@article {791, title = {Predicting human movement with multiple accelerometers using movelets.}, journal = {Med Sci Sports Exerc}, volume = {46}, year = {2014}, month = {2014 Sep}, pages = {1859-66}, abstract = {

PURPOSE: The study aims were 1) to develop transparent algorithms that use short segments of training data for predicting activity types and 2) to compare the prediction performance of the proposed algorithms using single accelerometers and multiple accelerometers.

METHODS: Sixteen participants (age, 80.6 yr (4.8 yr); body mass index, 26.1 kg{\textperiodcentered}m (2.5 kg{\textperiodcentered}m)) performed 15 lifestyle activities in the laboratory, each wearing three accelerometers at the right hip and left and right wrists. Triaxial accelerometry data were collected at 80 Hz using ActiGraph GT3X+. Prediction algorithms were developed, which, instead of extracting features, build activity-specific dictionaries composed of short signal segments called movelets. Three alternative approaches were proposed to integrate the information from the multiple accelerometers.

RESULTS: With at most several seconds of training data per activity, the prediction accuracy at the second-level temporal resolution was very high for lying, standing, normal/fast walking, and standing up from a chair (the median prediction accuracy ranged from 88.2\% to 99.9\% on the basis of the single-accelerometer movelet approach). For these activities, wrist-worn accelerometers performed almost as well as hip-worn accelerometers (the median difference in accuracy between wrist and hip ranged from -2.7\% to 5.8\%). Modest improvements in prediction accuracy were achieved by integrating information from multiple accelerometers.

DISCUSSION AND CONCLUSIONS: It is possible to achieve high prediction accuracy at the second-level temporal resolution with very limited training data. To increase prediction accuracy from the simultaneous use of multiple accelerometers, a careful selection of integrative approaches is required.

}, issn = {1530-0315}, doi = {10.1249/MSS.0000000000000285}, author = {He, Bing and Bai, Jiawei and Zipunnikov, Vadim V and Koster, Annemarie and Caserotti, Paolo and Lange-Maia, Brittney and Glynn, Nancy W and Harris, Tamara B and Crainiceanu, Ciprian M} } @article {821, title = {Practical Marginalized Multilevel Models.}, journal = {Stat}, volume = {2}, year = {2013}, month = {2013}, abstract = {

Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster-dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and association aspects. We then formulate marginal models through conditional specifications to facilitate estimation with mixed model computational solutions already in place. We illustrate the MMM and approximate MMM approaches on a cerebrovascular deficiency crossover trial using SAS and an epidemiological study on race and visual impairment using R. Datasets, SAS and R code are included as supplemental materials.

}, issn = {0038-9986}, doi = {10.1002/sta4.22}, author = {Griswold, Michael E and Swihart, Bruce J and Caffo, Brian S and Zeger, Scott L} } @article {805, title = {Practical recommendations for population PK studies with sampling time errors.}, journal = {Eur J Clin Pharmacol}, volume = {69}, year = {2013}, month = {2013 Dec}, pages = {2055-64}, abstract = {

PURPOSE: Population pharmacokinetic (PK) data collected from routine clinical practice offers a rich source of valuable information. However, in observational population PK data, accurate time information for blood samples is often missing, resulting in measurement errors (ME) in the sampling time variable. The goal of this study was to investigate the effects on model parameters when a scheduled time is used instead of the actual blood sampling time, and to propose ME correction methods.

METHODS: Simulation studies were conducted based on two major factors: the curvature in PK profiles and the size of ME. As ME correction methods, transform both sides (TBS) models were developed with application of Box-Cox power transformation and Taylor expansion. The TBS models were compared to a conventional population PK model using simulations.

RESULTS: The most important determinant of bias due to time ME was the degree of curvature (nonlinearity) in PK profiles; the smaller the curvature around sampling times, the smaller the associated bias. The second important determinant was the magnitude of ME; the larger the ME, the larger the bias. The proposed TBS models performed better than a conventional population PK modeling when curvature and ME were substantial.

CONCLUSIONS: Time ME in sampling time can lead to bias on the parameter estimators. The following practical recommendations are provided: 1) when the curvature of PK profiles is small, conventional population PK modeling is robust to even large ME; and 2) when the curvature is moderate or large, the proposed methodology reduces bias in parameter estimates.

}, keywords = {Humans, Models, Biological, Pharmacokinetics, Research Design, Time Factors}, issn = {1432-1041}, doi = {10.1007/s00228-013-1576-7}, author = {Choi, Leena and Crainiceanu, Ciprian M and Caffo, Brian S} } @article {reiss2012paradox, title = {Paradoxical Results of Adaptive False Discovery Rate Procedures in Neuroimaging Studies}, journal = {NeuroImage}, year = {2012}, note = {To appear}, author = {Philip T. Reiss, Armin Schwartzman, Feihan Lu, Lei Huang, and Erika Proal} } @article {goldsmithNI, title = {Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis}, journal = {Accepted in Neuroimage, PMCID: PMC3114268}, year = {2012}, author = {Goldsmith, J and Crainiceanu, C and Caffo, B and Reich, D.} } @article {pmid22442041, title = {Predicting Breakdown of the Blood-Brain Barrier in Multiple Sclerosis without Contrast Agents}, journal = {AJNR Am J Neuroradiol}, year = {2012}, month = {Mar}, author = {Shinohara, R and Goldsmith, J and Mateen, F J and Crainiceanu, C and Reich, D S} } @article {doi:10.1198/jcgs.2010.10007, title = {Penalized Functional Regression}, journal = {Journal of Computational and Graphical Statistics}, volume = {20}, number = {4}, year = {2011}, pages = {830-851}, doi = {10.1198/jcgs.2010.10007}, url = {http://pubs.amstat.org/doi/abs/10.1198/jcgs.2010.10007}, author = {Goldsmith, J and Bobb, J. and Crainiceanu, C and Caffo, B and Reich, D.} } @article {pmid21554962, title = {Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis}, journal = {Neuroimage}, volume = {57}, number = {2}, year = {2011}, month = {Jul}, pages = {431{\textendash}439}, author = {Goldsmith, J and Crainiceanu, C and Caffo, B and Reich, D S} } @article {doi:10.1198/jasa.2011.ap10089, title = {Population value decomposition, a framework for the analysis of image populations}, journal = {Journal of the American Statistical Association, with discussion and rejoinder}, volume = {106}, number = {495}, year = {2011}, pages = {775-790}, doi = {10.1198/jasa.2011.ap10089}, url = {http://pubs.amstat.org/doi/abs/10.1198/jasa.2011.ap10089}, author = {Crainiceanu, C and Caffo, B and Luo, S and Zipunnikov, V. and Punjabi, N} } @article {takiPCA, title = {Population-wide principal component-based quantification of blood-brain-barrier dynamics in multiple sclerosis}, journal = {Neuroimage, PMCID: PMC3138825}, volume = {57}, number = {4}, year = {2011}, pages = {1430 {\textendash} 1446}, author = {Shinohara, R and Crainiceanu, C and Caffo, B and Gaitan, M and Reich, D.} } @article {nazarian2011AI, title = {Prospective study to assess the safety of a protocol for magnetic resonance imaging of patients with implanted cardiac devices}, journal = {Annals of Interal Medicine}, volume = {155}, number = {7}, year = {2011}, pages = {415{\textendash}424}, publisher = {American College of Physicians}, author = {Nazarian, S and Hansford, R and Roguin, A and Goldsher, D and Zviman, M and Lardo, A and Caffo, B and Frick, K and Kraut, M and Kamel, I and Ihab, R and Calkins, H and Berger, R and Bluemke, D and Halperin, H} } @article {segev2010perioperative, title = {Perioperative Mortality and Long-term Survival Following Live Kidney Donation}, journal = {Journal of the American Medical Association, NIHMSID: 316358}, volume = {303}, number = {10}, year = {2010}, pages = {959{\textendash}966}, publisher = {Am Med Assoc}, author = {Segev, D and Muzaale, A and Caffo, B and Mehta, S and Singer, A and Taranto, S and McBride, M and Montgomery, R} } @article {o2009prospective, title = {Prospective Study of Sleep-disordered Breathing and Hypertension: The Sleep Heart Health Study}, journal = {American Journal of Respiratory and Critical Care Medicine, PMCID: PMC2695498}, volume = {179}, number = {12}, year = {2009}, pages = {1159{\textendash}1164}, publisher = {Am Thoracic Soc}, author = {O{\textquoteright}Connor, G and Caffo, B and Newman, A and Quan, S and Rapoport, D and Redline, S and Resnick, H and Samet, J and Shahar, E} } @article {zhang2008power, title = {Power Spectral Analysis of EEG Activity During Sleep in Cigarette Smokers}, journal = {Chest}, volume = {133}, number = {2}, year = {2008}, pages = {427{\textendash}432}, publisher = {Am Coll Chest Phys}, author = {Zhang, L and Samet, J and Caffo, B and Bankman, I and Punjabi, N} } @article {Wager08, title = {Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation}, journal = {Neuron}, volume = {59}, year = {2008}, pages = {1037-1050}, author = {Wager, T.D. and Davidson, M.L. and Hughes, B.L. and Lindquist, Martin A and Ochsner, K.N.} } @article {streid2008, title = {On probability matching priors}, journal = {The Canadian J. of Statistics}, volume = {36}, year = {2008}, pages = {613{\textendash}622}, author = {Staicu, A. M. and Reid, N} } @article {Huys2008, title = {Psychiatry : insights into depression through normative decision-making models}, journal = {Control}, year = {2008}, pages = {1{\textendash}8}, author = {Huys, Quentin J M and Vogelstein, Joshua T and Dayan, Peter} } @article {li2007potential, title = {On the potential for ill logic with logically defined outcomes}, journal = {Biostatistics}, volume = {8}, number = {4}, year = {2007}, pages = {800}, author = {Li, X and Caffo, B and Scharfstein, D} } @article {pmid12941553, title = {Prevalence estimates for paratuberculosis adjusted for test variability using Bayesian analysis}, journal = {Prev. Vet. Med.}, volume = {60}, number = {4}, year = {2003}, month = {Sep}, pages = {281{\textendash}295}, author = {van Schaik, G. and Schukken, Y. H. and Crainiceanu, C and Muskens, J. and VanLeeuwen, J. A.} }