Modeling survival in colon cancer: a methodological review
 Farid E Ahmed^{1}Email author,
 Paul W Vos^{2} and
 Don Holbert^{2}
DOI: 10.1186/14764598615
© Ahmed et al; licensee BioMed Central Ltd. 2007
Received: 07 November 2006
Accepted: 12 February 2007
Published: 12 February 2007
Abstract
The Cox proportional hazards model is the most widely used model for survival analysis because of its simplicity. The fundamental assumption in this model is the proportionality of the hazard function. When this condition is not met, other modifications or other models must be used for analysis of survival data. We illustrate in this review several methodological approaches to deal with the violation of the proportionality assumption, using survival in colon cancer as an illustrative example.
Background
Several methods for estimating survival probability of populations from patient samples have been proposed since the first systematic approach in 1950 [1]. One of the oldest and most straightforward methods for analyzing survival data is to compute the Life Table. This method, proposed by Berkson and Gage [1] for studying cancer survival, uses an enhanced frequency distribution approach. To compute a Life Table, the range of survival times for all patients is divided into subintervals. For each interval, one computes the number and proportion of cases that entered the interval "alive." the number and proportion of cases that "died", and the number of cases that were lost or "censored" in the respective time interval. An observation is censored if the subject leaves the study or is alive when the study ends. Appropriate manipulation of these quantities allows estimation of parameters of interest related to the survival distribution.
The hazard function (developed more fully in section 2) is a nonnegative function (of time) which can be thought of as reflecting the change in an individual's probability of death in the immediate future, given that the individual has survived up to the current time. In humans, the probability of death is higher immediately after birth than it is when the newborn becomes more mature; consequently, the hazard function decreases with age through the maturation process. Subsequent to maturation, there is a long period where an individual's probability of death (in the immediate future) is relatively low and changing very little with the passage of time; here, the hazard function is rather flat and closer to zero. In the final stages of life, this probability increases with increasing age, resulting in an increase in the hazard function.
This pattern of change gives rise to a Ushaped (or "bathtub shaped") hazard function for all cause mortality in humans. The hazard is sometimes referred to as the "force of mortality" or the "conditional failure rate".
Mathematically, the hazard function is simply a reexpression of the survival function, in that specification of either one of these uniquely determines the other. The hazard function, however, has more visual appeal in that it directly displays the time periods over which changes to the risk of death in the immediate future are occurring. To identify these periods from the survival function (rather than the hazard function), the analyst would have to look for sharp drops and flatter sections of the survival curve. The hazard function displays the information more directly.
The Cox model relates the hazard function of an individual at time t, with a vector X = (X_{1}, X_{2},..., X_{ p }) of p covariates (explanatory or predictor variables), to a baseline hazard function h_{0}(t) via a loglinear function:
where β = (β_{1}, β_{2},..., β_{ p }) is a vector of coefficients. An important consequence of this formulation, and the reason for the name "Proportional Hazards Model", is that the hazard ratio function (HRF) for two individuals with covariates X and X* does not depend on time; h(t; X) is a constant multiple of h(t; X*). It is also important to note that the effects of the covariates on the hazard are assumed to be constant over time. Inference about the regression parameter (β) is possible without making assumptions about the form of the baseline hazard function, h_{0}(t); the hypothesis of no association of one or more of the p independent variables with survival can be tested by the likelihood ratio test (LRT) [4].
Figure 1 illustrates the difference between proportional hazards and nonproportional hazards. In Figure 1a the two hazard functions are proportional, and their corresponding HRF (and log hazard ratio function, LHRF) is constant over time as shown in Figure 1b. On the other hand, if the hazard functions are not proportional, they might start at the same value and then diverge, or converge to some common value (Figure 1c), or cross and diverge again, and the corresponding LHRF will not be constant (Figure 1d). Many possibilities arise due to this nonproportionality. For example, the resulting LHRFs may be linear, non linear but monotonic (e.g. logarithmic), or nonmonotonic (e.g. quadratic) (OhnoMachado [5]).
In most applications of the Cox model in cancer research, the goal is to compare survival characteristics between two or more treatment groups. This is accomplished by letting one (or more) of the covariates serve as group indicator variable(s). For example, in comparing a treated group with a control group, we might assign X_{1} = 0 for control group cases and X_{1} = 1 for treated group cases, and compare the fitted survival curves. In this setting, it follows from the constancy of the HRF that the survival curves for different groups do not cross. In practice, however, the HRF may change over time leading to incorrect inferences (O' Quigley and Pessione [6] and Hess [7]) if the Cox model were applied. Although there are several tests available to check for PH violations (Lin et al [8]), they are rarely used because after rejecting the assumption it may not be known how to model the effect of the predictors (Quantin et al [9]).
This article focuses on a review of (a) the Cox model and interpretation of its parameters, (b) assessment of the validity of the PH assumption, (c) the use of timevarying coefficients, and (d) accommodating nonproportional hazards using covariate stratification, partitioning of the time axis, and modeling tune dependence of the regression coefficients.
1 Proportional Hazards Model
First five observations from SCCS
Time  Event  Treatment  Race  Stage  

1  3  1  1  W  1 
2  8  1  1  W  3 
3  60  0  2  W  1 
4  48  0  2  W  3 
5  2  1  2  B  3 
The proportional hazards model as well as the models for nonproportional hazards attempt to describe the survival function S(t) which is the proportion of individuals in the population surviving beyond time t,
For an individual selected at random from the population, the survival function can be interpreted as the probability this individual survives beyond time t.
For real data the survival function is not known but can be estimated from a sample. If everyone in the sample were observed until death the survival function could be estimated by
Typically, not all individuals are observed until death so that some of the data are censored. One method for dealing with censored data is to estimate the survival function with the KaplanMeier estimator. The KaplanMeier estimate for the data in SCCS is plotted in Figure 2. This estimate can be described as a decreasing step function where the steps occur when a death has been observed. The smooth function is the true survival function which we know in this case because the data have been simulated. This population survival function is for a population with the following distributions: Treatment (50% Treatment 1, 50% Treatment 2), Race (80% White, 20% Black), and Stage (40% Stage 1, 30% Stage 2, 30% Stage3).
The survival function shows the relationship between survival and time; that is, between the variables Time and Event in the SCCS data set. To describe the effect of other variables, such as Treatment. Race, and Stage, on survival it is convenient to use the hazard function.
The hazard function addresses the question:
What is the probability that an individual who has survived to time t dies in the interval t to t + Δt?
As the interval Δt gets close to zero so does the probability of death and the hazard function h(t) is the rate at which the probability goes to zero:
The hazard function h(t) is not a probability so it can be larger than one and its value will depend on the units used for time (e.g., days or months). The hazard function is used to give an approximate probability, namely h(t)Δt, for the event that an individual dies in interval t to t + Δt. This approximation is very good for small Δt. The hazard function is also related to the survival function. In fact, the hazard function is an equivalent formulation for the relationship between survival time and the event of death. The proportional hazards model is a simple model to describe the survival between two groups having hazard functions h_{1}(t) and h_{2}(t). The proportional hazards assumption is that ratio of these hazard functions does not depend on time:
where ψ is called the proportionality constant. If ψ > 1 Individuals in group 2 have a greater risk of dying than individuals in group 1, regardless of the time t.
While ψ does not depend on time, it does depend on the explanatory variables in the model, namely Treatment, Race, and Stage. A convenient and readily interpretable linear expression for the logarithm of ψ (which can be any real number, since ψ > 0) is obtained by the proper use of indicator variables according to the following scheme:
For Treatment:
For Race:
For Stage:
x_{Stg2} = 0 and x_{Stg3} = 0 if Stage is 1
x_{Stg2} = 1 and x_{Stg3} = 0 if Stage is 2
x_{Stg2} = 0 and x_{Stg3} = 1 if Stage is 3
With this coding of the explanatory variables, and appropriate substitution into equation 1, it can be shown that
where the β's have been subscripted to reflect their role in the hypothesized causal mechanism, and the subscript on x indicates when the variable takes the value "1" rather than "0". For example. x_{Trt2}, x_{RaceW}, and x_{Stg2} are all 1 and x_{Stg3} is 0 when the individual receives treatment 2, is white and has stage 2 cancer. An individual who received treatment 1, was black, and had stage 1 cancer would have each of these explanatory variables equal to zero. If β_{Trt2} > 0, individuals receiving treatment 2 have a larger log hazard function than those receiving treatment 1. Expressed in terms of the hazard function, exp(β_{Trt2}) > 1 indicates that the hazard function for treatment 2 is proportionally greater than that of treatment 1 and the proportionality is constant for all time t. The generic expression of the hazards proportionality constant is
Parameter estimates for the proportional hazards model using SCCS data
coef  exp(coef)  se(coef)  z  P  

Treatment2  0.78  0.46  0.31  2.53  0.01 
RaceW  0.92  0.40  0.30  3.09  0.00 
Stage2  1.05  0.35  0.39  2.69  0.01 
Stage3  0.05  1.05  0.34  0.15  0.88 
Since the validity of inferences based on the Cox model depends on the proportional hazards assumption, it is desirable to have diagnostic methods for checking this assumption. Many tools are available for checking the PH assumption. These include:
(i) Plots of log(log S(t))
Suppose the hazards for 2 groups are proportional, say h_{1}(t) = ψh_{2}(t). It can be shown that this results in the relationship log(log S_{1}(t)) = log ψ + log(log S_{2}(t)); the log(log S(t)) curves for the two groups differ by a constant. Plotting the estimated log(log S(t)) curves for the two groups, evaluated at the covariate mean values, provides a visual check of the PH assumption. A clear departure from parallelism of these two curves would be consistent with violation of the PH assumption.
(ii) Interactions with Time
Interaction of a covariate x with time can be modeled by including in the model a product term x × (time), resulting in a log hazard function of the form
The coefficient β_{2} reflects x's dependence on time; if this effect is statistically significant in the fitted model we have evidence for nonproportionality.
(iii) Schoenfeld Partial Residuals
For each covariate, a Schoenfeld residual can be calculated for each case that was not censored. A plot of these residuals against time should be "approximately flat" if the PH assumption holds. We illustrate a check on the proportionality assumption for the SCSS dataset using a Schoenfeld residual plot with a smooth curve fit to these residuals (Grambsch and Therneau [10]).
2 Dealing with Nonproportional Hazards
In the SCCS example from the previous section we encountered a model with a nonpreportional hazard function where one of the coefficients varied with time t. In this section we address some ways to cope with time varying coefficients β(t) in the Cox model. Before describing these methods we list the following considerations and caveats.

Detecting the true time dependence of the parameter can be difficult. The Schoenfeld plot for Treatment in Figure 3 shows that the parameter is not constant but does not indicate the proper functional form. Therneau and Grambsch [11] give examples where the data cannot distinguish two very different functions of time.

While there are tests to check for nonproportionality (see for example [10]), these need not be effective against some alternatives. In particular, tests based on the slope of β(t) need not be sensitive to quadratic or other nonlinear functions of time.

Even when a test for nonproportionality is statistically significant, this does not mean the nonproportionality is of practical importance or significance. This potential difficulty arises in any significance test, especially if the sample is large.

Time varying coefficients can be due to other inadequacies in the model. For example, one or more covariates are not included, the functional form of the covariates is incorrect, or another survival function can more effectively model the data.
We consider three methods for dealing with timevarying coefficients: stratification of variables with time varying coefficients, partitioning of the time axis, and modeling the dependence of β(t) on time. We discuss each of these using the SCCS example. Other examples of these approaches can be found on pages 145–147 in [11].
2.1 Covariate Stratification
Parameter estimates for the proportional hazards model after stratification on Treatment
coef  exp(coef)  se(coef)  z  P  

RaceW  0.87  0.42  0.30  2.93  0.00 
Stage2  1.03  0.36  0.39  2.65  0.01 
Stage3  0.09  1.09  0.34  0.26  0.80 
2.2 Partitioning of the Time Axis
Parameter estimates for the proportional hazards model on the first time interval
coef  exp(coef)  se(coef)  z  P  

Treatment 2  0.87  0.42  0.37  2.39  0.02 
RaceW  0.61  0.54  0.34  1.81  0.07 
Stage2  0.38  0.68  0.48  0.79  0.43 
Stage 3  0.26  1.30  0.39  0.67  0.50 
Parameter estimates for the proportional hazards model on the second time interval
coef  exp(coef)  se(coef)  z  P  

Treatment2  0.50  1.65  0.84  0.60  0.55 
RaceW  1.29  0.28  0.72  1.79  0.07 
Stage 2  1.22  0.30  0.86  1.42  0.16 
Stage3  0.80  0.45  0.87  0.91  0.36 
2.3 Modeling Time Dependence of β(t)
There are two basic approaches to modeling the dependence of β(t) on t. One is to specify a known functional form for this relationship. For example,
so that the treatment effect diminishes over time. Another is to allow the data to select the functional form of the time dependence. This can be done by using splines. The basic idea is to replace each of the dashed line segments in Figure 5 with a curve such that the curves are connected at time t = 30. The time axis in Figure 5 is partitioned into just two intervals but by increasing the number of intervals a close approximation can be obtained.
We do not use this approach for the SCCS data. One reason is that software for this approach is not readily available. The SAS procedure phreg can be used when the known functional form is linear; however, this will not work for the function in equation (3). A more important reason is that allowing the data to choose the functional form increases the chance of overfitting the data, and one must be cautious about interpreting the pvalues and confidence intervals for the parameters.
2.4 Other Methods
The methods given above for dealing with nonproportional hazards all involve modification of the Cox model. Another approach is to simply use a different model. Therneau and Grambsch [11] suggest accelerated failure time or additive hazard models. In the Cox model, the covariates act multiplicatively on the baseline hazard function (1). The additive hazard model:
allows the covariates to modify the baseline hazard in an additive way; the effect of the covariate may also vary with time. The additive model measures the additional risk due to the effect of a covariate in absolute terms, whereas the Cox model measures it in relative terms.
Artificial Neural Networks (ANNs) are another class of models that have been used to model cancer data. These are sometimes referred to as "black box" models because of the complex, nonlinear relationships used in the fitting procedure. In the Cox model, the form of the relationship between the covariates and survival is specified; only the parameters of the model are estimated. In contrast to this, with ANNs one tries to estimate both the functional form of the relationships between variables and the parameters that describe those relationships. This limits the ability of these models to identify causal relationships, because it is usually difficult to relate the model parameters to the biological context that generated the data (Ahmed [12]).
Tree Structured Survival Analysis (TSSA) provides another alternative for the study of risk factors (Segal [13]). It is an extension of the Classification And Regression Tree (CART) algorithm developed by Breiman et al [14]. TSSA is an exploratory, nonparametric method that requires no assumptions about the relationship between survival and the potential risk factors (covariates). The primary output of a TSSA algorithm is a "tree structure", whose branches and nodes identify the successive splits of the cases into risk groups with similar survival profiles. TSSA evaluates the relationship between risk factors and survival through recursive partitioning of patients according to their risk factors, with a comparison of survival patterns among the subgroups of patients resulting from each partition. The method not only identifies a set of significant risk factors, but also provides a simple procedure to identify subgroups of participants with an estimate of their associated risk.
3 Application of Modified Cox Models in Colon Cancer Research
Moreau et al [15] proposed a crude survival model that accounts for the nonproportionality of hazards by modeling the hazard ratio as a step function of the followup time (divided into predefined intervals). The application of the piecewise model requires determining both the number and the boundaries of these intervals. Clinicians have conveniently suggested six intervals with the following upper boundaries being 1 month, 6 months, 12 months, 24 months, 60 months, and 120 months (Bolard et al [16]), and employed the 2L program of BMDP Statistical Software analysis (Dixon et al [17]).
Esteve et al [18] fit the baseline hazard function and its dependence on time by partitioning the time axis. However, the coefficients of the covariates were not allowed to depend on time. In contrast, Moreau et al [15] allow the coefficient on age to depend on time and finds the effect of age continues to decline between 24 and 120 months. Moreau et al [15] also allow cancer stage to depend on time. These considerable differences between models that allow covariates to depend on time and those that do not demonstrate the ability of models with time varying coefficients to provide new insights about these changes.
Esteve et al [18] investigate the timedependent logarithm of the hazard ratio for each covariate by modeling the data using Bsplines (de Boor [19] and Stone and Koo [20]). Using this Bspline model, they find a significantly higher risk of post surgical mortality than the Cox model during the first six months, and by 12 months the reverse happens. Moreover, the Cox model overestimates the impact of age on colon cancerspecific mortality after the first 6 months of followup, whereas the impact of age is near zero during this time period when the Bspline model is used. Clinically, the risk of cancer related death for elderly patients is higher only during the initial few months corresponding to postsurgical period. The estimated effect of later periods of diagnosis shows that there may be some benefits of new treatments in reducing postsurgical mortality, but they do not appear to affect the longterm survival (Giorgi et al [21]).
4 Software
Software routines for fitting Cox models are available in all of the popular statistical software packages. Here we provide only a brief summary for some of those that are more widely used. It is worth noting that many of these programs have active and wellorganized user groups whose members are continually writing addon functions and macros, usually available at no cost.
GLIM
GLIM [22], the Generalised Linear Interactive Modeling package, is a flexible, interactive statistical analysis program developed by the GLIM working party of the Royal Statistical Society. It has a suite of macros that perform survival analysis. These include COXMODEL (fits Cox proportional hazards models), PHAZ (plots log hazard against log survival time), LIFETABS (produces a life table analysis from censored survival times), and STEPS (plots the estimated survival curve using the KaplanMeier approach). Other related macros are INIB, LOGRANK2, NORM, LNORM, WEIBMIX, WEIBULL, and RESPLOTS.
R
R [23] is a language and environment for statistical computing and graphics. Unlike the other packages mentioned here, R can be downloaded at no cost from the R website. It is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies). Much code written for S runs unaltered under R. Analysis is accomplished via calls to functions; the arguments to these functions include datasets and model specifications, and the function returns appropriate statistics and graphical output. An R addon package called "survival" (again, available at no cost) contains over 100 files consisting of functions and datasets for survival analysis.
SAS
SAS [24] software has six procedures that perform survival analysis computations. LIFETEST produces life tables and graphs of survival curves. LIFEREG estimates regression models with censored data, but does not allow for timedependent covariates. PHREG fits Cox models, handling both discretetime and continuoustime data, as well as timedependent covariates. Three other procedures (LOGISTIC, PROBIT, and GENMOD), while not designed specifically for survival analysis, are effective for estimating survival models in certain settings.
SPLUS
Marketed by Insightful Corporation, SPLUS [25] contains a complete array of survival analysis tools, including frailty models, smoothing splines, penalized survival models, parametric survival regression, KaplanMeier curves, and Cox proportional hazards models.
SPSS
SPSS [26] is a popular menudriven general statistical analysis package. It contains menudriven routines for constructing Life Tables, plotting KaplanMeier survival curves, and fitting Cox models with timevarying covariates.
Other Programs
Though less widely used than those we have mentioned above, other programs that have Cox model fitting capabilities include BMDP [27], NCSS [28], Minitab [29], Systat [30], Stata [31], and Statistica [32].
Conclusion
The Cox model, also known as the proportional hazards model, often provides a very good approximation to the survival function and its dependence on covariates. For those situations where the proportional hazards model is inadequate we have described three simple modifications that address nonproportional hazards. One solution is to stratify covariates. A disadvantage of this approach is that it does allow modeling the effect of the covariates used in the stratification. Another solution is to partition the time axis into intervals and require proportional hazards only within intervals. A disadvantage here is determining the number intervals that are necessary. The final approach is to model the time dependence of the coefficients. Interpretation should be tempered by the fact that these models tend to overfit the data.
Declarations
Authors’ Affiliations
References
 Berkson J, Gage R: The calculation of survival rates for cancer. Proceedings of Staff Meetings, Mayo Clinic. 1950, 25:Google Scholar
 Everitt B, RabeHesketh S: Analyzing Medical Data Using SPLUS. 2001, New York, NY: SpringerVerlag,View ArticleGoogle Scholar
 Kaplan E, Meier P: Nonparametric Estimation from Incomplete Observations. Journal American Statistical Association. 1958, 53: 457481. 10.1307/2281868..View ArticleGoogle Scholar
 Cox D: Regression models and life tables (with discussion). J Roy Stat Soc B. 1972, 4: 187220.Google Scholar
 OhnoMachado L: Modeling medical prognosis: surgical analysis techniques. J Biomed Inform. 2001, 34: 428439. 10.1006/jbin.2002.1038View ArticlePubMedGoogle Scholar
 O' Quigley J, Pessione F: The problem of a covariatetime qualitative interaction in a survival study. Biometrics. 1991, 47: 101115. 10.2307/2532499View ArticleGoogle Scholar
 Hess K: Graphical models for assessing violation of the proportional hazards assumption in Cox regression. Stat Med. 1995, 14: 17071723.View ArticlePubMedGoogle Scholar
 Lin D, Weil J, Zing Z: Checking the Cox model with cumulative sums of martingalebased residuals. Biometrika. 1995, 80: 557572. 10.1093/biomet/80.3.557.View ArticleGoogle Scholar
 Quantin C, Abrahamowicz M, Moreau T, Bartlett G, Mackenzie T, Tazi M, Lalonde L, Faivre J: Variation over time of the effects of prognostic factors in a populationbased study of colon cancer: comparison of statistical models. Am J Epidemio. 1999, 150: 11881200.View ArticleGoogle Scholar
 Grambsch P, Therneau T: Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994, 81: 51526. 10.1093/biomet/81.3.515.View ArticleGoogle Scholar
 Therneau T, Grambsch P: Modeling Survival Data: Extending the Cox Model. 2000, New York.: Springer,View ArticleGoogle Scholar
 Ahmed F: Artificial neural networks for diagnosis and survival prediction in colon cancer. Molec Cancer. 2005, 4: 2910.1186/14764598429. 10.1186/14764598429View ArticleGoogle Scholar
 Segal M: Regression trees for censored data. Biometrics. 1988, 44: 3547. 10.1307/2531894..View ArticleGoogle Scholar
 Breiman L, Friedman J, Olshen R, Stone C: Classification and Regression Trees. 1984, Wadsworth Statistics/Probability Series, Monterey, CA: Wadsworth & Brooks Cole,Google Scholar
 Moreau T, Le Minor M, Myquel P, Lellouch J: Estimation of the hazard ratio in two grouped samples. Biometrics. 1985, 41: 245252. 10.2307/2530659View ArticlePubMedGoogle Scholar
 Bolard P, Quantin C, Esteve J, Faivre J, Abrahamowicz M: Modeling timedependent hazard ratios in relative survival: application to colon cancer. J Clin Epidemiol. 2001, 54: 986996. 10.1016/S08954356(01)003638View ArticlePubMedGoogle Scholar
 Dixon W, Brown M, Engelman L: BMDP Statistical Software Manual. 1992, University of California Press, Berkeley, CA,Google Scholar
 Esteve J, Benhamou E, Croasdale M, Raymond L: Relative survival and the estimation of net survival: elements for further discussion. Stat Med. 1990, 9: 529538.View ArticlePubMedGoogle Scholar
 de Boor C: A Practical Guide to Splines. 1978, New York: SpringerVerlag,View ArticleGoogle Scholar
 Stone C, Koo C: Additive splines in statistics. Proc Stat Comp Sec. 1985, 4548.Google Scholar
 Giorgi R, Abrahamowicz M, Quantin C, Bolard P, Esteve J, Gouvernet J, Faivre J: A relative survival regression model using Bspline functions to model nonproportional hazard. Stat Med. 2003, 22: 27672784. 10.1002/sim.1484View ArticlePubMedGoogle Scholar
 Statistical Software – GLIM – Technical Content – Making Sense of Your Data., [http://www.nag.co.uk/stats/GDGE_soft.asp]
 The R Project for Statistical Computing., [http://www.rproject.org]
 Business Intelligence and Analytics Software – SAS., [http://www.sas.com]
 Statistical analysis software and data mining software – Insightful. [http://www.insightful.com/products/splus/default.asp]
 SPSS, Data Mining, Statistical Analysis Software, Predictive Analysis, Predictive Analytics, Decision Support Systems., [http://www.spss.com]
 Statistical Solutions: Software for Statisticians, Data Analysis & Clinical Research. , [http://www.statsol.ie/bmdp/bmdp.htm]
 NCSS, PASS, & GESS: Statistics, Graphics, Power Analysis, Sample Size, & Microarray Analysis. , [http://www.ncss.com]
 Statistical Analysis: Data Analysis and Statistics Software and Training – Minitab. , [http://www.minitab.com]
 Systat Software Inc. – Software, Services, Solutions for the Statistics, Scientific Community. ,[ http://www.systat.com]
 Stata: Statistical Software for Professionals. , [http://www.stata.com]
 Data Mining, Statistical Analysis, Quality Control – STATISTICA Software. , [http://www.statsoftinc.com]
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.