I am having difficulty interpreting the effects of the covariates of a linear model with log-transformed response for two specific time points.

This is the model: $log(Y_t) = \beta_0 + \beta_1 * X_{1t} + \beta_2 * X_{2t}$

Let's say I have $Y_t = 100$ and $Y_{t+1} = 110$. Now I want to explain this increase in $Y$ from $t$ to $t+1$ in terms of the explanatory variables. Is it possible to split this $10$ units increase in $Y$ between $X_1$ and $X_2$, e.g. $Y$ increased by $7$ units due to $X_1$ and by $3$ units due to $X_2$?

How could I mathematically split the increase in $Y$ between the covariates for two specific time points?

  • $\begingroup$ Would you please post the data (or a link to the data) before transform? $\endgroup$ May 17, 2019 at 12:20
  • $\begingroup$ No you can't. Y could increase 10 units due to a change in X1 irrespective of X2, and Y could increase 10 units due to a change in X2 irrespective of X1. $\endgroup$
    – AdamO
    May 17, 2019 at 16:47

1 Answer 1


This is known as an Accelerated Failure Time model.


From wiki:

The interpretation of ${\displaystyle \theta }$ in accelerated failure time models is straightforward: ${\displaystyle \theta =2}$ means that everything in the relevant life history of an individual happens twice as fast. For example, if the model concerns the development of a tumor, it means that all of the pre-stages progress twice as fast as for the unexposed individual, implying that the expected time until a clinical disease is 0.5 of the baseline time. However, this does not mean that the hazard function ${\displaystyle \lambda (t|\theta )}$ is always twice as high - that would be the proportional hazards model.

A textbook reference on this sort of models is

Kalbfleisch & Prentice (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Hoboken, NJ: Wiley Series in Probability and Statistics.

  • $\begingroup$ The response is not a time-to-event. Even if it was, a time-to-event model does not have the ability to attribute hazard differences to two or more covariates. $\endgroup$
    – AdamO
    May 17, 2019 at 16:48

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