Sentence examples for multivariate stepwise models from inspiring English sources

Multivariate stepwise models revealed that weight and height may be taken into account; however, their value added is mostly limited and depends on age-subgroup analysed.

Multivariate stepwise models were used to assess the predictive value of MMP levels compared with other potential baseline risk factors (such as, RF, nodules, CRP, ESR, HAQ, taking CVD drugs, smoking status).

The multivariate stepwise models further showed that except the health care workers (OR = 1.52; 95%CI 1.09-2.11), resinents in other occupations (OR = 0.06-0.67) were less likely to take up the A/H1N1 vaccination comparing with students (in Table 3).

Results: In the multivariate stepwise model the strongest predictor of ultrasound estimated fetal weight was basal hepatic glucose production, followed by late gestation insulin sensitivity (total R 2 = 0.27).

In a multivariate stepwise model, age 30 40 versus <30 (aOR 0.5; 95% CI 1.02 to 0.93) remained significantly associated with shingles in the model after adjusting for PTSD.

Conventional variables correlated with LSR with a p value < 0.05 at first univariate analyses were used to build the final multivariate stepwise model.

Multivariate stepwise model selection identified ECOG status, neutrophil count, number of disease sites, time since initial diagnosis, and body mass index as significantly associated with OS (P≤0.05, Table 2).

If height and weight were co-analysed with age in a multivariate stepwise model we found that age alone performed similarly well in gender-specific and overall analysis (Table  3).

In the final multivariate stepwise model, PTSD (adjusted OR (aOR) 1.7; 95% CI 1.03 to 2.89) and CD4 <200 versus >350 cells/µL (aOR 2.4; 95% CI 1.23 to 4.81) were independently associated with reported shingles.

In study 1 (discovery analysis), following Motzer et al (1999, 2002) and Heng et al (2009), baseline factors were first individually evaluated for association with OS using a univariate Cox proportional hazards model, and subsequently evaluated using multivariate stepwise model selection (forward selection at P≤0.1 to enter the model and backward selection at P≤0.05 to stay in the model) (Table 2).

Our multivariate stepwise regression models showed that primarily anthropogenic factors contribute to hybridization between naturally sympatric trout species in the more than 30 streams sampled.