DYNAMIC PREDICTION OF SURVIVAL DATA USING SINGLE OR MULTIPLE LONGITUDINAL MARKERS

Recurrent events and time-to-event data occur frequently in longitudinal studies. In large clinical trials with survival endpoints, researchers collect a multitude of longitudinal markers. There is a growing need to utilize these rich longitudinal information to build prediction models and assess their prognostic performance. In this dissertation research, I propose a novel approach of integrating longitudinal markers in modeling the recurrent event or terminal event data, and conduct dynamic prediction of event risks. Under joint a model framework, I jointly model a longitudinal outcome and a recurrent event process with the two process correlated via shared latent function. The probability of having a new occurrence of recurrent event in a given time interval is predicted based on subject-specific longitudinal profile and disease history. When multivariate longitudinal outcomes are considered, traditional joint model method has limitation on specifying ap propriate longitudinal structures and computation problem occur when using Bayesian approach. To avoid these potential issues, I employ multivariate functional principal component analysis approach which is more flexible, robust and time efficient. For terminal event data, I specify a prognostic model incorporating multivariate longitudinal information, the prediction can be updated with accumulated data over time. I also propose a recurrent event model integrating multiple longitudinal markers and conduct personalized dynamic prediction of new recurrent event risk, which helps physicians to identify patients at risk and give personalized health care

DYNAMIC PREDICTION OF SURVIVAL DATA USING SINGLE OR MULTIPLE LONGITUDINAL MARKERS

Recurrent events and time-to-event data occur frequently in longitudinal studies. In large clinical trials with survival endpoints, researchers collect a multitude of longitudinal markers. There is a growing need to utilize these rich longitudinal information to build prediction models and assess their prognostic performance. In this dissertation research, I propose a novel approach of integrating longitudinal markers in modeling the recurrent event or terminal event data, and conduct dynamic prediction of event risks. Under joint a model framework, I jointly model a longitudinal outcome and a recurrent event process with the two process correlated via shared latent function. The probability of having a new occurrence of recurrent event in a given time interval is predicted based on subject-specific longitudinal profile and disease history. When multivariate longitudinal outcomes are considered, traditional joint model method has limitation on specifying ap propriate longitudinal structures and computation problem occur when using Bayesian approach. To avoid these potential issues, I employ multivariate functional principal component analysis approach which is more flexible, robust and time efficient. For terminal event data, I specify a prognostic model incorporating multivariate longitudinal information, the prediction can be updated with accumulated data over time. I also propose a recurrent event model integrating multiple longitudinal markers and conduct personalized dynamic prediction of new recurrent event risk, which helps physicians to identify patients at risk and give personalized health care

Validation of the Polish version of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)

Background. In 2008, the Movement Disorders Society (MDS) published a new Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) as the official benchmark scale for Parkinson’s Disease (PD). We have translated and validated the Polish version of the MDS-UPDRS, explored its dimensionality, and compared it to the original English one. Methods. The MDS-UPDRS was translated into Polish by a team of Polish investigators led by JS and GO. The back-translation was completed by colleagues fluent in both languages (Polish and English) who were not involved in the original translation, and was reviewed by members of the MDS Rating Scales Programme. Then the translated version of the MDS-UPDRS underwent cognitive pretesting, and the translation was modified based on the results. The final translation was approved as the Official Working Document of the MDS-UPDRS Polish version, and was tested on 355 Polish PD patients recruited at movement disorders centres all over Poland (at Katowice, Gdańsk, Łódź, Warsaw, Wrocław, and Kraków). Confirmatory and explanatory factor analyses were applied to determine whether the factor structure of the English version could be confirmed in the Polish version. Results. The Polish version of the MDS-UPDRS showed satisfactory clinimetric properties. The internal consistency of the Polish version was satisfactory. In the confirmatory factor analysis, all four parts had greater than 0.90 comparative fit index (CFI) compared to the original English MDS-UPDRS. Explanatory factor analysis suggested that the Polish version differed from the English version only within an acceptable range. Conclusions and clinical implications. The Polish version of the MDS-UPDRS meets the requirements to be designated as the Official Polish Version of the MDS-UPDRS, and is available on the MDS web page. We strongly recommend using the MDS-UPDRS instead of the UPDRS for research purposes and in everyday clinical practice.

First Isolation of New Canine Parvovirus 2a from Tibetan Mastiff and Global Analysis of the Full-Length VP2 Gene of Canine Parvoviruses 2 in China

Canine parvovirus 2 (CPV-2) was first identified in 1978, and is responsible for classic parvoviral enteritis. Despite the widespread vaccination of domestic carnivores, CPVs have remained important pathogens of domestic and wild carnivores. In this study, we isolated CPV-2 from Tibetan mastiffs and performed a global analysis of the complete VP2 gene sequences of CPV-2 strains in China. Six isolates were typed as new CPV-2a, according to key amino acid positions. On a phylogenetic tree, these six sequences formed a distinct clade. Five isolates occurred on the same branch as KF785794 from China and GQ379049 from Thailand; CPV-LS-ZA1 formed a separate subgroup with FJ435347 from China. One hundred ninety-eight sequences from various parts of China and the six sequences isolated here formed seven distinct clusters, indicating the high diversity of CPVs in China. Of 204 VP2 sequences, 183 (91.04%) encoded the mutation Ser297Ala, regardless of the antigenic type, implying that most Chinese CPV-2 strains contain the VP2 mutation Ser297Ala. However, the biological significance of this change from prototype CPV-2a/2b to new CPV-2a/2b types remains unclear. This study is the first to isolate new CPV-2a from the Tibetan mastiff. Our data show that new CPV-2a/2b variants are now circulating in China