Paul Lambert

The increasing quantity of detailed cancer related data with access to large, linked electronic databases has stimulated novel applied research. Advances in statistical methodology and software are not so rapid. With more detailed data combined with greater understanding of when effects can be considered causal, there needs to be reconsideration of the most appropriate ways to analyse and report comparisons from population-based cancer studies. This project will concentrate on survival analysis methods using large registry-based data.AIM 1: Rethinking how survival is estimated within population-based cancer studies through appropriate integration of causal inference methods.AIM 2: Improving the understanding and communication of risk in population-based cancer studies through further development of reference-adjusted measuresAIM 3: Transferal of methods into applied work through collaboration, software development, and education material.Aims 1 and 2 concentrate on methodological development. Aim1 will develop causal inference methods within the relative survival framework for use in population-based cancer survival studies. Aim 2 will further develop reference adjusted measures, an alternative approach to quantifying survival differences making all-cause and crude probabilities comparable between population groups. Aim 3 will ensure that the novel methods are used in practice through leading and collaborating on applied studies, software development, and education material.

Sweden has some of the best population-based cancer data in the world. An important advantage is the possibility of linking different databases, which leads to more detailed data that makes it possible to deal with more complex research questions. The increased complexity and details of the data lead to challenges with the statistical analyzes to ensure that the clinical research questions are answered in the most appropriate way. Estimating disease progression and survival after a cancer diagnosis, through the use of population-based registry data, is crucial to be able to track progress against cancer and identify risk factors associated with prognosis. With more detailed data, it is possible to use more sophisticated statistical methods. In this project, we will use and further develop methods called multistate models, which aim to give a deeper understanding of the course of the disease and how different risk factors are related to this. When statistical methods become more complex, it is important that the results are communicated in a way that is understandable and useful for patients, healthcare professionals and health care policy makers. This project will therefore further develop methods for presenting results that are easy to understand for those without technical background.   It is important that new methods that are developed are made available to other researchers. We will therefore develop freely available software, collaborate with clinics and epidemiologists in studies that answer important clinical issues, and organize courses / workshops aimed at those who want to use the methods.

Estimating survival after a cancer diagnosis using data from population-based cancer registers is important to be able to monitor the development of cancer and identify risk factors associated with prognosis. Sweden has, internationally, cancer register data of very high quality, and an important advantage is the possibility of merging different databases. This leads to more detailed information and enables studies with more complex issues. The increasing complexity and details of data, however, poses some challenges for statistical analysis to ensure that the clinical research questions are answered in the most appropriate manner. In this project we will develop statistical methods that can be used to answer important clinical issues using data from cancer registers. With more detailed information, it is possible to use more advanced statistical methods. However, it is important that the results of these analyzes are communicated in a way that is understandable and useful for patients, doctors and decision makers. Thus, this project will also ensure that even if the statistical methods are complex, it must be possible to present the results in a way so that the results are easy to understand for them without a technical background. We will develop statistical methods that help us understand the disease process after a cancer diagnosis and further understand the effect of any risk factors. We will also work with other cancer researchers to use the methods in applied research. We will also develop user-friendly software to enable other researchers to use our methods in their research.

Estimating survival after a cancer diagnosis using data from population-based cancer registers is important to be able to monitor the development of cancer and identify risk factors associated with prognosis. Sweden has, internationally, cancer register data of very high quality, and an important advantage is the possibility of merging different databases. This leads to more detailed information and enables studies with more complex issues. The increasing complexity and details of data, however, poses some challenges for statistical analysis to ensure that the clinical research questions are answered in the most appropriate manner. In this project we will develop statistical methods that can be used to answer important clinical issues using data from cancer registers. With more detailed information, it is possible to use more advanced statistical methods. However, it is important that the results of these analyzes are communicated in a way that is understandable and useful for patients, doctors and decision makers. Thus, this project will also ensure that even if the statistical methods are complex, it must be possible to present the results in a way so that the results are easy to understand for them without a technical background. We will develop statistical methods that help us understand the disease process after a cancer diagnosis and further understand the effect of any risk factors. We will also work with other cancer researchers to use the methods in applied research. We will also develop user-friendly software to enable other researchers to use our methods in their research.

Estimating survival after a cancer diagnosis using data from population-based cancer registers is important to be able to monitor the development of cancer and identify risk factors associated with prognosis. Sweden has, internationally, cancer register data of very high quality, and an important advantage is the possibility of merging different databases. This leads to more detailed information and enables studies with more complex issues. The increasing complexity and details of data, however, poses some challenges for statistical analysis to ensure that the clinical research questions are answered in the most appropriate manner. In this project we will develop statistical methods that can be used to answer important clinical issues using data from cancer registers. With more detailed information, it is possible to use more advanced statistical methods. However, it is important that the results of these analyzes are communicated in a way that is understandable and useful for patients, doctors and decision makers. Thus, this project will also ensure that even if the statistical methods are complex, it must be possible to present the results in a way so that the results are easy to understand for them without a technical background. We will develop statistical methods that help us understand the disease process after a cancer diagnosis and further understand the effect of any risk factors. We will also work with other cancer researchers to use the methods in applied research. We will also develop user-friendly software to enable other researchers to use our methods in their research.

Data from cancer registers are often used to assess how many people get a certain cancer diagnosis, how many die from a particular cancer form and the proportion of those who have received a cancer diagnosis that still lives, for example. 5 years after diagnosis. It is of interest to follow changes over time, compare regions (both within the country and internationally) and to identify factors that either increase or decrease the risk of suffering or dying from a particular disease. Within this project, we will develop new statistical methods to be able to answer important clinical research questions when using cancer register data. Increased computer capacity has made it possible to use more sophisticated statistical methods than before. However, it is also of great importance that the results from these methods are presented in a way that is understandable and useful for patients, doctors and decision makers in the healthcare sector. Therefore, we will also attach great importance to developing more effective and insightful ways to present and communicate results from these types of studies. The project combines the development of new static methods and applications of the methods on data from the Swedish quality registers for cancer, to answer important clinical research questions. Cancer patient survival will be analyzed in order to demonstrate to researchers what benefits the new methods have, and to increase understanding of the effect of risk factors on cancer mortality.