Background

2

Diabetes

stroke and hypertension are all common diseases

and they are known to be associated with each other. People with hypertension seem to be at a higher risk at diabetes and stroke. Previous studies have used Cox proportional hazard models to analyze the association between repeated measures and each disease independently. However

previous studies fail to take into account on repeated measurements of blood pressure and time-to-event data for two diseases into one model to analyze their association. Also

separate analyses would cause bias and invalid inference on parameter estimates

since those previous analyses fail to capture the latent relationship among clinical outcomes.

Methods

2

We used the joint model to evaluate the association between diabetes

stroke and hypertension on 5

000 UK Biobank participants. In joint model

we could link the longitudinal and time-to-event outcome through the random effects. We used linear mixed effect model and Cox proportional hazards model to do the model diagnostics for survival part and longitudinal part separately.

Results

2

In the longitudinal sub-model

age at visit

body mass index (BMI)

sex

ethnicity were all statistically significant (p<0.05). In the competing risks sub-model

BMI

sex and ethnicity were significantly associated with diabetes (p<0.0001)

but not with stroke. Our results indicated that higher BMI and increased age were associated with an increased risk at diabetes

as well as a higher blood pressure. Male received higher risk at diabetes than female (p<0.0001). Non-white population received the higher risk than white. Discussion and Conclusion: Higher blood pressure contributed to higher risk at diabetes

but we didn't find significant evidence that higher blood pressure had an impact on stroke. One possible reason is that the participants had much lower occurrence rate at stroke. In the longitudinal data diagnostics

we found that the assumption of normal distribution of random effect may not be appropriate. In the survival data diagnostics

the Cox-Snell plot showed that Cox PH model is a good fit for modeling stroke

but not for diabetes. Therefore

further research on model diagnostics of joint model is necessary to take into account the latent association.