies on the surgical and nonsurgical management of fibroids in 2002, 36 the authors were unable to perform meta-analysis because of the lack of consistent data on many important preclinical variables and the use of different outcome measures across studies.