Assumptions of logistic regression include linearity and independence of errors, neither of which were violated in the following logistic regression models.
3.1.1 Hypothesis 1A: ASD to non-ASD
A logistic regression model including relevant predictors was developed for hypothesis 1A. The sample included all participants with T1 ASD diagnosis, and the outcome was a diagnosis of ASD (ASD- ASD) or non-ASD (ASD-NON). Predictors were selected based on correlations (see Table 5) and contingency coefficients (see Table 6) between each variable and the outcome (T2 Non-ASD status).
Based on these correlations and contingency coefficients, the initial model included ADOS CSS (Pearson’s r=-.225, p<.001), MSEL Expressive Language (Pearson’s r=.147, p=.018), MSEL Receptive Language (Pearson’s r=.207, p=.001), MSEL Visual Reception (Pearson’s r=.189, p=.002), VABS(-II) Communication (Pearson’s r=.145, p=.017), VABS(-II) Socialization (Pearson’s r=.177, p=.004), and number of intervention hours (Pearson’s r=-.141, p=.023). Two possible final models resulted; one included ADOS CSS, number of intervention hours, and MSEL receptive language, whereas the other model included ADOS CSS, number of intervention hours, and MSEL visual reception. MSEL receptive language and MSEL visual reception were significantly correlated (Pearson’s r=.553, p<.001) and had
significant overlapping variance, and both models had similar odds ratios and p-values for all three variables. Therefore, the model using MSEL receptive language (p=.025) was selected as the final model, as it had a lower p-value compared to MSEL visual reception (p=.032). However, it is important to note that either variable was significant in the model without affecting the odds ratios and significance of the other variables.
Therefore, the final model included ADOS CSS, MSEL Receptive Language, and number of intervention hours (see Table 7). In the sample of 253 participants, ADOS CSS was predictive of the outcome such that higher T1 ADOS CSS led to lower odds of transitioning from ASD to non-ASD, OR=.816, p=.014. Receptive language ability was predictive of diagnostic transition; higher T1 MSEL receptive language DQ score was associated with greater likelihood of transitioning, OR=1.016, p=.025. Finally, hours of intervention received predicted diagnostic transition, as greater participation in intervention was associated with reduced odds of changing from ASD to non-ASD, OR=.949, p=.032.The other cognitive and language measures were not predictive of diagnostic change, ps>.05.
Several mechanisms were explored which could correspond with the finding that more hours of participation in intervention were associated with smaller likelihood of transitioning from ASD to non- ASD. To test the hypothesis that those who participate in these services had more severe symptoms and were thus less likely to transition, the correlation between intervention hours and ADOS CSS was examined. Number of intervention hours and ASD symptom severity were not significantly correlated, Pearson’s r=.075, p=.228. Furthermore, maternal education did not predict number of intervention hours a participant engaged in, B=1.661, p=.164. However, differences in participation in intervention between the ASD-ASD and ASD-NON groups were examined at each of four time points between evaluations (see Figure 3); they were found to be initially similar (t(65.23)=-.996, p=.323), but diverged over time and were significantly different as children approached T2, t(92.56)=-2.905, p=.005. Change in intervention from two to 3.5 years also differed by group, B=-3.002, p=.009.
3.1.2 Hypothesis 1B: ASD to GDD
For hypothesis 1B, the logistic regression model included all participants who had ASD at T1 and a T2 diagnosis of GDD (ASD-GDD) or ASD (ASD-ASD). Based on correlations (see Table 8) and contingency coefficients (see Table 6), the original model included ADOS CSS (Pearson’s r=-.193, p=.003), VABS(-II) socialization skills (Pearson’s r=.153, p=.019), intervention hours (Pearson’s r=-.183, p=.006), maternal education (Phi=-.159, p=.019), and race (Phi=.213, p=.001) as predictors; the final model included all of these variables except VABS(-II) socialization skills (see Table 9). In the sample of 208 participants, ADOS CSS was a significant predictor, as higher ADOS CSS contributed to greater odds of diagnostic transition from ASD to GDD, OR=.658, p=.005. Maternal education was a significant predictor; having a parent who attended college and/or graduate school led to lower odds of changing from ASD to GDD, OR=.291, p=.034. Finally, number of hours of intervention was a significant predictor, as more hours of
intervention led to smaller likelihood of changing diagnosis, OR=.851, p=.039. Race was not a significant predictor, though its p-value approached significance; belonging to a minority race was associated with greater odds of transitioning from ASD to GDD, OR=3.342, p=.051. Post-hoc analysis revealed that maternal education level was not significantly correlated with race (Phi= -.081, p=.230). Also, group differences in change in participation in intervention over time (see Figure 4) were not significant, B=- 2.85, p=.159, though comparisons between ASD-ASD and ASD-GDD groups at each time point had moderate to high effect sizes (age two Cohen’s d=-.54, age 2.5 Cohen’s d=-.61, age three Cohen’s d=-.78, age 3.5 Cohen’s d=-.72).
3.1.3 Hypothesis 2A: non-ASD to ASD
For hypothesis 2A, the logistic regression model included all participants who did not have ASD at T1, and the outcome was a diagnosis of ASD (NON-ASD) or non-ASD (NON-NON). Based on
correlations (see Table 10) and contingency coefficients (see Table 6), ADOS CSS (Pearson’s r=.165, p=.041) and hours of intervention (Pearson’s r=.193, p=.018) were included in the model. Both variables
were significant predictors of transitioning from non-ASD to ASD, and were included in the final model (see Table 11). In particular, T1 ASD CSS led to greater odds of transitioning, OR=1.397, p=.022, and more participation in intervention contributed to greater odds of transitioning from non-ASD to ASD, OR=1.072, p=.030. At age two, using the Mann Whitney U test, the NON-NON and NON-ASD groups participated in a similarly low number of intervention hours (p=.898), though their participation diverged by age 3.5 (p=.065, Cohen’s d=-.51; see Figure 5).
3.1.4 Hypothesis 2B: GDD to ASD
Hypothesis 2B, comparing children who changed from a GDD to an ASD diagnosis to those who maintained GDD diagnosis, was examined qualitatively due to small sample sizes. See Table 3 for demographic characteristics of GDD-GDD and GDD-ASD groups. As expected, the GDD-GDD group generally had lower ADOS CSS scores at both T1 and T2 than the GDD-ASD group (see Figure 6).
Similarly, ADOS CSS in the GDD-GDD group tended to stay consistent or decrease over time, in contrast with the majority of the GDD-ASD group who experienced an increase in ADOS CSS (see Figure 7). In terms of T1 language, communication, and social skills (see Figures 8-11), the GDD-ASD group appeared to have fewer individuals with high receptive language ability (see Figure 8). In contrast, there were no apparent differences between groups in T1 expressive language ability (see Figure 9) or on a T1 measure of parent reported communication ability (see Figure 10). Finally, a measure of nonverbal cognitive ability also did not appear to distinguish between the GDD-ASD and GDD-GDD groups (see Figure 12).
Post-hoc analyses were used to explore possible differences between the group that maintained GDD diagnosis (N=32) and the group that changed from GDD to non-GDD (N=28), as the diagnosis was only 53.3 percent stable. Those who maintained GDD diagnosis did not differ from those who no longer met GDD criteria in terms of T1 nonverbal problem-solving ability (MSEL Visual Reception;
receptive language ability (MSEL Receptive Language; t(51.17)=.005, p=.996), or fine motor ability (MSEL Fine Motor; t(53.58)=1.886, p=.065, Cohen’s d=.498).