Sentence examples for use of multivariable modelling from inspiring English sources

A further strength is the use of multivariable modelling techniques to adjust for the effects of other variables and test for independence of associations, something that has not always been done in previous studies of this nature [ 5].

Another strength of this study is the use of multivariable modelling to identify associations with screening status while taking into account the clustered nature of the data.

Although we attempted to control for some of these variables through the use of multivariable models that included some of these factors (substance abuse and psychiatric diagnoses), residual confounding may remain.

As we did not randomise patients to the two different treatment regimens, we cannot exclude unmeasured confounding factors that may have influenced our outcomes; although we attempted to control for some of these variables through the use of multivariable models that included some of these factors (substance abuse and psychiatric diagnoses), residual confounding may remain.

Integral controllability (IC) is a desired property of multivariable models used in robust controller design.

To estimate how much each additional level of data contributed to the model, we developed a succession of multivariable models using increasing numbers of variables.

We constructed several types of multivariable models.

The data were analysed by use of two multivariable models where the response was the area of the shoulder ulcer on day 14 and day 21, respectively.

Heteroskedasticity and kurtosis of pertinent measures were determined to be within acceptable limits for the use of multivariable linear regression models.

Further, the risk of dying in women with methylation of the FBXW7/hCDC4-β isoform compared with women with unmethylated FBXW7/hCDC4-β isoform was modelled by use of multivariable proportional hazards (Cox) models, adjusted for possible confounders of survival such as age at diagnosis, date of primary tumour surgery, oestrogen receptor and progesterone receptor status, and P53 mutation.

These household age and occupation cluster groups were used in the multivariable modelling of dog ownership instead of individual variables for each age and occupation.