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Summary  
Online application for survival analysis (OASIS) is a onestop tool for various statistical tasks involved in analyzing survival data in a userfriendly manner.
OASIS provides a uniform platform that is an essential application to facilitate efficient statistical analyses of survival data in the ageing field.  
The statistical features of OASIS include the calculation of KaplanMeier estimates, mean/median lifespan, mortality rate, MantelCox LogRank test, Fisher's exact test, weighted LogRank test, KolmogorovSmirnov test and Neyman's smooth test.
Moreover, OASIS generates survival and mortality curves that can be easily exported and modified by using common graphic softwares. 
Download  
We provides OASIS source codes for statistical analyses of survival data.
All codes are provided as open source under a GNU General Public License (GPL).
 
Basic Survival Analysis  
Python (higher than 2.4) and matplotlib should be properly installed.
[ Download ]
 
Statistical testing methods  
Python (higher than 2.4), numpy, rpy, statlib, chow test, R, and surv2sample (R package) should be properly installed.
[ Download ]
 
Cox proportional hazards regression  
Python (higher than 2.4), rpy, R, and coxrobust (R package) should be properly installed.
[ Download ]

Basic survival analysis  
Input Format  
As shown in above figure, OASIS takes following format of input data for basic analysis and statistical testing between samples. The data should be tabdelimited.
You can download example files : example1, example2  
% Condition Identifier (Required) [Total number of subjects] (Optional)
# Comment (Optional) Survival Data (Required, The data should be TabDelimited)
 
Output Format  
In basic survival analysis, OASIS provides several survival statistics such as KaplanMeier estimator, Mean/Median lifespan, Survival curve and Mortality curves which can help to user interpret their survival data.
 
Statistics for the estimation of observed survivalIn ageing research, a description of survival data such as the estimation of mean lifespan is essential for determining the effects of a drug treatment or genetic manipulation on ageing. Thus, one of the primary objectives of survival analysis is the estimation of survival function from incomplete datasets. To estimate survival time as the area under the survival curve, it is necessary to characterize the survival function which is a probability of death after some specific time t.
where is the KaplanMeier estimator.

Statistical testing between samples  
Input Format  
Input Format for statistical testing is same as that of basic survival analysis  
Ouput Format  
Test for the significance of difference in lifespanIn a lifespan study, the comparisons of survival functions between experiment and control groups are important to determine the efficacy of the experimental treatments such as genetic manipulation, dietary intervention, or drug treatments. To systematically compare survival functions between experiment and control, we need to check various statistics in survival datasets because different conditions may increase or decrease lifespan in different ways. For example, some conditions could only increase the average lifespan, whereas others could increase both of average and maximum lifespan. Therefore, the statistics of overall lifespan is compared using logrank test, whilst those of a specific time point is compared with Fisher's exact test. Based on comparisons of various statistics with overall lifespan, we can infer which condition reduces mortality caused by midlife diseases or slow down fundamental processes of ageing.

Cox proportional hazards regression  
Input Format  
OASIS takes following format of input data for Cox proportional hazards regression.  
% Name of fields (Required) Survival Data (Required, The data should be TabDelimited)
 
Ouput Format  
 
Evaluating the effect of several risk factorsOASIS provides Cox proportional hazards regression which can evaluate the effect of several risk factors such as sex, age, and weight on survival on survival. By considering that hazard function such as mortality rate can be explained by the proportional sum of risk factors, Cox formulated semiparametric model with following equation.where represent k risk factors which are assumed to act independently, are their regression coefficients, h_{0}(t) is the baseline hazard at time t, and i is a subscript for observation. To find risk factors that can explain hazard function with proportion, the input data format should be different from that of survival analysis. Therefore, we made a separate input form for Cox proportional hazards regression.

Description  Reference  Url 
KaplanMeier Estimator    http://en.wikipedia.org/wiki/KaplanMeier_estimator 
Survival Function    http://en.wikipedia.org/wiki/Survival_function 
Examples    http://www.weibull.com/LifeDataWeb/nonparametric_analysis.htm 
Examples    http://www.statsdirect.com/help/survival_analysis/kaplan.htm 
Wilcoxon signedrank test    http://faculty.vassar.edu/lowry/wilcoxon.html 
Wilcoxon rank sum test    http://elegans.swmed.edu/~leon/stats/utest.html 
Fisher's exact test    http://www.socr.ucla.edu/htmls/ana/FishersExactTest_Analysis.html 
Fisher's exact test    http://faculty.vassar.edu/lowry/fisher.html 
Fisher's exact test    http://www.langsrud.com/fisher.htm 
Log Rank Test    http://bioinf.wehi.edu.au/software/russell/logrank/index.html 
KaplanMeier survival function    http://www.hutchon.net/KaplanMeier.htm 
SURVSOFT  Geiss K et al.,2009  http://www.krebsregisterbayern.de/software_e.html 
coxrobust  Bednarski T et al., 2006  http://cran.rproject.org/web/packages/coxrobust/index.html 
Chow test  Dr. Ernesto P. Adorio  http://adorioresearch.org/wordpress/?p=1789 
Structural Bioinformatics Lab. 