I have two groups that I follow over 4 time points (Baseline, Three months, Six months, and Year). The outcome is some binary variable, lets say presence or absence of cancer.
model <- glmer(Binary ~ Group + Time + Group:Time + (1 | Person), data = test, family = binomial(), nAGQ = 10, control = glmerControl(optimizer = "bobyqa"))
When I exponeniate the fixed effects log odds (with CIs), I get the following:
se <- sqrt(diag(vcov(model))) output <- cbind(Estimate = fixef(model), LL = fixef(model) - 1.96 * se, UL = fixef(model) + 1.96 * se) exp(output) Estimate LL UL (Intercept) 0.0293727 0.01996715 0.04320876 Group1 3.8690905 2.47674209 6.04417441 TimeSixMos 1.7443529 1.13560801 2.67941654 TimeThreeMos 2.1692536 1.45871709 3.22589007 TimeYear 3.1738210 2.06651131 4.87446620 Group1:TimeSixMos 0.7220189 0.38374473 1.35848475 Group1:TimeThreeMos 0.8459276 0.48017633 1.49027247 Group1:TimeYear 0.7085735 0.37362965 1.34378115
The probability from odds is odds / (1 + odds), but how can you calculate the predicted probability (of presence of cancer) for each group at each time point from this output? More so, how do you find the predicted probability for a mixed logistic model that uses categorical covariates?