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Section 3 presents the simulation for the interruption prediction model using multivariable regression model and then on trained neural network for best possible approximations.

We constructed a risk model using multivariable logistic regression and compared its predictive performance for the primary endpoint with and without addition of plasma HBP using area under the receiver operating curve (ROC area) and category-free net reclassification index (cfNRI) and integrated discrimination improvement (IDI).

The independent predictors of increasing severity of CIN as revealed by a proportional odds model using multivariable ordinal logistic regression included (i) currently receiving antiretroviral therapy [adjusted odds ratios (aOR): 2.24 (1.17, 4.26), p = 0.01] and (ii) presence of cervical high-risk HPV-DNA [aOR: 1.93 (1.13.28.28), p = 0.02].

Hazard ratios were calculated by a proportional hazards model using multivariable Cox regression analyzes.

Single SNP associations with disease risk were tested assuming an additive model, using multivariable logistic regression implemented in PLINK [ 31] including the following covariates: Age (defined for Mayo Clinic cohort as age at diagnosis/death/last diagnosis for clinical LOAD/autopsied LOAD/controls), sex, APOE ϵ4 dosage and series.

As post hoc analysis identified significant differences between controls and patients, the first model used multivariable analysis with backward elimination and pooled patient and control data to determine the relation of a diagnosis of HC, gender, age, peak HR achieved, body mass index, and oxygen consumption at peak exercise to HR at 1, 2, and 3 minutes after exercise.

Continuous outcomes will be modelled using multivariable linear regression and dichotomous outcomes will be modelled using multivariable logistic and log-linear regression methods.

Predictive models used multivariable logistic regression.

Sex-specific and anatomical subsite-specific models were also fitted using multivariable models 1 (579 cases and 1996 controls) and 2 (458 cases and 1734 controls).

Initially, dimensional models were built using multivariable logistic regression analyses of all study variables within each of the seven dimensions simultaneously entered into individual models.

The models were constructed using multivariable logistic regression, as described above, with gestational age at first ANC analyzed continuously and in trimesters.