The phrase "a survival model" is correct and usable in written English.
It can be used in contexts related to statistics, biology, or any field that involves analyzing survival data or predicting outcomes based on survival analysis.
Example: "Researchers developed a survival model to predict the likelihood of patients surviving after treatment."
Alternatives: "a survival analysis" or "a survival framework".
In this paper, we investigate whether the predictive power of a survival model based on clinical patient features improves when MRI features are also included in the model.
Lastly, we control for selection at exit with a survival model, which also explicitly accommodates for the structure of our dependent variable.
MEASUREMENTS: A survival model was developed using the following predictor variables: diagnosis, age, number of days in the hospital before study entry, presence of cancer, neurologic function, and 11 physiologic measures recorded on day 3 after study entry.
Mortality during the whole FET time span was evaluated through Cox proportional hazards analysis, a survival model considering multiple observation events [57] used in clinical, epidemiologic and also in ecotoxicological [58] research.
We compare the benchmark specification of the linear probability model with a panel linear probability model with random effects, a panel linear probability model with fixed effects, and the marginal effects of a survival model.
We present the main regressions in Appendix B: Table 3. Column (1) shows the results from a pooled logit, column (2) from an OLS, column (3) from a panel OLS with random effects, column (4) from a panel OLS with fixed effects, and column (5) from a survival model (a pooled complementary log-log model).
For growth, there must also be a within-species standard-deviation, called the residual, which we assumed to be constant for all species (the same assumption is used is mixed models in lme4); there is no residual in a survival model because the binomial distribution defines the variation.
Given the longitudinal character of our data in which the observed customer tenure varies significantly across time and because most of the observations are right censored (because upselling events can occur any time during the customer tenure), we use a survival model to analyze the effect of the social media activity level on the likelihood and timing of the upselling.
The larger the C-index, the better is the predictability of a survival model.
To test the hypothesis that the video-based intervention compared to the text-based intervention increased the likelihood of HIV testing at the clinic in a survival model, we calculated adjusted Hazard Ratios using the Cox proportional hazards model.
The last model is a survival model.
