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A Non-parametric Bayesian Approach for Estimating Treatment-Response Curves from Sparse Time Series

Yanbo Xu, Yanxun Xu, Suchi Saria
2016 Machine Learning in Health Care  
On a challenging dataset containing time series from patients admitted to a hospital, we estimate treatment responses for treatments used in managing kidney function and show that the resulting fits are  ...  We study the problem of estimating the continuous response over time of interventions from observational time series-a retrospective dataset where the policy by which the data are generated are unknown  ...  Conclusion In this paper, we have developed a novel Bayesian nonparametric method for estimating treatment response curves from sparse observational time series.  ... 
dblp:conf/mlhc/XuXS16 fatcat:tcpet3opnfgf5edj3de225oohq

Errors-in-variables modeling of personalized treatment-response trajectories

Guangyi Zhang, Reza Ashrafi, Anne Juuti, Kirsi Pietilainen, Pekka Marttinen
2020 IEEE journal of biomedical and health informatics  
We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline  ...  However, methodology is lacking for the case when the response is a continuous temporal curve, treatment covariates suffer extensively from measurement error, and even the exact timing of the treatments  ...  This is particularly challenging when the response is a continuous curve, for example a time-series of a biological marker.  ... 
doi:10.1109/jbhi.2020.2987323 pmid:32324579 fatcat:eit5fbtgyfhoxh3zxxoffyj5qy

Bayesian models with a weakly informative prior: a useful alternative for solving sparse data problems

A.M. Soliman, L.RF Macehose, A. Carlson
2013 Value in Health  
The proposed method was used in the analysis of a comparative effectiveness (CE) study of ophthalmologic treatments for openangle glaucoma patients.  ...  The linear unbiased estimate of dependent variables were transformed to the original probability, sorted from least to largest, and divided into deciles.  ...  Use of a fully parametric model provides a more flexible approach and a better estimate of treatment effects.  ... 
doi:10.1016/j.jval.2013.03.272 fatcat:axsmc4cn7rcfdhpblqsvdkk3ti

Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions [article]

Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria
2017 arXiv   pre-print
Our proposed solution represents the treatment response curves using linear time-invariant dynamical systems---this provides a flexible means for modeling response over time to highly variable dose curves  ...  Treatment effects can be estimated from observational data as the difference in potential outcomes.  ...  Acknowledgements The authors would like to thank Yanbo Xu for valuable discussions.  ... 
arXiv:1704.02038v2 fatcat:xjih7dnxwnholmqlosyob7n5ki

The use of registry data to extrapolate overall survival results from randomised controlled trials [article]

Reynaldo Martina, Keith Abrams, Sylwia Bujkiewicz, David Jenkins, Pascale Dequen, Michael Lees, Frank A. Corvino, Jessica Davies
2019 arXiv   pre-print
We also explore a Bayesian model constrained to the long term estimate of survival reported in literature, a Bayesian power prior approach on the variability observed from published literature, and a Bayesian  ...  However, for regulatory and Health Technology Assessment (HTA) decision-making a longer time horizon is often required than is studied in RCTs.  ...  The work leading to these results has received support from the Innovative Medicines Initiative A.4 Statistical analysis Lifetable survival estimates were generated for the patient cohort by treatment  ... 
arXiv:1911.05691v1 fatcat:jc2lwydhlbaorpxizgsfd4iwti

How to infer gene networks from expression profiles, revisited

C. A. Penfold, D. L. Wild
2011 Interface Focus  
Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes.  ...  For smaller systems, DBNs are competitive with the non-parametric approaches with respect to computational time and accuracy, and both of these approaches appear to be more accurate than Granger causality-based  ...  We acknowledge support from grants BBRSC BB/F005806/1 (Plant Response to Environmental Stress in Arabidopsis; C.A.P. and D.L.W.) and EPSRC EP/ G021163/1 (Mathematics of Complexity Science and Systems Biology  ... 
doi:10.1098/rsfs.2011.0053 pmid:23226586 pmcid:PMC3262295 fatcat:glhjailtdngwzmi3zcnb4keo5y

Survival Time Analysis of Hypertension Patients Using Parametric Models

Markos Abiso Erango, Kabtamu Tolosie Gergiso, Sultan Hussen Hebo
2019 Advances in Research  
Bayesian estimation approach was smaller deviance information criteria as compare to classical estimation approaches for the current data set.  ...  Parametric distributions: Exponential, Weibull, Lognormal and loglogistic are studied to analysis survival probabilities of the patients in both Bayesian and classical approaches.  ...  health professionals, Addis Ababa Administration Health bureau Yekatit 12 Hospital Medical College medical director's office, Addis Ababa Administration Health bureau Yekatit 12 Hospital Medical College for  ... 
doi:10.9734/air/2019/v20i230155 fatcat:r7utt5rvdncnvivp4h3pxxk4va

A Sparse Spatial Linear Regression Model for fMRI Data Analysis [chapter]

Vangelis P. Oikonomou, Konstantinos Blekas
2010 Lecture Notes in Computer Science  
In the same time, sparse properties are also embedded through a RVM-based sparse prior over coefficients.  ...  The basic building block of our method is the general linear model (GML) that constitute a well-known probabilistic approach for regression.  ...  estimated and the true coefficients responsible for the BOLD signal.  ... 
doi:10.1007/978-3-642-12842-4_24 fatcat:wqcb7nruurcnlniyaf46gdtwaa

New Frontiers in Bayesian Modeling Using the INLA Package in R

Janet van Niekerk, Haakon Bakka, Håvard Rue, Olaf Schenk
2021 Journal of Statistical Software  
It is a non-sampling based framework which provides approximate results for Bayesian inference, using sparse matrices.  ...  The swift uptake of this framework for Bayesian modeling is rooted in the computational efficiency of the approach and catalyzed by the demand presented by the big data era.  ...  The Cox proportional hazards model is included as a semi-parametric model resulting from a non-parametric constant baseline hazard in each of many time partitions (see Cox 1972) .  ... 
doi:10.18637/jss.v100.i02 fatcat:ekkcsooncfaqfl5l23424tnb64

A new numerical method for processing longitudinal data: clinical applications

Ilaria Stura, Emma Perracchione, Giuseppe Migliaretti, Franco Cavallo
2018 Epidemiology Biostatistics and Public Health  
Results: Here, we propose an alternative approach to be used as effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data.  ...  For this problem, a large variety of well-known methods have already been developed.  ...  Using a non-parametric Empirical Bayes approach, our study analyzes the growth response to GH treatment in a homogeneous cohort of 317 patients with pituitary GH deficiency who were enrolled during their  ... 
doi:10.2427/12881 doaj:fe85db212be548f7a68fb1aa35639452 fatcat:7ekrwn4htreyhdb4fludl5eidm

A sequential distance-based approach for imputing missing data: Forward Imputation

Nadia Solaro, Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari
2016 Advances in Data Analysis and Classification  
non-linearity in a time series.  ...  The motivation is the Bayesian non-parametric modeling of the dependence between the clustering structures and the distributions of different time series.  ... 
doi:10.1007/s11634-016-0243-0 fatcat:yvrqlgllsbesbnvnzzci2egpl4

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

Claudia Schillings, Mikael Sunnåker, Jörg Stelling, Christoph Schwab, Costas D. Maranas
2015 PLoS Computational Biology  
Here, we propose a sparse, deterministic PLOS Computational Biology | adaptive interpolation method tailored to high-dimensional parametric problems that allows for fast, deterministic computational analysis  ...  Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it  ...  Discussion We propose a sparse, adaptive interpolation scheme for the efficient deterministic computational treatment of parametric uncertainty in complex, nonlinear systems.  ... 
doi:10.1371/journal.pcbi.1004457 pmid:26317784 pmcid:PMC4552555 fatcat:tuxhzopht5atfjwjb7c6anxmgy

Bayesian mixed treatment comparison (MTC): A novel method to demonstrate equivalence and non-inferiority

W.A. Malcolm, O.A. Uthman
2013 Value in Health  
CONCLUSIONS: This innovative method has the potential to improve understanding of equivalence (or non-inferiority) between drugs for multiple stake-holders.  ...  A 1 -A 2 9 8 that vildagliptin 50 mg bid and sitagliptin 100 mg qd are equivalent is 99.3%.  ...  CONCLUSIONS: Bayesian models with WIP represent a useful tool for modeling health outcomes sparse data with small sample size.  ... 
doi:10.1016/j.jval.2013.03.279 fatcat:pi5uzit3svclxgvziqduoa2zli

Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology inR

Brandon Whitcher, Volker J. Schmid
2011 Journal of Statistical Software  
A non-parametric model, using penalized splines, is also available to characterize the contrast agent concentration time curves.  ...  Kinetic parameters are obtained from nonlinear regression, Bayesian estimation via Markov chain Monte Carlo or Bayesian maximum a posteriori estimation.  ...  compartmental model, a parametric model may be applied to the estimated response curve (nlr = TRUE).  ... 
doi:10.18637/jss.v044.i05 fatcat:5b6ktkbcsrcmdc7zvs6pfsupri

A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves [article]

Yanbo Xu, Yanxun Xu, Suchi Saria
2016 arXiv   pre-print
On a challenging dataset containing time series from patients admitted to a hospital, we estimate responses to treatments used in managing kidney function and show that the resulting fits are more accurate  ...  We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown  ...  Conclusion In this paper, we have developed a novel Bayesian nonparametric method for estimating treatment response curves from sparse observational time series.  ... 
arXiv:1608.05182v2 fatcat:jqq2r2id6vc2bltyrqzwrdmara
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