# How to account for incrementation in a log-linear model

I want to perform a mixed regression analysis with random intercept and uncorrelated random slope after multiple imputation.

The dependent variable is continuous, namely a duration as number of days (‘days’).

A lot of observations have the value 0 as you can the following table()-output depicts:

   0 0.25  0.5 0.75    1    2    3    4    5    6    7    8    9   10   11   12   14   16   18   19   20   24   29   35   38   56
220    1    8    2  199  128   35    8    6    2    5    7    4    5    4    3    1    1    1    1    1    1    1    1    1    1


When I use a linear model

with(imp, lmer(days ~ binary_predictor + ... +
(1 | group) + (0 + binary_predictor | group)))


I get for 20 of 20 imputations the message “singular fit”. Using qqp(days, "lnorm") shows that the probability distributions fits quite well after logarithmic transformation. However, due to the “0”-values I have to increment days by 1 for regression analysis.

with(imp, lmer(log(days + 1) ~ binary_predictor + ... +
(1 | group) + (0 + binary_predictor | group)))


The resulting estimate for binary_predictoris is 0.595 with exp(0.596) = 1.813031. Unfortunately, the estimate differs if I increment days by 2. Its then exp(0.408) = 1.503807.

Is there a way to account for the incrementation of days in the regression model? Or would you suggest another regression model instead?

I tried mixed Cox regression but the assumption of proportional hazards is violated.

UPDATE #1 after the answer of Dimitris Rizopoulos

After I found this website, I checked the fit of the probability distributions.

days.1 <- days + 1
days.int <- ceiling(days)


From the resulting plots I figured that lnorm would be the best probability distributions to use.    I also calculated a Poisson model

with(imp, glmer(days.int ~ binary_predictor + ... +
(1 | group) + (0 + binary_predictor | group)), family=poisson ))


                Estimate Std.Error   t.value        df   P(>|t|)       RIV       FMI
(Intercept)        0.167     0.190     0.879  5911.367     0.380     0.060     0.057
binary_predictor     1.037     0.178     5.814 74393.785     0.000     0.016     0.016


Unfortunately, I do not know how to interpret the estimate of the Poisson model. Is it in days or some kind of ratio?

exp(1.037) -> 2.820742


The result of the log-linear model

with(imp, lmer(log(days + 1) ~ binary_predictor + ... +
(1 | group) + (0 + binary_predictor | group)))


is quite diferent

                  Estimate   Std.Error     t.value          df     P(>|t|)         RIV         FMI
(Intercept)          0.494       0.180       2.745  551034.661       0.006       0.006       0.006
binary_predictor       0.595       0.158       3.763 4528779.275       0.000       0.002       0.002


with

exp(0.595) -> 1.813031

• One technique that can sometime be used is to replace zero values with very small values, such as 1.0E-10, which will allow logs to be used. As a practical matter, values such as 1.0E-10 days are effectively zero. Mar 24, 2019 at 11:26
• From the Poisson model the coefficients have an intepretation for the log expected number of day. Hence, if you expontiate them you get an intepretation for the expected number of days. With regard to the fit of the distribution to your data, check if you need to account for over-dispersion and/or extra zeros. Examples are in the links I provided in my answer. Mar 24, 2019 at 18:08