Sentence examples for models predicting depression from inspiring English sources

Binomial regression models predicting depression utilized forward selection model building with total concussion history as the main exposure.

In multivariable models predicting depression and poor or fair self-rated physical health, the interaction between diabetes and age was significant after adjustment for sex, ethnic group, and education level (Supplementary Table 2).

The final model predicting depression included: having multiple vascular co-morbidities compared with none; having less social support; having experienced a recent stressful life event; female sex; and being a smoker.

In the model predicting depressive symptomatology, age, education, and a history of depression were significant predictors.

Latent growth curve models were estimated for stress, for depressive symptoms, and for stress predicting depression net of several covariates.

The model supports a complex interaction between personal resources and negative symptoms in predicting depression in schizophrenia.

Two separate analyses were conducted: depression predicting type 2 diabetes and type 2 diabetes predicting depression.

To examine possible differences in the influence of positive and negative images, imagery distress was first entered into a multiple regression model that predicted depression scores at t1.

The linear mixed model predicting SMS depression scores over 15 weeks (adjusting for baseline PHQ-9) revealed significant fixed effects of trial arm (F2 = 4.99, p = 0.007), time (F14 = 8.78, p < .001) and arm by time interaction (F28 = 1.78, p = 0.007).

Radiation therapy, employment change and personal income change did not contribute to models predicting persistent symptoms of anxiety or depression.

Combining FHI lifetime depression and anxiety problems in four models predicting both outcome scores for boys and girls, again only FHI lifetime depression significantly predicted all outcome scores.