Statistical software developed at the Institute of Biomedical Informatics, Faculty of Medicine, University of Ljubljana:

  • randomCancers functions for estimating an upper limit to the probability of random cancer using registry data as described in paper Random Cancers as Supported by Registry Data by Janez Stare, Robin Henderson and Nina Ružić Gorenjec
    • functions
    • programmed by Nina Ružić Gorenjec & Robin Henderson
  • re R function for calculating the Re measure of explained variation for the Cox model, Aalen's model or any of the parametric models included in the survreg function
    • function
    • programmed by Maja Pohar Perme
  • pseucheck R function for model diagnostics using pseudo-observations: for models fitted with 'coxph', 'crr', 'lm' and 'geese' function
    • function
    • requires packages: pseudo, survival, cmprsk, geese
    • programmed by Maja Pohar Perme

  • koq function for S/S-PLUS (available at StatLib)
    • calculates Kent & O'Quigley's measure of dependence for censored data when dependence between time and covariates is modelled by Cox's model
    • programmed by Andrej Blejec & Janez Stare
  • bj function for S/S-PLUS/R (incorporated into Design package by Frank E. Harrell)
    • fits the Buckley-James distribution-free least squares multiple regression model to a possibly right-censored response variable
    • programmed by Janez Stare, Harald Heinzl & Frank E. Harrell
  • measures of explained variation in survival analysis (other than koq) for S/S-PLUS (available upon request)
  • pseu R function for Cox and additive model diagnostics using pseudo-observations for producing plots described in Pohar Perme M, Andersen PK. Checking hazard regression models using pseudo-observations. Statistics in Medicine 2008;27:5309-5328.
  • relsurv package for relative survival analysis in R (version 2.0, included as contributed package into CRAN)
    • implementation of existing regression methods plus our own method (Stare, Henderson & Pohar: An individual measure of relative survival. JRSS Series C, 2005, 54(1), 115-126)
    • implementation of goodness of fit methods based on residuals (Stare, Pohar & Henderson: Goodness of fit of relative survival. Statistics in Medicine, 2005, 24(24), 3911-3925)
    • paper describing the package (Pohar & Stare: Relative survival analysis in R. Computer Methods and Programs in Biomedicine, 2006, 81(3), 272-278)
    • programmed by Maja Pohar
  • interactive demonstration of relative survival
    • website (form with links to instructions and references)
    • based on the individual measure of relative survival and the relsurv package (see above)
    • idea by Janez Stare, implementation by Brane Leskošek (Perl, Linux) and Maja Pohar (R)
  • R functions for calculating the reduced citation distribution (h-index) function
  • chplot package for R (current version 1.2, included as contributed package into CRAN)
    • produces augmented convex hull plots, which are a nice and informative way of displaying large amounts of grouped bivariate data
    • paper describing the package (Vidmar & Pohar: Augmented convex hull plots: Rationale, implementation in R and biomedical applications. Computer Methods and Programs in Biomedicine, 2005, 78(1), 69-74)
    • package source (tar.gz)
    • binary package for Windows (zip)
    • idea and design by Gaj Vidmar, programmed by Maja Pohar
  • visualisation of concordance
    • two newly proposed graphical displays for the previously unaddressed task of visualising concordance (for ranked data, as for, e.g., Kendall's W)
      • concordance bubble-plot (Excel workbook with macro and sample data) -- depicts raw data, i.e., the actual ranks assigned to objects
      • pin-cushion plot (interactive and batch-mode code with sample data for jsplot; zipped) -- depicts within-object rank differences
    • paper describing the methods (Vidmar & Rode: Visualising concordance. Computational Statistics, 2007, 22(3), in press)
    • concordance parallel-coordinates-plot (SigmaPlot 2000 notebook; zipped) -- another possibility (depicts pairs of ranks assigned to objects), which proved to be less practically useful (though esthetically appealing)
    • ideas, design and implementation by Gaj Vidmar & Nino Rode
  • Bland-Altman function for R
    • displays the classic Bland-Altman difference plot with limits of agreement, prints the mean difference and both agreement limits (along with 95% CIs) and returns a list of relevant parameters (the mean difference, its SD and SE, upper and lower agreement limits, their SE, and the t value used in the calculation of CIs)
    • function and sample data (zip)
    • programmed by Jaro Lajovic
  • Excel workbooks for demonstrational and teaching purposes
    • Poisson regression
      • estimation procedure demonstrated with example 4.5 from A.J.Dobson, An Introduction to Generalized Linear Models (1st ed.), Chapman & Hall, 1990
      • based on Excel's matrix functions, step-by-step iteration with macro
      • requires Excel 97 or higher
      • idea and implementation by Gaj Vidmar
    • parallel coordinates plot
      • static version for producing presentation/publication graphics
      • for two variables, typical use is in conjunction with paired-samples t-test, but such plot can also accompany the Bland-Altman method (see below)
      • requires Excel 97 or higher
      • idea and implementation by Gaj Vidmar
    • modified Box-Cox power transformation towards normal distribution
      • the lambda parameter can be either input manually or estimated with the maximum-likelihood method using Excel's Solver add-in
      • the transformation effect is demonstrated with automated histogram
      • requires Excel 97 or higher
      • idea and implementation by Gaj Vidmar
    • interactive population pyramid (Eurostat's projection for Slovenia 2004-2051)
    • Bland-Altman plot
      • implements the classic, simple, indispensable yet still too often overlooked method-comparison procedure by Bland & Altman
      • requires Excel 97 or higher
      • core code by Michael Schacht Hansen (Section for Health Informatics, University of Aarhus, Dennmark); workbook, error trapping and embellishment by Jaro Lajovic & Gaj Vidmar
    • windowgram and coplot
      • presents two methods, endorsed by Larry Weldon (Simon Fraser University, Vancouver, Canada) as under-used but simple and ideal for inclusion into introductory statistics courses
      • windowgrams are the simplest case of and hence the introduction to kernel density estimation; coplots is short for conditional plots, so it is just another name for what is also called panel plots, Trellis display or lattice graphics
      • requires Excel 97 or higher
      • idea and implementation by Gaj Vidmar
    • dot plots
      • presents simple and multi-way dotplots as invented by William S. Cleveland and promoted by S-Plus and R
      • requires Excel 97 or higher
      • idea and implementation by Gaj Vidmar
    • univariate dot-density plot
      • presents the simplest yet very nice solution, i.e, character-based cell-chart, of what is also known as stacked dot-plot
      • requires Excel 97 or higher
      • idea and implementation by Gaj Vidmar
    • stem-and-leaf plot
      • requires Excel 97 or higher
      • macro by Nick Maxwell (Data Matters Resource Center); debugging and workbook design by Gaj Vidmar
    • Kaplan-Maier survival curve estimation
      • requires Excel 97 or higher
      • unauthorised English translation and modification of the workbook with macro by Shigenobu Aoki (Faculty of Social and Information Studies, Gunma University, Japan); produced by Gaj Vidmar
    • automated histogram binning (with random sampling from normal distribution)
      • requires Excel 97 or higher
      • idea and implementation by Gaj Vidmar
    • Iris dataset
      • the most (perhaps too) often used (and sometimes abused) dataset in the field of statistics (too often abused in the fields of machine learning, data mining and information visualisation)
      • reference for data collection: Anderson, E. (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2-5.
      • reference for data analysis: Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7 (2), 179-188.
      • a truly comprehensive presentation: Les Iris de Fisher ou Comment se familiariser avec le logiciel R (in French)

Ostalo programje ki smo ga razvili:

  • PowerPointov makro za štetje vseh znakov v predstavitvi
    • sprogramiral Gaj Vidmar
    • razvit za potrebe prevajalcev, da olajša zaračunavanje prevodov predstavitev
    • poleg znakov v diapozitivih, zna makro prešteti tudi znake v matricah diapozitiva, opomb, izročkov in naslova
    • deluje z angleško in slovensko verzijo Microsoftove pisarne
    • makro je namenjen slovenskim uporabnikom, zato je uporabniški vmesnik v slovenščini in tudi navodila za namestitev/odstranitev (spodaj) so le v slovenščini
    • programska koda je zaščitena z geslom
    • namestitev in odstranitev:
      • datoteko StetjeZnakov.ppt shranite na disk
      • zaženite PowerPoint in datoteko odprite, nato pa jo shranite kot dodatek (.ppa)
      • kam se bo dodatek shranil, je odvisno od konfiguracije računalnika (npr. na Windows® 2000 v mapo C:\Documents and Settings\uporabniško_ime\Application Data\Microsoft\Addins)
      • zaprite PowerPoint
      • ponovno zaženite Powerpoint ter poskrbite, da so makri omogočeni (Orodja - Možnosti - Varnost - Varnost makrov... - nizka ali srednja)
      • namestite nov dodatek (Orodja - Dodatki... - Dodaj nov...)
      • v meniju Orodja se bo pojavila opcija "Štetje znakov"
      • če ga ne želite več uporabljati, dodatek preko menija, iz katerega ste ga namestili, odstranite iz pomnilnika ali povsem odstranite
      • tudi, če izberete popolno odstranitev, bo datoteka StetjeStrani.ppa ostala na disku, zato jo morate, če to želite, izbrisati ročno iz zgoraj omenjene mape

Na kratko o IBMI

Inštitut za biostatistiko in medicinsko informatiko (IBMI), prej Inštitut za biomedicinsko informatiko (torej tudi IBMI), je Medicinska fakulteta ustanovila leta 1973 kot izraz potrebe po izvajanju in usklajevanju del, vezanih na analizo podatkov in posredovanje informacij. Program dela in razvoja se je skozi čas prilagajal predvsem spremembam pri financiranju in tehnološkemu napredku, vendar so temeljne smernice ostale enake: inštitut se predvsem posveča dejavnostim, ki so pomembne za raziskovalno delo v medicini. Te pa lahko razdelimo na:


Inštitut za biostatistiko in medicinsko informatiko
Medicinska fakulteta
Univerza v Ljubljani
Vrazov trg 2, 1000 Ljubljana, Slovenija

tel: (01) 543-77-70
fax: (01) 543-77-71
email: ibmi (at)