[Forum SIS] : Avviso di Seminario Prof. Jeanine-Houwing Duistermaat at SAPIENZA

[Francesca Martella]

*Seminar- Thursday 14 June at 11.30, room 34, IVth floor, Dipartimento di
Scienze Statistiche*

Sapienza Università di Roma

P.le Aldo Moro, 5 - 00185 Rome (ITALY)


*Title: Statistical challenges in functional data analysis.*

Jeanine-Houwing Duistermaat and Haiyan Liu

from Department of Statistics, University of Leeds

*Abstract: *

The current increase of the availability of temporal datasets provides many
opportunities for methods development. Examples are integration and joint
analysis of multiple temporal datasets and modelling of sparse and
irregular data from Electronic Health Records (EHR). One of our motivating
studies aims to build a prediction tool for disease progress of Scleroderma
using data from EHRs. Scleroderma is a rare, clinically heterogeneous
multisystem disorder which greatly affects patients’ physical and
psychological functioning. Since only 15% of the patients show progress of
the disease, prediction of  progression is important for clinicians and
patients to decide on follow up and treatment strategies. One of the
outcomes of progression of the disease is drop in DLCO which is an index of
lung function capacity. In our dataset, we have DLCO measurements for 152
patients with 2 to 7 visits over 60 months. DLCO measurements appear to
change continually over time, hence they are (sparse) functional data. In
addition to the historical DLCO measurements, we have access to
measurements for four biomarkers. Our aim is to predict Scleroderma disease
progress based on patient’s historical data together with the information
of all other patients, and biomarkers. Here the methodological challenges
are sparsity and irregularity of the data.

To address these challenges, we propose a functional principal component
analysis method and scalar-on-function regression method. The restricted
maximum likelihood method is employed to estimate the eigenelements of
underlying covariance function and scores are estimated through conditional
expectation method. Then the DLCO trajectories are recovered by using the
truncated Karhunen-Loeve decomposition based on the estimated eigenelements
and scores. Similar FPCA procedure is also applied to predict a patient’s
last visit DLCO value by borrowing the information of all the other
patients and its own history (with the last visit DLCO value being
removed).

We will present our methods, results of the data analysis and discuss
future challenges for modelling temporal datasets.

-- 
Francesca Martella, PhD
Assistant Professor, Dipartimento di Scienze Statistiche
Facoltà di Ingegneria dell'Informazione, Informatica e Statistica
Sapienza Università di Roma
Piazzale Aldo Moro, 5 - 00185 Rome (ITALY)
Tel +39-06-49910464

-- 
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