scholarly journals Special feature: Recent statistical methods for survival analysis

2021 ◽  
Author(s):  
Takeshi Emura ◽  
Il Do Ha
Keyword(s):  
Survival Analysis ◽  
Statistical Methods

An overview on standard statistical methods for assessing exposure-outcome link in survival analysis (Part II): the Kaplan-Meier analysis and the Cox regression method

2012 ◽  
Vol 24 (3) ◽  
pp. 203-206 ◽  
Author(s):  
Samar Abd ElHafeez ◽  
Claudia Torino ◽  
Graziella D’Arrigo ◽  
Davide Bolignano ◽  
Fabio Provenzano ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Statistical Methods ◽  
Cox Regression ◽  
Regression Method ◽  
Kaplan Meier

scholarly journals Statistical Methods for Conditional Survival Analysis

2017 ◽  
Vol 28 (5) ◽  
pp. 927-938 ◽  
Author(s):  
Sin-Ho Jung ◽  
Ho Yun Lee ◽  
Shein-Chung Chow
Keyword(s):  
Survival Analysis ◽  
Statistical Methods ◽  
Conditional Survival

scholarly journals Comparisons of Statistical Approaches for Modelling Land-Use Change

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 144 ◽  
Author(s):  
Bo Sun ◽  
Derek Robinson
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Markov Chain ◽  
Survival Analysis ◽  
Land Use Change ◽  
Sample Size ◽  
Statistical Methods ◽  
Cross Validation ◽  
Land Use Type ◽  
Statistical Approaches

Land-use change can have local-to-global environment impacts such as loss of biodiversity and climate change as well as social-economic impacts such as social inequality. Models that are built to anaPlyze land-use change can help us understand the causes and effects of change, which can provide support and evidence to land-use planning and land-use policies to eliminate or alleviate potential negative outcomes. A variety of modelling approaches have been developed and implemented to represent land-use change, in which statistical methods are often used in the classification of land use and land cover as well as to test hypotheses about the significance of potential drivers of land-use change. The utility of statistical models is found in the ease of their implementation and application as well as their ability to provide a general representation of land-use change, given a limited amount of time, resources, and data. Despite the use of many different statistical methods for modelling land-use change, comparison among more than two statistical methods is rare and an evaluation of the performance of a combination of different statistical methods with the same dataset is lacking. The presented research fills this gap in land-use change modelling literature using four statistical methods—Markov chain, logistic regression, generalized additive models and survival analysis—to quantify their ability to represent land-use change. The selection of these methods is based on criteria: (1) the popularity of a method, (2) the difficulty level of implementation, and (3) the ability of accounting for different scenarios. The four methods were compared across three dimensions: accuracy (overall and by land-use type), sample size, and spatial independence via conventional and spatial cross-validation. Our results show that generalized additive model outperformed the other three in terms of overall accuracy and were the best for modelling most of land-use changes with both conventional and spatial cross-validation regardless of sample size. Logistic regression and survival analysis were more accurate for specific land-use types, and Markov chain was able to represent those changes that could not be modeled by other approaches due to sample size restrictions. The overall spatial cross-validation accuracies were slightly lower than the conventional cross-validation accuracies. Our results also demonstrate that not only is the choice of model by land-use type more important than sample size, but also that a hybrid land-use model comprising the best statistical modelling approaches for each land-use change outperformed the individual statistical approaches. While Markov chain was not competitive, it was useful in providing representation using other methods or in other cases where there is no predictor data.

Application of survival analysis to the inception of dementia

1986 ◽  
Vol 16 (3) ◽  
pp. 583-593 ◽  
Author(s):  
Elizabeth Sturt
Keyword(s):  
Survival Analysis ◽  
Individual Differences ◽  
Statistical Methods ◽  
Early Onset ◽  
Age At Onset ◽  
Analytical Techniques ◽  
Early Onset Dementia ◽  
Degenerative Process ◽  
Age Related ◽  
The Difference

SynopsisThe age at onset of dementia of the Alzheimer, Pick or senile type in relatives of probands with early onset dementia was examined using survival analytical techniques applied to data collected by Sjögren et al. (1952). Female relatives were found to have a higher risk of dementia than males, and there was a deficit of affected brothers compared with fathers of probands. In these comparisons due allowance was made for age at the last observation of each relative. Relatives of probable and definite Pick probands had a higher risk than relatives of probable and definite Alzheimer probands, but the difference was not significant and dementia did not occur at an earlier age to the former group.For the relatives as a whole, and for subgroups of relatives, the risk of dementia increased with age, at least up to age 80. It is hypothesized that the pattern of the age-related hazard of dementia is due to the nature of the dementing process; that this slow degenerative process is widespread; and that individual differences in the rate of the process are under the influence of genes. The statistical methods are explained in detail as they have rarely been applied to dementia before, though Chase et al. (1983) have used life tables and survival analysis in testing genetic hypotheses, with an application to Alzheimer's disease.

What are the determinants of survival curves of words?

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Freek Van de Velde ◽  
Alek Keersmaekers
Keyword(s):  
Survival Analysis ◽  
Statistical Methods ◽  
Historical Linguistics ◽  
Survival Rates ◽  
Evolutionary Approach ◽  
Short Paper ◽  
Survival Curves

Abstract An evolutionary approach to historical linguistics can be enlightening when not only the mechanisms, but also the statistical methods are considered from neighboring disciplines. In this short paper, we apply survival analysis to investigate what factors determine the lifespan of words. Our case study is on post-classical Greek from the 4th century BC to beginning of the 8th century AD. We find that lower frequency and phonetically longer lexemes suffer earlier deaths. Furthermore, verbs turn out to have higher survival rates than adjectives and nouns survival analysis.

An overview of standard statistical methods for assessing exposure-outcome link in survival analysis (Part I): basic concepts

2012 ◽  
Vol 24 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Giovanni Tripepi ◽  
Claudia Torino ◽  
Graziella D’Arrigo ◽  
Davide Bolignano ◽  
Fabio Provenzano ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Statistical Methods ◽  
Basic Concepts
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