Browsing by Subject "survival analysis"
Now showing items 1-4 of 4
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(2020)Prediction of the pathological T-stage (pT) in men undergoing radical prostatectomy (RP) is crucial for disease management as curative treatment is most likely when prostate cancer (PCa) is organ-confined (OC). Although multiparametric magnetic resonance imaging (MRI) has been shown to predict pT findings and the risk of biochemical recurrence (BCR), none of the currently used nomograms allow the inclusion of MRI variables. This study aims to assess the possible added benefit of MRI when compared to the Memorial Sloan Kettering, Partin table and CAPRA nomograms and a model built from available preoperative clinical variables. Logistic regression is used to assess the added benefit of MRI in the prediction of non-OC disease and Kaplan-Meier survival curves and Cox proportional hazards in the prediction of BCR. For the prediction of non-OC disease, all models with the MRI variables had significantly higher discrimination and net benefit than the models without the MRI variables. For the prediction of BCR, MRI prediction of non-OC disease separated the high-risk group of all nomograms into two groups with significantly different survival curves but in the Cox proportional hazards models the variable was not significantly associated with BCR. Based on the results, it can be concluded that MRI does offer added value to predicting non-OC disease and BCR, although the results for BCR are not as clear as for non-OC disease.
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(2022)This thesis presents a complication risk comparison of the most used surgical interventions for benign prostatic hyperplasia (BPH). The investigated complications are the development of either a post-surgery BPH recurrence (reoperation), an urethral stricture or stress incontinence severe enough to require a surgical procedure for their treatment. The analysis is conducted with survival analysis methods on a data set of urological patients sourced from the Finnish Institute for Health and Welfare. The complication risk development is estimated with the Aalen-Johansen estimator and the effects of certain covariates on the complication risks is estimated with the Cox PH regression model. One of the regression covariates is the Charlson Comorbidity Index score, which attempts to quantify a disease load of a patient at a certain point in time as a single number. A novel Spark algorithm was designed to facilitate the efficient calculation of the Charlson Comorbidity Index score on a data set of the same size as the one used in the analyses here. The algorithm achieved at least similar performance to the previously available ones and scaled better on larger data sets and with stricter computing resource constraints. Both the urethral stricture and urinary incontinence endpoints suffered from a lower number of samples, which made the associated results less accurate. The estimated complication probabilities in both endpoint types were also so low that the BPH procedures couldn’t be reliably differentiated. In contrast, BPH reoperation risk analyses yielded noticeable differences among the initial BPH procedures. Regression analysis results suggested that the Charlson Comoborbidity Index score isn’t a particularly good predictor in any of the endpoints. However, certain cancer types that are included in the Charlson Comorbidity Index score did predict the endpoints well when used as separate covariates. An increase in the patient’s age was associated with a higher complication risk, but less so than expected. In the urethral stricture and urinary incontinence endpoints the number of preceding BPH operations was usually associated with a notable complication risk increase.
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(2023)In this thesis, we model the graduation of Mathematics and Statistics students at the University of Helsinki. The interest is in the graduation and drop-out times of bachelor’s and master’s degree program students. Our aim is to understand how studies lead up to graduation or drop-out, and which students are at a higher risk of dropping out. As the modeled quantity is time-to-event, the modeling is performed with survival analysis methods. Chapter 1 gives an introduction to the subject, while in Chapter 2 we explain our objectives for the research. In Chapter 3, we present the available information and the possible variables for modeling. The dataset covers a 12-year period from 2010/11 to 2021/22 and includes information for 2268 students in total. There were many limitations, and the depth of the data allowed the analysis to focus only on the post-2017/18 bachelor’s program. In Chapter 4, we summarize the data with visual presentation and some basic statistics of the follow-up population and different cohorts. The statistical methods are presented in Chapter 5. After introducing the characteristic concepts of time-to-event analysis, the main focus is on two alternative model choices; the Cox regression and the accelerated failure time models. The modeling itself was conducted with programming language R, and the results are given in Chapter 6. In Chapter 7, we introduce the main findings of the study and discuss how the research could be continued in the future. We found that most drop-outs happen early, during the first and second study year, with the grades from early courses such as Raja-arvot providing some early indication of future success in studies. Most graduations in the post-2017/18 program occur between the end of the third study year and the end of the fourth study year, with the median graduation time being 3,2 years after enrollment. Including the known graduation times from the pre-2017/18 data, the median graduation time from the whole follow-up period was 3,8 years. Other relevant variables in modeling the graduation times were gender and whether or not a student was studying in the Econometrics study track. Female students graduated faster than male students, and students in the Econometrics study track graduated slower than students in other study tracks. In future continuation projects, the presence of more specific period-wise data is crucial, as it would allow the implementation of more complex models and a reliable validation for the results presented in this thesis. Additionally, more accuracy could be attained for the estimated drop-out times.
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(2024)Acute myeloid leukemia (AML) is a disease in which blood cell production is severely disrupted. Cell count and morphological analysis from bone marrow (BM) samples are key in the diag- nosis of AML. Recent advances in computer vision have led to algorithms developed at the Hematoscope Lab that can automatically classify cells from these BM samples and calculate various cell-level statistics. This thesis investigated the use of cytomorphological data along with standard clinical data to predict progression-free survival (PFS). A benchmark study using penalized Cox regression, random survival forests, and survival support vector machines was conducted to study the utility of cytomorphology data. As features greatly outnumber samples, the methods are further compared over three feature filtering methods based on Spearman’s correlation coefficient, conditional Cox screening, and mutual information. In a dataset from the national VenEx trial, the penalized Cox regression method with ElasticNet penalization supplemented with Cox conditional screening was found to perform best in the nested CV benchmarking. A post-hoc dissection of two best-performing Cox models revealed potentially predictive cytomorphological features, while disease etiology and patient age were likewise important.
Now showing items 1-4 of 4