Marie Reilly

The major investments in the collection and measurement of biomarkers for cancer have generated an interest in more cost-effective designs of studies and more efficient use of the costly data generated. Fall control studies are a very well-used study design where cases of a disease are selected and compared to a selection of healthy controls. For an unusual disease, this is much more effective than following up a sizable amount of healthy individuals to ensure that enough disease cases occur during the follow-up. However, until recently, it was unusual to reuse already collected data from case-control studies. Our work develops methods that eliminate limitations and for case-control studies forward towards new and effective study designs. Our methods make it possible to calculate the prognosis or risk of other cancers using the data from case-control studies or from more cost-effective designs where the earliest cases are compared to the longest living controls. We focused on a number of different cancers where the costly information is collected / collected

dose of radiation therapy and lung cancer, mammographic density and breast cancer, genetic biomarkers for lethal prostate cancer. As time and money is a limitation in itself, researchers continue to use case control and other designs for data collection. Data currently generated for these studies is currently not fully utilized. Our work is to develop methods and software that more cost-effectively utilize research data to estimate cancer risk and prognosis. This can in turn lead to a more optimal and more ethical use of the biological material that volunteers contributed to cancer research.

The major investments in the collection and measurement of biomarkers for cancer have generated an interest in more cost-effective designs of studies and more efficient use of the costly data generated. Fall control studies are a very well-used study design where cases of a disease are selected and compared to a selection of healthy controls. For an unusual disease, this is much more effective than following up a sizable amount of healthy individuals to ensure that enough disease cases occur during the follow-up. However, until recently, it was unusual to reuse already collected data from case-control studies. Our work develops methods that eliminate limitations and for case-control studies forward towards new and effective study designs. Our methods make it possible to calculate the prognosis or risk of other cancers using the data from case-control studies or from more cost-effective designs where the earliest cases are compared to the longest living controls. We focused on a number of different cancers where the costly information is collected / collected

dose of radiation therapy and lung cancer, mammographic density and breast cancer, genetic biomarkers for lethal prostate cancer. As time and money is a limitation in itself, researchers continue to use case control and other designs for data collection. Data currently generated for these studies is currently not fully utilized. Our work is to develop methods and software that more cost-effectively utilize research data to estimate cancer risk and prognosis. This can in turn lead to a more optimal and more ethical use of the biological material that volunteers contributed to cancer research.

The major investments in the collection and measurement of biomarkers for cancer have generated an interest in more cost-effective designs of studies and more efficient use of the costly data generated. Fall control studies are a very well-used study design where cases of a disease are selected and compared to a selection of healthy controls. For an unusual disease, this is much more effective than following up a sizable amount of healthy individuals to ensure that enough disease cases occur during the follow-up. However, until recently, it was unusual to reuse already collected data from case-control studies. Our work develops methods that eliminate limitations and for case-control studies forward towards new and effective study designs. Our methods make it possible to calculate the prognosis or risk of other cancers using the data from case-control studies or from more cost-effective designs where the earliest cases are compared to the longest living controls. We focused on a number of different cancers where the costly information is collected / collected

dose of radiation therapy and lung cancer, mammographic density and breast cancer, genetic biomarkers for lethal prostate cancer. As time and money is a limitation in itself, researchers continue to use case control and other designs for data collection. Data currently generated for these studies is currently not fully utilized. Our work is to develop methods and software that more cost-effectively utilize research data to estimate cancer risk and prognosis. This can in turn lead to a more optimal and more ethical use of the biological material that volunteers contributed to cancer research.