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9 months ago in Statistical Analysis By Aaron Armstrong

Which family of generalized linear models (e.g., logistic regression, probit models) is optimal for estimating the probability of a binary outcome as a function of a continuous predictor, and how should model fit and assumptions be evaluated?

I'm modeling the probability of cell viability (live/dead) as a function of increasing drug concentration. While logistic regression is the default, I want to ensure I'm using the most appropriate link function and rigorously checking the model's assumptions beyond a simple significance test. The probit model is also mentioned in the literature for such dose-response relationships.

 

Statistical Analysis LogisticRegression BinomialTheorem

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By Sohail Answered 4 months ago

For your dose-response viability data, both logistic and probit models are excellent and typically yield nearly identical substantive conclusions. In my practice, I usually start with logistic regression due to its more intuitive odds-ratio interpretation. The optimal choice can hinge on the underlying error distribution you assume; probit implies a normal latent variable. More critical is the evaluation. I would recommend examining the Hosmer-Lemeshow test for global fit, scrutinizing residual plots (like deviance residuals vs. predicted values), and checking for influential points. Ensure your continuous predictor has a reasonably linear relationship with the log-odds.

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