title: Survival analysis with functional covariates for partial follow-up studies.
abstract: Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of the actual counts, that affect patient's disease-free survival time.
authors.affiliation: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC, USA.
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Fang
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA tongtong_wu@urmc.rochester.edu.
authors.forename: Tong Tong
authors.initials: TT
authors.lastname: Wu
authors.affiliation: Marlene and Stewart Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.
authors.forename: Aaron P
authors.initials: AP
authors.lastname: Rapoport
authors.affiliation: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Tan
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D018655:Lymphocyte Count; D008297:Male; D008875:Middle Aged; D009101:Multiple Myeloma / Q000401:mortality* / Q000473:pathology / Q000628:therapy; D025341:Principal Component Analysis; D011379:Prognosis; D016016:Proportional Hazards Models; D033581:Stem Cell Transplantation; D013601:T-Lymphocytes / Q000637:transplantation; D013997:Time Factors
keywords: Cox model; absolute lymphocyte cell counts; advanced myeloma; eigenfunction; functional principal components analysis; longitudinal studies; transplantation