Sentence examples for backward stepwise elimination model from inspiring English sources

Results from the backward stepwise elimination model were reported.

We included all covariates in a backward stepwise elimination model (exclusion criteria p < 0.20) to evaluate the influence of these potential confounders on the effect of exposures to DDT or DDE on cryptorchidism and hypospadias.

Covariates with P < 0.1 were included in a multivariate logistic regression model (LBW, anaemia) or linear regression (birthweight) as a starting model for a backward stepwise elimination model selection procedure and considered independent risk factors if P < 0.05.

Backward stepwise elimination regression models were developed for each of the 5 separate groups of variables described in Table 1 for each phase of the physical activity program.

In situations with fewer than 10 observations per covariate, we used backward stepwise elimination terminating in models that included age, gender, HSCL10, and musculoskeletal complaints, and as many other covariates as possible with at least 10 cases per covariate.

Previous history of a mental disorder and the absence of a self-reported strong social support group remained significant predictors of depression in backward and stepwise elimination models for depression by both instruments.

The absence of a self-reported strong social support group (AOR 5.8, 95% CI 1.3 25.7) and previous history of a mental health disorder (AOR 5.8, 95% CI 1.9 17.7) also remained significant in the DASS-21 backward and stepwise elimination models.

The absence of a self-reported strong social support group (adjusted odds ratio [AOR] 4.3, 95% CI 1.4 13.7) and previous history of a mental health disorder (AOR 4.8, 95% CI 1.9 12.4) remained significant in the backward and stepwise elimination models for current depression by CES-D (Table  3).

Backward stepwise elimination found that the model which explained most of the variance in mean FA outside the lesion was the one with lesion size as the only factor (p = 0.006 for effect of lesion size).

The method of backward stepwise elimination was used as the model fitting strategy.

Others have chosen the patient covariates to be considered for inclusion in the model by constructing two multivariate logistic regression models, a pregnancy model and a twin pregnancy model, using a backward stepwise elimination [ 7].