The DMC provides internships throughout the year for master students interested in Health economics, statistics and economic modeling. For more information about the possibilities, reach out to us or one of the researchers you are interested in working with. Here are some examples of recent internships at the DMC.

External validation of the ASCCA model against data from the national Dutch colorectal cancer screening program

The ASCCA model is developed within the Decision Modeling Center to evaluate the Dutch colorectal cancer (CRC) screening program. It is externally validated by comparing the model-estimated longterm CRC incidence and mortality reductions with the results of large CRC screening trials. However, these trials have been conducted several years ago and were not conducted in the Dutch population.

As the Dutch screening program is now implemented for several years, there is data available on the performance of the screening program. This enables an external validation of the ASCCA model against results of the national screening program, thereby increasing the confidence in model predictions for the Dutch CRC screening setting.

Anne worked on this project during her internship for the master Health Sciences. First, she conducted extensive data-analyses on data from the national screening program to determine the validation targets. Next, she set up the ASCCA model to replicate the Dutch CRC screening program and compared the model's results to the results of the data-analyses. Her research showed that in general, the ASCCA model predictions are in line with the observed results within the Dutch CRC screening program. These findings suggest that the ASCCA model can be a useful decision-making tool for the optimization of the Dutch CRC screening program. However, some recalibration of the test characteristics of the screening test may be required to further improve the fit of the model predictions to the observed data.

To give a quick summary of how I am experiencing my internship at the Decision Modeling Center: ‘It’s just great!’

Read more about a day in the life of an intern at the DMC

Bayesian modelling of disease transition times from screening data

Beatrice, Master student Mathematics, explored the development and implementation of an innovative Bayesian parametric accelerated failure time (AFT) multi-state model, known as BayesTSM, during her intenship. This model is specifically designed to estimate the transition times in disease progression, drawing on the example of human papillomavirus (HPV) infections progressing to cervical intraepithelial neoplasia (CIN) lesions, and further to cervical cancer. The research leveraged data from the POBASCAM study, which included 1,454 HPV-positive women, to address critical gaps in cervical cancer screening strategies. This project introduced Gamma and Generalized Gamma distributions to BayesTSM to better understand and predict the transition times between the different disease states. These distributions are chosen for their flexibility and the detailed modelling they offer over transition dynamics, which are crucial for optimizing screening schedules and improving intervention strategies for women at risk of developing cervical cancer. Furthermore, by expanding the AFT model with these distributions, the research aimed to provide a more nuanced understanding of the progression from HPV infection to severe cervical disease states, thereby contributing to the advancement of personalized medicine and precision in cancer screening programs.

Comparison of promising approaches to improve the Dutch CRC screening program

Since the introduction of the Dutch colorectal cancer (CRC) screening program, CRC incidence has decreased with 15%. Although this is a commendable achievement, there is still room for improvement. During her internship, Lara (Master student Econometrics and Operations Research) compared four promising approaches to improve the screening program, namely improved performance of the stool test, improved detection of polyps by colonoscopy, improved participation and risk-stratified screening. These four approaches were compared in terms of cost-efficiency in a head-to-head comparison, in order to determine which approach is likely to create most impact. Laura started with a scoping review to determine the potential benefit of each approach. Then, she modelled strategies based on the four approaches to improve the program using the findings of the scoping review as an input. For each strategy, effects, costs and required colonoscopy capacity were determined and compared to the current screening program to identify the optimal strategy. All analyses were conducted with the ASCCA model. Results showed that several strategies exist which make the Dutch CRC screening program more effective and sometimes even less costly. Optimal strategies were all based on risk stratification with screening starting at an earlier age and a lower FIT cut-off than in the current program.