Sentence examples for baseline survivor function and from inspiring English sources

The Cox models were used to derive the baseline survivor function and the hazard ratio associated with each prognostic factor.

For a group of patients with covariates (x1, x2, … x p), the model is written mathematically as S (t )= S 0 ϕt ) where S0 t) is the baseline survivor function and ϕ is an 'acceleration factor' that depends on the covariates according to the formula ϕ =exp{(b 1 x 1+ b 2 x 2+⋯+ b p x p )}.

PREDICT uses the baseline survivor function and the hazard ratio estimates (Table  1) to predict survival for a patient with a specific set of prognostic factors without adjuvant therapy and with adjuvant hormone therapy or chemotherapy assuming the relative risk reductions reported by the Early Breast Cancer Trialists Collaborative Group overview [ 10].

We combined these weights with the baseline survivor function evaluated at 1 and 2 years to derive a risk equation which could be applied for each time period.

We used the baseline survivor function from the ER negative and ER positive Cox proportional hazards models for breast cancer specific survival adjusted for the other prognostic factors to estimate the predicted number of deaths from breast cancer.

We used coefficients from the Cox proportional hazards model as weights for the probability of CVD event in 5 years and the baseline survivor function to obtain the risk equations (11).

We combined these weights with the baseline survivor function for diagnosis of diabetes evaluated at 10 years and centred on the means of continuous risk factors to derive a risk equation for 10 years' follow-up.

Alternative methods include the method of Buckley and James (1979), which is discussed by Stare et al (2000), and semiparametric AFT models, in which the baseline survivor function is estimated nonparametrically (see Wei, 1992, for an overview), but have not yet been widely implemented in statistical software.

We took the regression coefficients for each variable from the final models and used these as weights which we combined with the baseline survivor function for moderate-severe CKD evaluated at 5 years to derive risk equations for (a) moderate-severe CKD and (b) End Stage Kidney Failure at 5 years' follow-up.

We took the regression coefficients for each variable from the final models and used these as weights, which we combined with the baseline survivor function for each outcome evaluated at each of 1-5 years to derive absolute risk equations at each years' follow-up.

Deaths from other causes were estimated from the baseline survivor function for competing mortality after adjusting for age.