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Survival analysis with STATISTICA
Kaderjáková, Zuzana ; Hudecová, Šárka (advisor) ; Hurt, Jan (referee)
Survival analysis is a separate statistical area. This paper discusses the~interpretation of basic concepts, principles and methods used and implemented in the software STATISTICA. First, we introduce censoring and ways of characterizing a distribution of survival time. We present Kaplan-Meier estimate of a survival function and also a method of mortality tables. Later, we discuss basic methods of comparison of the survival time distribution in two groups and their suitability for different situations. The paper also deals with application of the survival analysis methods in the financial sector, where we introduce Cox proportional hazards model. Finally, we apply theoretical knowledge to a real data set.
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The results of treatment of primary brain tumours
MRÁZKOVÁ, Tereza
The study is aimed to primary brain tumour patient's prognosis. The study is comprised of data processing, basic information summary of the issue, evaluation of overall survival of primary brain cancer patients and results comparison to the literature. The hypothesis says that the results gained at department of oncology are comparable with the literature. Patient's data were assembled from medical history of Oncology ward of České Budějovice hospital. Data collection was aimed of gender, age, tumour localization, histology, WHO staging, way of treatment, date of treatment initiation and current condition. Study included 116 patients with treatment initiation between 2011 - 2015 at the Oncology ward of České Budějovice hospital. Data were processed using Excel and survival rate was assessed by Kaplan - Meier analysis of the statistical program IBM SPSS Statistics. Overall survival was assessed in terms of the way of treatment (a combination of resection and radiation therapy, a combination of resection, radiation therapy and chemotherapy, palliative treatment), histology (astrocytoma, oligodendroglioma, glioblastoma multiforme), gender and age (younger than 50 years old, older than 50 years). Median overall survival of whole group under treatment was 12 months. In terms of overall survival analysis based on the histological results and treatment (combinations of resection, radiation therapy and chemotherapy) was at variance with literature. The acquired results should serve to further processing by students and as feedback for the oncology ward, because it assesses success of treatment on department.
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Survival analysis with STATISTICA
Kaderjáková, Zuzana ; Hudecová, Šárka (advisor) ; Hurt, Jan (referee)
Survival analysis is a separate statistical area. This paper discusses the~interpretation of basic concepts, principles and methods used and implemented in the software STATISTICA. First, we introduce censoring and ways of characterizing a distribution of survival time. We present Kaplan-Meier estimate of a survival function and also a method of mortality tables. Later, we discuss basic methods of comparison of the survival time distribution in two groups and their suitability for different situations. The paper also deals with application of the survival analysis methods in the financial sector, where we introduce Cox proportional hazards model. Finally, we apply theoretical knowledge to a real data set.
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Introduction to Survival Analysis
Valenta, Zdeněk
Survival analysis is concerned with analyzing time-to-event data where the event of interest usually represents some type of “failure”. In clinical medicine, the event of interest may be e.g. death of a patient from well specified causes, autoimmune rejection of the graft by the transplant recipient or other type of graft failure in transplant studies. In certain situations, however, the true survival outcomes may not be observable, because we have observed a so called “censoring event” which prevented the event of interest from occurring. Such censoring event may represent, for instance, loss of a particular subject from follow-up, occurrence of administrative censoring, which typically takes place in clinical trials, or we may indeed observe other type of “failure”, e.g. death from fatal injuries rather than from cardiovascular causes which were of primary interest in a particular clinical trial. In this article we will stress the importance of a key assumption relating censoring process to survival outcomes and review principle univariate survival analysis methods for uncorrelated data. We will review popular models for analyzing univariate survival data, many of which enable us quantifying effect the prognostic variables independently exert on survival outcomes. Model examples will cover the classes of non-parametric, parametric and semi-parametric methods. We will also review underlying assumptions of individual models and stress the importance of using appropriate models in analyzing univariate time-to-event data.
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