Public Health, Health Services Research and HTA

Doctoral Thesis (Dr. phil.)

Doctoral Thesis

Topic: Relations between tumor-specific mortality and overall mortality

Background and objective: Screening and therapeutic trials in cancer showing a reduction in tumor-specific mortality do not always show a reduction in overall mortality as well. How can this be explained and how do decision-analytic models simulating effects on life-expectancy deal with it?

Included work packages:

1) Problem assessment: Literature review identifying cancer screening and treatment trials indicating a discrepancy between tumor-specific and overall survival and showing how decision-analytic models accounted for this.

2) Theoretical part: Identification and elaboration of possible explanations for discrepancies in tumor-specific and overall survival (Suggestion to distinguish two situations – (i) where competing mortality risks are independent and (ii) where competing mortality risks are dependent) and presentation of available methods to adapt decision-analytic models to the respective situation.

3) Empirical part: Application of different methods to deal with the problem using an existing or newly developed decision-analytic model (comparing analyses assuming independence of competing mortality risks vs. analyses assuming and accounting for interdependent competing mortality risks).

Time horizon: 3 years

Contact: Assoc.-Prof. PD. Dr. Nikolai Mühlberger MPH

Institute of Public Health, Medical Decision Making and Health Technology Assessment

UMIT - University for Health Sciences, Medical Informatics and Technology

email: nikolai.muehlberger@umit.at

Application requirements:

Applicants should have knowledge and strong interest in epidemiology and decision-analytic modeling, a high degree of research autonomy (self-learning skills), and English language skills.

 

Topic: Effectiveness and cost-effectiveness of active surveillance strategies in prostate cancer patients

Background and objective:

Prostate cancer screening causes harm by overdiagnosis and overtreatment. Active surveillance of detected cancers instead of immediate treatment may break the link between overdiagnosis and overtreatment and thus is recommended to reduce the harms of screening. However, there are multiple active surveillance protocols and results of the decision-analytic Prostate Cancer and Outcome Model (PCOP Model) have shown that active surveillance is not always beneficial but may result in less quality-adjusted life-years than immediate treatment, if it postpones treatment for too long. Therefore, criteria for treatment initiation under active surveillance are crucial. The objective of this thesis is to evaluate different active surveillance strategies (existing and hypothetical) using an updated and extended version of the PCOP-Model.

Included work packages:

1) Preparation phase: Review of existing active surveillance strategies and decision-analytic models used for the evaluation of active surveillance

2) Benefit-harm analysis comparing prostate cancer screening combined with various active surveillance strategies to screening with immediate treatment and no screening with active surveillance or immediate treatment using an extended version of the PCOP model. (To allow for simulation of various active surveillance strategies extensive modifications of the model are required, e.g. integration of data on prostate-specific antigen (PSA) progression data.)

3) Cost-Effectiveness and Utility analysis with the extended PCOP model evaluating the cost-effectiveness of various active surveillance strategies. (To allow for cost-effectiveness analyses additional cost parameters need to be assessed and implemented.)

Time horizon: 3-4 years

Contact: Assoc.-Prof. PD. Dr. Nikolai Mühlberger MPH

Institute of Public Health, Medical Decision Making and Health Technology Assessment

UMIT - University for Health Sciences, Medical Informatics and Technology

email: nikolai.muehlberger@umit.at

Application requirements:

Applicants should have good knowledge and strong interest in the areas of decision-analytic modeling, economic evaluation and epidemiology, a high degree of research autonomy (self-learning skills), and English language skills. Clinical knowledge in the field prostate cancer would be helpful.