Models are fitted to predict wheezing at age 12 based on its presence/absence at earlier ages. Estimated values and AIC comparisons would identify the best model, but actual data is needed to provide specifics.
To predict the presence of wheezing at age 12 using a transitional logistic regression model based on various combinations of presence and absence of wheezing at earlier ages, we need to fit multiple models.
We will be making predictions based on the presence or absence of wheezing at ages 11, 10 and 9.
For each model, we use the count as weight and determine the estimated values. After fitting the models, we compare them using the Akaike Information Criterion (AIC), where the model with the lowest AIC is considered the best fit for the data.
Without actual data, we cannot provide the estimated values or the best model based on AIC. However, we would typically use statistical software to perform this analysis and output both the estimated coefficients and their AIC values.