REGSTATTOOLS: freeware statistical tools for the analysis of disease population databases used in health and social studies
 Laura Esteban^{1},
 Ramon Clèries^{1, 2}Email author,
 Jordi Gálvez^{1},
 Laura Pareja^{1},
 Josep Maria Escribà^{1},
 Xavier Sanz^{1},
 Ángel Izquierdo^{3},
 Jaume Galcerán^{4} and
 Josepa Ribes^{1, 2}
DOI: 10.1186/1471245813201
© Esteban et al.; licensee BioMed Central Ltd. 2013
Received: 24 July 2012
Accepted: 27 February 2013
Published: 7 March 2013
Abstract
Background
The repertoire of statistical methods dealing with the descriptive analysis of the burden of a disease has been expanded and implemented in statistical software packages during the last years. The purpose of this paper is to present a webbased tool, REGSTATTOOLShttp://regstattools.net intended to provide analysis for the burden of cancer, or other group of disease registry data. Three software applications are included in REGSTATTOOLS: SART (analysis of disease’s rates and its time trends), RiskDiff (analysis of percent changes in the rates due to demographic factors and risk of developing or dying from a disease) and WAERS (relative survival analysis).
Results
We show a realdata application through the assessment of the burden of tobaccorelated cancer incidence in two Spanish regions in the period 1995–2004. Making use of SART we show that lung cancer is the most common cancer among those cancers, with rising trends in incidence among women. We compared 2000–2004 data with that of 1995–1999 to assess percent changes in the number of cases as well as relative survival using RiskDiff and WAERS, respectively. We show that the net change increase in lung cancer cases among women was mainly attributable to an increased risk of developing lung cancer, whereas in men it is attributable to the increase in population size. Among men, lung cancer relative survival was higher in 2000–2004 than in 1995–1999, whereas it was similar among women when these time periods were compared.
Conclusions
Unlike other similar applications, REGSTATTOOLS does not require local software installation and it is simple to use, fast and easy to interpret. It is a set of webbased statistical tools intended for automated calculation of population indicators that any professional in health or social sciences may require.
Keywords
Webapplication Prediction Standardized incidence mortality ratio Annual percent change Net percent change of rates Relative survivalBackground
An aim of public health assessment involves describing the health status of a defined population by looking at their changes over time or by comparing their health events to events occurring in other populations. Descriptive epidemiology of cancer, for example, may assess the size of the problem that cancer poses to health, measuring the risk in the same population at different periods of time [1]. To account for rising trends of cancer in a population or to compare populations of different sizes, rates are usually developed to provide the number of events per population unit [2], whereas the number of cancer cases is used to measure the burden of cancer into the health system [3].
Worldwide, statistical methods for descriptive analysis has been expanded and implemented in statistical software packages during the last years. The most comprehensive coverage of statistical methods for analyzing cancer data is SEER*Stat [4], whereas userfriendly statistical software packages for specific timetrend modelling of rates have also been developed to measure the burden of cancer and its projections [5]–[7] changes in trends [8], and survival analysis [9]–[11].
The purpose of this paper is to present a set of webbased tools, REGSTATTOOLShttp://regstattools.net, in order to provide a very easyintuitive way to carry out statistical analyses. The user must upload the predefined file to REGSTATTOOLS webpage to obtain for a determined disease and a period of time: (i) descriptive statistics, (ii) the estimated annual percent change in rates; (iii) the standardized incidence or mortality ratio comparing two time periods or two geographical areas; (iv) the prediction of the expected incident or death cases; (v) the assessment of the differences for incidence or deceased cases between two different time points or two geographical areas in order to clarify the role of the changes on demographic factors and the risk of developing or dying from the disease, and finally, (vi) comparing observed and relative survival.
In this paper REGSTATTOOLS is introduced describing its use through an example on the assessment of the burden of tobaccorelated cancer incidence in two Spanish regions during the period 1995–2004.
Implementation
Descriptive statistics for rates
Suppose that we want to assess the burden of a disease in a certain population of size N during a certain period of time. Consider that we have observed X cases of the disease under study, therefore the crude rate (CR) is defined as X/N. The CR is the simplest and most straightforward summary measure of the population’s diseases under study. But the events may be strongly related to age, so the agespecific events will differ greatly from one another, therefore it is of interest to calculate the agespecific rates. The use of a world standard population [12] and direct standardization (or any other adjustment procedures) seek to provide numbers and comparisons that minimize the influence of age and/or other extraneous factors through the agestandardized rates (ASR) [13]–[15]. These ASRs can be also truncated for the age groups of interest. In cancer, the calculation of truncated rates (TR) over the agerange 3564 [14] has been proposed, mainly because of doubts about the accuracy of agespecific rates in the elderly when diagnosis and recording of cancer may be much less certain. Finally, another useful summary measure of disease frequency is the cumulative rate [14, 15] (CumR), which is the sum of the agespecific incidence rates, taken from birth to age 74, in a certain time period. CumR is an estimate of the cumulative risk (Cumulative Risk= 1exp[−CumR]), which is the risk which an individual would have of developing an event of interest during a certain agespan if no other causes of death were in operation [14].
Estimating the annual percent change in rates (EAPC)
where EAPC = (e^{ β } − 1)·100. The 95% confidence intervals of the EAPC can be easily derived through the standard errors of model (1) [15].
Predicting the Expected number of incident (or death) disease cases by age group using the time trends of rates
where this model is known as the agedrift model. For these models we assume C_{ iT } to follow the Poisson distribution [17]. However, the negative binomial distribution has been also used as an alternative to Poisson when there is evidence of “overdispersion” (higher variance than expected) in the data [18].
Prediction of incidence at a future time F can be made using the fitted model (2) or (3), and replacing T by F and Y_{ iT } by Y_{ iF } into the fitted model. Poisson and Negative Binomial distribution are both assumed for each model in (2) and (3). Therefore 4 models are assessed for the selection of the best fitting model to data. The assessment is made through the Akaike’s Information Criterion (AIC) [19] and the Chisquare test [17].
Comparing risk between two groups (time periods or geographical areas): standardized incidence or mortality ratio (SIMR)
where D is the number of observed events in the target population and E the number of expected events in this population using the incidence (or mortality) rates of the reference population [15].
Assessment of differences due to risk and demographic factors when comparing disease rates of two populations
We note that in each age group we must take into account that rates into the period 2000–2004 must be considered as constant as well as rates into the period 1995–1999. If the population size is expected to increase by 10%, incident cases will also increase by 10%. The effect of population structure is estimated by comparing the rate observed in 1995–1999 and the rate expected in the 2000–2004, through applying the age specific rates observed in 1995–1999 to the population pyramid in 2000–2004. Lastly, the percent change not explained by percent change in the population will be considered to be due to the variation in risk of developing the disease [20]. We note that the net change can be also calculated for the CR [20]. Mathematical details of equation (6) can be found in the Additional file 1.
Assessing survival of a cohort of patients diagnosed with a certain disease
where S_{ o }(T) is the observed survival rate in the cohort of study and S_{ E }(T) is the expected survival of that cohort, this last estimated from a comparable general population life tables stratified by age, sex and calendar time and assume that the cancer deaths are a negligible proportion of all deaths [21]. The RS(T) can be calculated through estimating S_{ o }(T) by the Kaplan–Meier method and S_{ E }(T) using Hakulinen method [22]. The 95% confidence intervals of the RS(T) can be estimated through the standard errors of the logtransformation of S_{ o }(T) assuming S_{ E }(T) as a constant value [23]. Some interpretations about RS are not straightforward. Note that improvements in general mortality of the reference population affect S_{ E }(T) in Equation (7) [24]. Let’s suppose we want to compare 5year RS of lung cancer between periods 1999–1994 (RS(5)=10,5%) and 1995–1999 (RS(5)=8,5%) among men in Catalonia (Spain) [25]. Although cancer mortality decreased in 2000–2004 compared to 1995–1999 in Catalonia [26], we observed a decrease of 5year RS of lung cancer. It may suggest that RS(5) was worsening in 1995–1999 but the explanation is that S_{ E }(5) between both periods increased but S_{ o }(5) remained stable, and therefore RS(5) decreased [26]. In this line, two period comparison of RS for cancers with poor survival should be interpreted with caution [24].
The set of webbased applications included in REGSTATTOOLS
REGSTATTOOLS (http://regstattools.net/) includes a set of webbased statistical applications running under Linux operating system installed in a webserver: SART (Statistical Analysis of Rates and Trends) [27], RiskDiff (a web tool for the analysis of the difference due to risk and demographic factors for incidence or mortality data) [28] and WAERS (WebAssisted Estimation of Relative Survival) [29]. The web pages of all these applications were implemented using the serverside scripting language PHP and HTML [[30] whereas statistical computation has been implemented using R statistical software [31].
The SART applications [27] require an Aggregated Data file that must contain the following columns: sex, agegroup, incidence or mortality year, type of disease, cases and personyears at risk. To perform a descriptive analysis of the disease rates, the user can make use of the Descriptive application after preparing an agegroups file and a standard population’s file. The time trends analysis of rates can be performed using the EAPC application which also requires a standard population file. The application Expected allows a prediction of the expected number of cases in a future period or in other geographical areas, using the aggregated data and a file which contains an external population distribution (personyears at risk by agegroup). The comparison of risk between two groups can be performed through SIMR application which requires a partition of the Aggregated Data file into two files, each one with data of the corresponding time period. Another possibility could be comparing these data with data from another area in the same time period; therefore, two files are required. In this line, note that the user must prepare 6 files to fully run SART.
The RiskDiff application [28] has been developed to perform the analysis described in section Assessment of differences due to risk and demographic factors when comparing disease rates of two populations. It requires information on the number of cases and person years at risk by agegroup in each one of the two periods or two geographical areas to be compared. In this line, the Aggregated Data Selection file must contain 2 columns for each period/area compared: one column referring to personyears and another column referring to number of cases.
Finally, the RS must be obtained through the WAERS application [29] which requires a Selection of Individual Records file with the following variables: patients ID, age and year at beginning of study, sex, years of followup and vital status (death or alive).
We will refer to AF throughout the paper where additional figures and tables can be found, and those that are related to the example section.
Results
File example of individual records
Patient_ID  Sex  d_group  d_age  i_month  i_year  f_month  f_year  Status  Follow_up 

1  1  Lung  84  3  1995  3  1995  1  0.08 
2  1  Lung  65  2  1995  12  1999  1  0.83 
3  1  Lung  63  3  1995  5  1999  1  4.17 
…  …  …  …  …  …  …  …  …  … 
194  2  Lung  72  2  1995  8  1995  1  0.50 
195  2  Lung  42  9  1995  11  1995  1  0.17 
196  1  Lung  72  10  1995  1  1996  1  0.25 
197  1  Lung  52  5  1995  12  2008  0  12.58 
198  1  Lung  79  1  1996  9  1997  1  1.67 
…  …  …  …  …  …  …  …  …  … 
6087  1  Larynx  59  6  2000  8  2004  1  4.17 
6088  2  Larynx  68  7  2001  8  2004  1  3.08 
6089  2  Larynx  53  3  2000  12  2006  0  6.75 
6090  1  Larynx  87  7  1995  12  2006  0  11.42 
…  …  …  …  …  …  …  …  …  … 
12989  1  Kidney  50  7  2002  5  2003  1  0.83 
12990  2  Kidney  90  7  2003  7  2003  1  0.08 
12991  1  Kidney  49  6  2004  6  2004  1  0.08 
12992  1  Kidney  67  4  2004  6  2004  1  0.08 
Girona and Tarragona aggregated data
Sex  Age.group  Year  Group  Cases  Population 

1  1  2000  Kidney  0  27251 
1  2  2000  Kidney  0  27741 
1  3  2000  Kidney  0  29381 
…  …  …  …  …  … 
2  16  2004  Larynx  0  26115 
2  17  2004  Larynx  0  19665 
2  18  2004  Larynx  0  15737 
…  …  …  …  …  … 
1  1  2000  Lung  0  27251 
1  2  2000  Lung  0  29381 
…  …  …  …  …  … 
2  16  2004  Stomach  8  26115 
2  17  2004  Stomach  19  19665 
2  18  2004  Stomach  15  15737 
WAERS output for lung cancer incidence in Girona and Tarragona. 4 executions by period and sex
Men  

19951999  20002004  
Risk  T  RS  LCI  UCI  OS  Risk  T  RS  LCI  UCI  OS 
2252  0  0.999  0.998  1  0.999  2514  1  0.325  0.307  0.344  0.313 
2250  1  0.292  0.273  0.312  0.282  775  2  0.191  0.175  0.207  0.179 
616  2  0.156  0.141  0.173  0.146  436  3  0.15  0.135  0.165  0.136 
319  3  0.113  0.1  0.128  0.103  299  4  0.129  0.116  0.144  0.114 
224  4  0.096  0.084  0.11  0.085  219  5  0.115  0.101  0.13  0.099 
186  5  0.083  0.071  0.096  0.071  140  6  0.106  0.093  0.121  0.089 
155  6  0.074  0.063  0.087  0.062  89  7  0.101  0.087  0.117  0.082 
135  7  0.066  0.055  0.079  0.054  40  8  0.089  0.073  0.108  0.07 
117  8  0.056  0.046  0.068  0.044  17  9  0.065  0.045  0.093  0.049 
89  9  0.051  0.042  0.063  0.039  
61  10  0.047  0.037  0.059  0.035  
41  11  0.04  0.031  0.052  0.029  
21  12  0.037  0.028  0.05  0.026  
12  13  0.036  0.025  0.05  0.024  
4  14  0.027  0.014  0.053  0.018  
Women  
19951999  20002004  
Risk  T  RS  LCI  UCI  OS  Risk  T  RS  LCI  UCI  OS 
240  1  0.275  0.222  0.339  0.267  312  1  0.339  0.289  0.397  0.33 
61  2  0.174  0.131  0.232  0.166  100  2  0.227  0.184  0.281  0.218 
38  3  0.135  0.097  0.189  0.127  65  3  0.174  0.135  0.224  0.164 
29  4  0.123  0.086  0.177  0.114  42  4  0.147  0.11  0.196  0.137 
26  5  0.103  0.071  0.157  0.096  31  5  0.14  0.104  0.189  0.128 
22  6  0.093  0.061  0.142  0.083  20  6  0.128  0.092  0.179  0.115 
19  8  0.085  0.054  0.033  0.074  9  7  0.073  0.037  0.143  0.064 
15  9  0.08  0.05  0.128  0.069  1  9  0.086  0.044  0.169  0.064 
12  10  0.08  0.05  0.129  0.069  
7  11  0.082  0.051  0.031  0.069  
3  12  0.083  0.052  0.134  0.069  
1  13  0.088  0.055  0.141  0.069 
The Descriptive application has been used after preparing an agegroups file (Additional file 1: Table Aff1) and a world standard population’s file (Additional file 1: Table Aff2). Since we are analysing rates, note that Girona and Tarragona population’s personyears at risk for each sexagegroupyear are required. We made use of the Catalan Institute of Statistics population’s distribution (available at: www.idescat.net). Merging a PopulationDistribution (personyears) data file (Additional file 1: Table Aff3) and the previous one, we can obtain the Aggregated Data file (Table 2). Results of the descriptive analysis of tobaccorelated cancer in Girona and Tarragona showed that ASR of lung cancer is the first ranking in men’s (ASR=48.27 per 100,000 menyears at risk) and the second one in women’s (ASR=5.12 per 100,000 womenyears at risk) in the ranking of the tumours of interest (Additional file 1: Figure Aff1). Lung cancer has the highest risk among the tobaccorelated cancers in men (CumR=6%, 6 per 100 men are at risk of developing lung cancer before 75 years old, Additional file 1: Table Aff4). In women, lung and stomach cancers have the highest risk among the tobaccorelated cancers (CumR= 0.58%, less than 1 per 100 women are at risk of developing lung or stomach cancer before 75 years old, Additional file 1: Table Aff4).
We also assessed the evolution of the 5year RS rates of lung cancer between the time period 2000–2004 and the time period 1995–1999 using WAERS through a Selection of Individual Records file (Additional file 1: Table Aff10). Table 3 shows the WAERS output, where we found that 5year RS improved significantly among men (5year RS 19951999=8.3%, 95% CI: 7.1%9.6% versus 5year RS 20002004=11.5%, 95% CI: 10.1%13.0%) whereas these differences between RS were not statistically significant among women (5year RS 19951999=10.6%, 95% CI: 7.1%15.7% versus 5year RS 20002004=14.0%, 95% CI: 10.4%18.9%).
Finally, we predicted the burden of lung cancer for the year 2014 in Catalonia through the Expected application of SART making use of the predicted population for Catalonia in 2014 (Additional file 1: Table Aff11). The application selected the agedrift models (Additional file 1: Table Aff12) as the best fitting ones, predicting 777 cases among women with an ASR of 10.74 cases per 100,000 womenyears and 3085 cases among men with an ASR of 46.82 cases per 100,000 menyears (Additional file 1: Table Aff13). We can also observe the cases by agegroup in Additional file 1: Table Aff14 (the output of the application).
Discussion
There are many “standalone” web pages which are designed to perform only a single statistical test or calculation. REGSTATTOOLS is a website which performs an entire suite of calculations for registry data, with a logical organization and consistent user interface. REGSTATTOOLS incorporates a flexible data import with a variety of methods in order to facilitate the widespread use of these applications with a basic statistical knowledge. The development of REGSTATTOOLS’ applications is an ongoing process which has been implemented with the current version of SART, RiskDiff and WAERS. In this paper, the use of REGSTATTOOLS’ applications was illustrated by analyzing populationbased cancer registry data. However, it can be used to analyze other disease registry data such as diabetes or any other chronic disease.
Up to date of publication SART, WAERS and RiskDiff were accessed 3,179 times in total. Although each application was available on the internet at a different period of time, each one shows different percentage of use by user type (see Additional file 1: Table Aff15). Cancer registries are the main users of SART (65.94%) and WAERS (60.71%) whereas they are the second in the ranking of RiskDiff (35.71%) users. Universities and research centres are also major WAERS (26.16%) and RiskDiff (42.86%) users.
SART includes the calculation of disease rates and other indicators in the same application with no requirement of software installation [27]. WAERS is a webbased survivalspecific application to perform basic RS analysis [29]. Nowadays, mortality rates from all European countries, the United States of America, Canada and Argentina have been incorporated into WAERS. Any WAERS user from all these countries can estimate RS making use of these mortality tables. More advanced survival analysis can be carried out using other statistical software [9]–[11] that requires previous installation in the user’s local computer as well as some technical skills about multivariate analysis. On the other hand and to our knowledge, RiskDiff is the only available web tool that can perform a statistical analysis which identifies which percentage of change in disease rates between two time periods or areas are due to changes in demographic factors, and which are due to changes in risk [28].
Some limitations should be noted in these applications in their current versions. SART does not provide confidence intervals for rates, and axis limits of graphs are created automatically by the application. WAERS does not allow a comparison of two or more RS curves since it does not compute RS for two or more groups at the same time. RiskDiff does not allow the assessment of statistical significance for the percent changes in risk and demographics. Future work will incorporate the integration of these features within these applications, although research in statistical methods must be developed specifically for RiskDiff.
Conclusions
Unlike other similar applications, REGSTATTOOLS does not require local software installation and it is simple to use, fast and easy to interpret. REGSTATTOOLS is a set of webbased statistical tools intended for automated calculation of population indicators that any professional in the health or social sciences may require.
Availability and requirements
Project name: REGSTATTOOLS.
Project home page: Access to the set of applications SART, RiskDiff and WAERS can be found through http://regstattools.net/.
Operating system: Platform independent for accessing the public web server.
Programming language: R and PHP.
Requirements: R statistical software available at http://www.rproject.org/ website is required for the functions implemented.
License: None.
Any restriction to use by nonacademics: None.
Abbreviations
 ASR:

Age standardized rate
 CR:

Crude rate
 TR:

Truncated rate
 cumR:

Cumulative rate
 EAPC:

Estimated annual percent of change
 OS:

Observed survival
 RS:

Relative survival
 SIMR:

Standardized incidence or mortality ratio
 AF:

Additional file
 95%CI:

95% confidence interval
 RiskDif:

A web tool for the analysis of the difference due to risk and demographic factors for incidence or mortality data
 SART:

Statistical analysis of rates and trends
 WAERS:

Webassisted estimation of relative survival.
Declarations
Acknowledgements
We would like to thank the two reviewers as well as the editor for their careful reviews and constructive comments that have improved the manuscript significantly. We would like to thank Dr. Genevieve Buckland for the very constructive and helpful comments. This work was supported by a UICC International Cancer Technology Transfer Fellowship and with Federal funds from the National Cancer Institute, National Institutes of Health under Contract NO2CO41101.
Authors’ Affiliations
References
 Tomatis L, Aitio A, Day N, Heseltine E, Kaldor J, Miller A, Parkin D, Riboli E: Cancer: causes,occurrence and control. 1990, Lyon: IARC Scientific Publications Nr 100
 Esteve J, Benhamou E, Raymond L: Statistical methods in cancer research. Descriptive epidemiology. IARC Sci Publ. 1994, IV: 1302.
 Bray F, Moller B: Predicting the future burden of cancer. Nat Rev Cancer. 2006, 6: 6374. 10.1038/nrc1781.View ArticlePubMed
 Surveillance Research Program, N. C. I. S. s. v. 7. 0. 5: Surveillance research program, national cancer institute SEER*stat software. http://www.seer.cancer.gov/seerstat. 2011. 17120012 Ref Type: Electronic Citation
 De Angelis G, De Angelis R, Frova L, Verdecchia A: MIAMOD: a computer package to estimate chronic disease morbidity using mortality and survival data. Comput Methods Programs Biomed. 1994, 44: 99107. 10.1016/01692607(94)900914.View ArticlePubMed
 Verdecchia A, De Angelis G, Capocaccia R: Estimation and projections of cancer prevalence from cancer registry data. Stat Med. 2002, 21: 35113526. 10.1002/sim.1304.View ArticlePubMed
 Schmid V, Held L: Bayesian ageperiodcohort modeling and prediction  BAMP. J Stat Softw. 2007, 21 (8): 115.View Article
 Kim HJ, Fay MP, Feuer EJ, Midthume DN: Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000, 19: 335351. 10.1002/(SICI)10970258(20000215)19:3<335::AIDSIM336>3.0.CO;2Z.View ArticlePubMed
 Dickman PW, Hakulinen T, Voutilaninen ET: SURV3, Relative survival analysis. 2000, Ref Type: Unpublished Work
 Yu B, Tiwari RC, Cronin KA, McDonald C, Feuer EJ: CANSURV: a windows program for populationbased cancer survival analysis. Comput Methods Programs Biomed. 2005, 80: 195203. 10.1016/j.cmpb.2005.08.002.View ArticlePubMed
 Geiss K, Meyer M, RadespielTroger M, Gefeller O: SURVSOFTSoftware for nonparametric survival analysis. Comput Methods Programs Biomed. 2009, 96: 6371. 10.1016/j.cmpb.2009.04.002.View ArticlePubMed
 Segi M, FUKUSHIMA I, Kurihara M: A proposal on a calculation method to be applied by geographical comparison of cancer mortality. Tohoku J Exp Med. 1954, 60: 307310. 10.1620/tjem.60.307.View ArticlePubMed
 Rothman KJ, Greenland S: Modern epidemiology. 1998, Philadelphia: Lippincott Williams & Wilkins
 Bray F: Agestandardization. Cancer incidence in five continents, volume VIII. IARC scientific publications No. 155. Edited by: Parkin DM, Whelan SL, Ferlay J, Teppo L, Thomas DB. 2002, Lyon: International Agency for Research on Cancer, 8789.
 Estève J, Benhamou E, Raymond L: Statistical Methods in Cancer Research (Vol. IV). 1994, Lyon: IARC Scientific Publications, No 128
 Hakulinen T, Hakama M: Predictions of epidemiology and the evaluation of cancer control measures and the setting of policy priorities. Soc Sci Med. 1991, 33: 13791383. 10.1016/02779536(91)90282H.View ArticlePubMed
 Dyba T, Hakulinen T: Comparison of different approaches to incidence prediction based on simple interpolation techniques. Stat Med. 2000, 19: 17411752. 10.1002/10970258(20000715)19:13<1741::AIDSIM496>3.0.CO;2O.View ArticlePubMed
 McCullagh P, Nelder J: Generalized linear models. 1989, London: Chapman & HallView Article
 Akaike H: IEEE transactions on automatic control 19. IEEE Trans Autom Control. 1974, 19 (6): 716723. 10.1109/TAC.1974.1100705.View Article
 Bashir SA, Estève J: Projecting cancer incidence and mortality using Bayesian ageperiodcohort models. J Epidemiol Bioestat. 2001, 6: 287296. 10.1080/135952201317080698.View Article
 Ederer F, Axtell LM, Cutler SJ: The relative survival rate: a statistical methodology. Natl Cancer Inst Monogr. 1961, 6: 101121.PubMed
 Hakulinen T: Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics. 1982, 38: 933942. 10.2307/2529873.View ArticlePubMed
 Cleries R, Ribes J, Moreno V, Esteban L, Pareja L, Galvez J, Martinez JM, Bosch FX, Borras JM: Relative survival computation. Comparison of methods for estimating expected survival. Gac Sanit. 2006, 20: 325331. 10.1157/13091149.View ArticlePubMed
 Perme MP, Stare J, Esteve J: On estimation in relative survival. Biometrics. 2012, 68: 113120. 10.1111/j.15410420.2011.01640.x.View ArticlePubMed
 Galceran J, Puigdefabregas A, Ribas G, Izquierdo A, Pareja L, MarcosGragera R: [Cancer survival trends in Catalonia and comparison with Europe]. Med Clin (Barc.). 2008, 131 (Suppl 1): 1924.View Article
 Gispert R, Cleries R, Puigdefabregas A, Freitas A, Esteban L, Ribes J: [Cancer mortality trends in Catalonia, 1985–2004. Med Clin (Barc.). 2008, 131 (Suppl 1): 2531.View Article
 Esteban L, Cleries R, Langohr K, Galvez J, Pareja L, Escriba JM, Ribes J: [Statistical analysis of rates and trends (SART): a webbased tool for statistical calculation of population indicators]. Gac Sanit. 2011, 25: 427431. 10.1016/j.gaceta.2011.04.004.View ArticlePubMed
 Valls J, Cleries R, Galvez J, Moreno V, Gispert R, Borras JM, Ribes JRD: A web tool for the analysis of the difference due to risk and demographic factors for incidence or mortality data. BMC Publ Health. 2009, 9: 47310.1186/147124589473.View Article
 Cleries R, Valls J, Esteban L, Galvez J, Pareja L, Sanz X, Alliste L, Martinez JM, Moreno V, Bosch X, Borras JM, Ribes JM: WAERS: an application for Webassisted estimation of relative survival. Med Inform Internet Med. 2007, 32: 169175. 10.1080/14639230601185575.View ArticlePubMed
 Davis ME, Phillips JA: Learning PHP and MySQL. 2007, California, US: O’ReillyMedia Inc. Gravenstein
 R Development Core Team: R: A language and environment for statistical computing. c, Vienna, Austria: R Foundation for Statistical Computing, http://www.Rproject.org. 2008 Ref Type: Electronic Citation, ISBN 3900051070
 The prepublication history for this paper can be accessed here:http://www.biomedcentral.com/14712458/13/201/prepub
Prepublication history
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.