OBJECTIVE: Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting. Therefore, to complement existing approaches, we set out here to design a longitudinal multivariate analytical framework that accepts as input an entire clinical trial database, comprising all measurements, patients, and time points across multiple trials. METHODS: Our framework composes probabilistic principal component analysis with a longitudinal linear mixed effects model, thereby enabling clinical interpretation of multivariate results, while handling data missing at random, and incorporating covariates and covariance structure in a computationally efficient and principled way. RESULTS: We illustrate our approach by applying it to four phase III clinical trials of secukinumab in Psoriatic Arthritis (PsA) and Rheumatoid Arthritis (RA). We identify three clinically plausible latent factors that collectively explain 74.5% of empirical variation in the longitudinal patient database. We estimate longitudinal trajectories of these factors, thereby enabling joint characterisation of disease progression and drug effect. We perform benchmarking experiments demonstrating our method's competitive performance at estimating average treatment effects compared to existing statistical and machine learning methods, and showing that our modular approach leads to relatively computationally efficient model fitting. CONCLUSION: Our multivariate longitudinal framework has the potential to illuminate the properties of existing composite endpoint methods, and to enable the development of novel clinical endpoints that provide enhanced and complementary perspectives on treatment response.

In the past few years a growing number of cities have started monitoring the position of public transportation vehicles using GPS devices. In this paper, we focus on a particularly important urban dataset: GPS bus data. Buses are valuable sensors and information associated with buses can provide unprecedented insight into many different aspects of city's life, from human behavior to mobility patterns. But analyzing these large urban datasets presents many challenges. Urban datasets are complex, containing location and temporal components in addition that they are commonly released in their raw format. Furthermore, urban datasets may have noisy and missing data, locations gathered in a low sampling rate and not mapped to the underlying road network, among other issues which makes it difficult for citizens, administrators and developers to get insights. In this paper, we present a system, called USapiens, for analyzing large urban trajectory data. We first describe the architecture of the proposed system for pre-processing and analyzing urban trajectory data. We then detail five use cases we build using very large GPS dataset obtained from buses operating in the city of Rio de Janeiro to get insights into various aspects of public transportation in the city.

Many factors can affect the predictability of public bus services such as traffic, weather, day of week, and hour of day. However, the exact nature of such relationships between travel times and predictor variables is, in most situations, not known. In this paper we develop a framework that allows for flexible modeling of bus travel times through the use of Additive Models. The proposed class of models provides a principled statistical framework that is highly flexible in terms of model building. The experimental results demonstrate uniformly superior performance of our best model as compared to previous prediction methods when applied to a very large GPS data set obtained from buses operating in the city of Rio de Janeiro.