Spatial survival modelling of business re-opening after Katrina: Survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months

2020 ◽  
pp. 1471082X2096715
Author(s):  
Roger S. Bivand ◽  
Virgilio Gómez-Rubio
Keyword(s):  
Hurricane Katrina ◽  
Survival Data ◽  
R Package ◽  
American Statistical Association ◽  
Survival Models ◽  
Royal Statistical Society ◽  
Business Decisions ◽  
Modeling And Analysis ◽  
Probit Models ◽  
Spatial Probit

Zhou and Hanson; Zhou and Hanson; Zhou and Hanson ( 2015 , Nonparametric Bayesian Inference in Biostatistics, pages 215–46. Cham: Springer; 2018, Journal of the American Statistical Association, 113, 571–81; 2020, spBayesSurv: Bayesian Modeling and Analysis of Spatially Correlated Survival Data. R package version 1.1.4) and Zhou et al. (2020, Journal of Statistical Software, Articles, 92, 1–33) present methods for estimating spatial survival models using areal data. This article applies their methods to a dataset recording New Orleans business decisions to re-open after Hurricane Katrina; the data were included in LeSage et al. (2011b , Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27). In two articles ( LeSage etal., 2011a , Significance, 8, 160—63; 2011b, Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27), spatial probit models are used to model spatial dependence in this dataset, with decisions to re-open aggregated to the first 90, 180 and 360 days. We re-cast the problem as one of examining the time-to-event records in the data, right-censored as observations ceased before 175 businesses had re-opened; we omit businesses already re-opened when observations began on Day 41. We are interested in checking whether the conclusions about the covariates using aspatial and spatial probit models are modified when applying survival and spatial survival models estimated using MCMC and INLA. In general, we find that the same covariates are associated with re-opening decisions in both modelling approaches. We do however find that data collected from three streets differ substantially, and that the streets are probably better handled separately or that the street effect should be included explicitly.

scholarly journals Sparse network-based regularization for the analysis of patientomics high-dimensional survival data

2018 ◽  
Author(s):  
André Veríssimo ◽  
Eunice Carrasquinha ◽  
Marta B. Lopes ◽  
Arlindo L. Oliveira ◽  
Marie-France Sagot ◽  
...  
Keyword(s):  
Survival Data ◽  
Linear Models ◽  
Predictive Performance ◽  
R Package ◽  
Molecular Data ◽  
Data Availability ◽  
Survival Models ◽  
Centrality Measures ◽  
Flexible Tool ◽  
Sparse Network

AbstractData availability by modern sequencing technologies represents a major challenge in oncological survival analysis, as the increasing amount of molecular data hampers the generation of models that are both accurate and interpretable. To tackle this problem, this work evaluates the introduction of graph centrality measures in classical sparse survival models such as the elastic net.We explore the use of network information as part of the regularization applied to the inverse problem, obtained both by external knowledge on the features evaluated and the data themselves. A sparse solution is obtained either promoting features that are isolated from the network or, alternatively, hubs, i.e., features that are highly connected within the network.We show that introducing the degree information of the features when inferring survival models consistently improves the model predictive performance in breast invasive carcinoma (BRCA) transcriptomic TCGA data while enhancing model interpretability. Preliminary clinical validation is performed using the Cancer Hallmarks Analytics Tool API and the String database.These case studies are included in the recently released glmSparseNet R package1, a flexible tool to explore the potential of sparse network-based regularizers in generalized linear models for the analysis of omics data.

IPDfromKM: Reconstruct Individual Patient Data from Published Kaplan-Meier Survival Curves

2020 ◽  
Author(s):  
Na Liu ◽  
Yanhong Zhou ◽  
J. Jack Lee
Keyword(s):  
Survival Data ◽  
Individual Patient Data ◽  
Secondary Analysis ◽  
R Package ◽  
Patient Data ◽  
Survival Curves ◽  
Raw Data ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Shiny Application

Abstract BackgroundWhen applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to the IPD. We aim to propose a straightforward and robust approach to help researchers to obtain IPD from published survival curves with a friendly software platform. ResultsImproving upon the existing methods, we proposed an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we develop the R package IPDfromKM and an accompanied web-based Shiny application. Both the R package and Shiny application can be used to extract raw data coordinates from published K-M curves, reconstruct IPD from data coordinates extracted, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, as well as hazard ratios. ConclusionsIPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD data and advance the use of secondary data to make informed decision in medical research.

Probit or Logit? Which is the better model to predict the longevity of seeds?

2020 ◽  
Vol 30 (1) ◽  
pp. 49-58
Author(s):  
Rute Q. de Faria ◽  
Amanda R. P. dos Santos ◽  
Deoclecio J. Amorim ◽  
Renato F. Cantão ◽  
Edvaldo A. A. da Silva ◽  
...  
Keyword(s):  
Survival Data ◽  
Logit Model ◽  
Probit Model ◽  
Estimation Procedure ◽  
Logistic Distribution ◽  
Logit Models ◽  
The Central Limit Theorem ◽  
Probit Models ◽  
Logit Function ◽  
Logit And Probit Models

AbstractThe prediction of seed longevity (P50) is traditionally performed by the use of the Probit model. However, due to the fact that the survival data are of binary origin (0,1), the fit of the model can be compromised by the non-normality of the residues. Consequently, this leads to prediction losses, despite the data being partially smoothed by Probit and Logit models. A possibility to reduce the effect of non-normality of the data would be to apply the principles of the central limit theorem, which states that non-normal residues tend to be normal as the n sample is increased. The Logit and Probit models differ in their normal and logistic distribution. Therefore, we developed a new estimation procedure by using a small increase of the n sample and tested it in the Probit and Logit functions to improve the prediction of P50. The results showed that the calculation of P50 by increasing the n samples from 4 to 6 replicates improved the index of correctness of the prediction. The Logit model presented better performance when compared with the Probit model, indicating that the estimation of P50 is more adequate when the adjustment of the data is performed by the Logit function.

scholarly journals Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application

2019 ◽  
Vol 19 (4) ◽  
pp. 931-949 ◽  
Author(s):  
Michael J. Crowther
Keyword(s):  
Random Effects ◽  
Survival Data ◽  
Relative Survival ◽  
Survival Models ◽  
Left Truncation ◽  
Delayed Entry ◽  
Working Paper ◽  
Expected Mortality ◽  
Parametric Survival ◽  
Parametric Survival Analysis

In this article, I present the community-contributed stmixed command for fitting multilevel survival models. It serves as both an alternative to Stata’s official mestreg command and a complimentary command with substantial extensions. stmixed can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston–Parmar and the log-hazard equivalent) and user-defined hazard models. Simple or complex time-dependent effects can be included, as can expected mortality for a relative survival model. Left-truncation (delayed entry) is supported, and t-distributed random effects are provided as an alternative to Gaussian random effects. I illustrate the methods with a commonly used dataset of patients with kidney disease suffering recurrent infections and a simulated example illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, Stata Journal 12: 674–687; 2013, Statistics in Medicine 32: 4118–4134). stmixed is part of the merlin family (Crowther 2017, arXiv Working Paper No. arXiv:1710.02223; 2018, arXiv Working Paper No. arXiv:1806.01615).

scholarly journals ANALISIS SINTASAN PARAMETRIK PADA PASIEN STROKE DENGAN PENDEKATAN DISTRIBUSI WEIBULL

2019 ◽  
Vol 8 (1) ◽  
pp. 55
Author(s):  
NI MADE SRI WAHYUNI ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Body Mass Index ◽  
Survival Analysis ◽  
Weibull Distribution ◽  
Survival Data ◽  
The Body ◽  
Predictor Variables ◽  
Survival Models ◽  
Stroke Patients ◽  
Parametric Survival ◽  
Parametric Survival Analysis

Parametric survival analysis is one of the survival analysis that has a distribution of survival data that follows a certain distribution. Weibull distribution is a distribution that is often used in parametric survival analysis. The purpose of this study is to determine parametric survival models using the Weibull distribution and to determine  the factors that can influence the recovery of stroke patients. This study uses data on stroke patients in the Wangaya hospital, Denpasar in 2017. The best model obtained in this study is a model that consists of two predictor variables, namely the age and the body mass index (BMI).Therefore the  factors that can influence the recovery of stroke patients are age and BMI.

Modelling heterogeneity in survival data

1991 ◽  
Vol 28 (03) ◽  
pp. 695-701 ◽  
Author(s):  
Philip Hougaard
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Mixture Distribution ◽  
Survival Models ◽  
Risk Of Death ◽  
Hazards Model ◽  
Occupational Mortality ◽  
Heterogeneity Distribution ◽  
Biased Estimates

Ordinary survival models implicitly assume that all individuals in a group have the same risk of death. It may, however, be relevant to consider the group as heterogeneous, i.e. a mixture of individuals with different risks. For example, after an operation each individual may have constant hazard of death. If risk factors are not included, the group shows decreasing hazard. This offers two fundamentally different interpretations of the same data. For instance, Weibull distributions with shape parameter less than 1 can be generated as mixtures of constant individual hazards. In a proportional hazards model, neglect of a subset of the important covariates leads to biased estimates of the other regression coefficients. Different choices of distributions for the unobserved covariates are discussed, including binary, gamma, inverse Gaussian and positive stable distributions, which show both qualitative and quantitative differences. For instance, the heterogeneity distribution can be either identifiable or unidentifiable. Both mathematical and interpretational consequences of the choice of distribution are considered. Heterogeneity can be evaluated by the variance of the logarithm of the mixture distribution. Examples include occupational mortality, myocardial infarction and diabetes.

scholarly journals BayesMFSurv: An R Package to Estimate Bayesian Split-Population Survival Models With (and Without) Misclassified Failure Events

2020 ◽  
Vol 5 (48) ◽  
pp. 2164
Author(s):  
Minnie Joo ◽  
Nicolás Schmidt ◽  
Sergio Béjar ◽  
Vineeta Yadav ◽  
Bumba Mukherjee
Keyword(s):  
R Package ◽  
Survival Models ◽  
Population Survival
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