A spatial scan statistic for survival data based on Weibull distribution

Bhatt, Vijaya Raj; Tiwari, Neeraj

doi:10.1002/sim.6075

Cited by 33 publications

(30 citation statements)

References 16 publications

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“…Although spatial scan statistics vary in terms of the shape of scanning window and the probability model, most of them employ the logarithm of the likelihood ratio (LLR) as the test statistic to identify maximum likelihood clusters[ 17 – 22 ]. A maximum likelihood estimation method is also applied to determine the most clustered sub-region Z .…”

Section: Methodsmentioning

confidence: 99%

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic

Yin

Zhang

et al. 2016

PLoS ONE

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Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set–proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

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Section: Methodsmentioning

confidence: 99%

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic

Yin

Zhang

et al. 2016

PLoS ONE

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“…Our approach was built into a general framework for use with various parametric models (Bernoulli, Gaussian, and Poisson models, etc.). With a Poisson model, it has been pointed out that the FMASSS is equivalent to Kulldorff's classical spatial scan statistic in an adjusted population [6]. This result is also valid for Jung's approach and considerably reduces the time need to compute Jung's spatial scan statistic for Poisson model.…”

Section: Discussionmentioning

confidence: 82%

“…Under the null hypothesis, the truncated log-likelihood function (6) associated with the Poisson model (11) is given by:…”

Section: Appendix a An Explicit Intercept Estimator In The Poisson Mmentioning

confidence: 99%

“…Following on from Kulldorff's initial work, several researchers have adapted spatial scan statistics to other spatial data distributions, such as ordinal, 3 normal, 4 exponential, 5 and Weibull model. 6 Spatial scan statistics have been extended to the multivariate framework by Kulldorff et al, 7 Neill et al, 8 and, most recently, Cucala et al 9,10 One of the main problems in cluster detection is the need to adjust for covariates. If a covariate is a confounding factor associated with the event of interest, and is not homogeneously distributed over a geographical area, a cluster analysis can generate clusters in which the covariate (and not the event of interest) predominates.…”

Section: Introductionmentioning

confidence: 99%

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A functional‐model‐adjusted spatial scan statistic

Ahmed

Génin

2020

Statistics in Medicine

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This paper introduces a new spatial scan statistic designed to adjust cluster detection for longitudinal confounding factors indexed in space. The functional‐model‐adjusted statistic was developed using generalized functional linear models in which longitudinal confounding factors were considered to be functional covariates. A general framework was developed for application to various probability models. Application to a Poisson model showed that the new method is equivalent to a conventional spatial scan statistic that adjusts the underlying population for covariates. In a simulation study with single and multiple covariate models, we found that our new method adjusts the cluster detection procedure more accurately than other methods. Use of the new spatial scan statistic was illustrated by analyzing data on premature mortality in France over the period from 1998 to 2013, with the quarterly unemployment rate as a longitudinal confounding factor.

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“…In this study, we demonstrate a novel method to identify and determine the criteria for fibrinogen and FDPs by using the AI function included in Mathematica (Wolfram Inc.). The fibrinogen criterion was determined based on the functional form of FDP using AI, specifically focusing on the two types of data distributions: a log normal distribution that is often observed in physiological data distributions and a Weibull distribution that is usually observed in modeling failure times. Then, the production of FDPs from fibrin and fibrinogen in patients with massive hemorrhage with or without DIC during delivery was investigated by original simultaneous differential equations.…”

Section: Introductionmentioning

confidence: 99%

New method for determining fibrinogen and FDP threshold criteria by artificial intelligence in cases of massive hemorrhage during delivery

Miyagi

Tada

Yasuhi

et al. 2019

J of Obstet and Gynaecol

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Aim: To investigate the feasibility of a novel method using artificial intelligence (AI), in which the fibrinogen criterion was determined by the quantitative relation between the distributions of fibrin/fibrinogen degradation products (FDPs) and fibrinogen. Methods: A dataset of 154 deliveries comprising more than 2000 g of blood lost due to hemorrhage, excluding disseminated intravascular coagulation (DIC), among patients from eight national perinatal centers in Japan from 2011 to 2015 were obtained. The fibrinogen threshold criterion was identified by using the function that best fit the distributions of FDP as determined by AI. FDP production was described by differential equations using a dataset containing fibrinogen levels less than the fibrinogen criterion and solved numerically. Results: A fibrinogen level of 237 mg/dL as the threshold criterion was obtained. The FDP threshold criteria were 2.0 and 8.5 mg/dL for no coagulopathy and a failed coagulation system, respectively. Conclusion: The fibrinogen threshold criterion for patients with massive hemorrhage excluding DIC at delivery were obtained by selecting the functions that best fit the distributions of FDP data by using AI.

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A spatial scan statistic for survival data based on Weibull distribution

Cited by 33 publications

References 16 publications

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic

A functional‐model‐adjusted spatial scan statistic

New method for determining fibrinogen and FDP threshold criteria by artificial intelligence in cases of massive hemorrhage during delivery

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