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Obtaining statistical data

Projects / Programmes source: ARIS

Years of life lost as a measure of disease burden

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Research activity

Code Science Field Subfield
3.08.00  Medical sciences  Public health (occupational safety)   

Code Science Field
P160  Natural sciences and mathematics  Statistics, operations research, programming, actuarial mathematics 

Code Science Field
3.03  Medical and Health Sciences  Health sciences 
Keywords
Survival analysis, relative survival, years lost, pseudo-observations, competing risks
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (15)
no. Code Name and surname Research area Role Period No. of publications
1.  30722  PhD Rok Blagus  Systems and cybernetics  Researcher  2019 - 2020  198 
2.  39138  Rok Hribar  Computer science and informatics  Technical associate  2022 - 2023  21 
3.  24344  PhD Nataša Kejžar  Systems and cybernetics  Researcher  2019 - 2022  156 
4.  56197  Tina Košuta  Medical sciences  Researcher  2021 - 2023  9 
5.  56836  Maša Kušar  Medical sciences  Researcher  2022 - 2023  8 
6.  15355  PhD Branimir Leskošek  Public health (occupational safety)  Researcher  2019 - 2023  182 
7.  29917  PhD Lara Lusa  Public health (occupational safety)  Researcher  2022 - 2023  250 
8.  51959  PhD Damjan Manevski  Public health (occupational safety)  Researcher  2019 - 2023  42 
9.  57119  Urh Peček    Technical associate  2022 - 2023  0 
10.  51964  PhD Jakob Peterlin  Mathematics  Researcher  2022 - 2023  10 
11.  23437  PhD Maja Pohar Perme  Public health (occupational safety)  Head  2019 - 2023  291 
12.  33230  PhD Nina Ružić Gorenjec  Mathematics  Researcher  2019 - 2023  51 
13.  08992  PhD Janez Stare  Public health (occupational safety)  Researcher  2019 - 2023  279 
14.  36368  PhD Marko Vidak  Medical sciences  Researcher  2022 - 2023  22 
15.  17837  PhD Gaj Vidmar  Systems and cybernetics  Researcher  2019 - 2022  550 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0381  University of Ljubljana, Faculty of Medicine  Ljubljana  1627066  48,238 
Abstract
How good is the survival of our observed group when compared to the general population survival? This seemingly simple question has been frequently addressed in the literature and often answered with the number of years lost or saved compared to the general poulation. However, the approaches to estimate this measure are typically highly biased and often misleading due to implicit immortal bias. The number of years of life lost due to a specific cause has also been reported in the literature, but the methodology there is even more questionable as the total of years lost by causes may be greater than the overall number of years lost. Recently, a new estimator of years of life lost or gained has been proposed and its variance has been defined. Further work must be done to make this approach useful in practice. First, the properties of the measure must be clearly understood and the estimator must be redefined to allow the time-varying hazards in the general population and to take into account the fact that the administrative censoring at the end of the study may be informative due to the long follow-up time. The variance estimator must be adapted to take into account the variability of the population part, which is not independent of the observed data. We will consider pseudo-observations as a tool that may prove useful in modelling the number of years of life saved or lost. We will study the properties of such models and adapt them for left truncation and the presence of covariate dependent informative censoring, as these are two of the issues that are common in data with long follow-up time. Lastly, all the newly proposed methodology requires development of user-friendly software. Our project will address all these issues in a comprehensive way and thus ensure that the methodology will be well understood and ready to be used in standard survival analyses.
Significance for science
The goal of the project is to develop the methodology of years lost or saved compared to the general population in a comprehensive way, so it could be well understood and directly used in practice. The project is based on the recently introduced cause-specific estimator – the basic theory has been developed and the estimator has been used in the analysis of patients with bipolar disorder. By developing this theory further we wish to make this methodology an important new and generally usable approach that can shed new light on survival data. With a comprehensive approach, we intend to deal with all the major issues that arise in practical use of the methodology. The basic theoretical advance is to define a consistent estimator in case of time-varying population mortality hazard, to deal with informative censoring and to develop a sensible variance estimator. We expect that regression models with years lost/saved as the outcome will prove as another useful application of the pseudo-observations theory. Furthermore, the left-truncated outcome will present the first practical application of the most recent theory of stopped pseudo-observations. The development of efficient algorithms and user-friendly functions will enable wide usage of the methodology which will contribute to diminishing the gap between the advances in statistical theory and its use in practice. However, the availability of easy-to-use functions only makes sense when accompanied with a comprehensive study of the properties of the measure that enables the user to make valid interpretation of the results. The new methodology will provide interesting new information when analyzing survival data and will be particularly interesting when studying relative survival of groups of patients whose survival is comparable or even better than that of the general population – such data can not be analyzed with standard relative survival methodology. The methodology will be particularly useful in presenting the results of scientific studies to the layman audience as it provides a well defined and easy to understand outcome of analyses.
Significance for the country
The goal of the project is to develop the methodology of years lost or saved compared to the general population in a comprehensive way, so it could be well understood and directly used in practice. The project is based on the recently introduced cause-specific estimator – the basic theory has been developed and the estimator has been used in the analysis of patients with bipolar disorder. By developing this theory further we wish to make this methodology an important new and generally usable approach that can shed new light on survival data. With a comprehensive approach, we intend to deal with all the major issues that arise in practical use of the methodology. The basic theoretical advance is to define a consistent estimator in case of time-varying population mortality hazard, to deal with informative censoring and to develop a sensible variance estimator. We expect that regression models with years lost/saved as the outcome will prove as another useful application of the pseudo-observations theory. Furthermore, the left-truncated outcome will present the first practical application of the most recent theory of stopped pseudo-observations. The development of efficient algorithms and user-friendly functions will enable wide usage of the methodology which will contribute to diminishing the gap between the advances in statistical theory and its use in practice. However, the availability of easy-to-use functions only makes sense when accompanied with a comprehensive study of the properties of the measure that enables the user to make valid interpretation of the results. The new methodology will provide interesting new information when analyzing survival data and will be particularly interesting when studying relative survival of groups of patients whose survival is comparable or even better than that of the general population – such data can not be analyzed with standard relative survival methodology. The methodology will be particularly useful in presenting the results of scientific studies to the layman audience as it provides a well defined and easy to understand outcome of analyses.
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