Krebsinzidenz und digitale Informationssuche in Deutschland (KREDID)

Ziel: Primäres Ziel des Vorhabens ist es, den möglichen räumlichen und temporalen Zusammenhang zwischen der Inzidenz häufiger Krebserkrankungen und den Suchvolumina auf Google, der meist genutzten Suchmaschine, innerhalb Deutschlands zu charakterisieren. Hintergrund und Vorarbeiten: Um Maßnahmen der Krebsprävention zu verbessern, ist es nötig die öffentliche Aufmerksamkeit für Krebserkrankungen und ihre Prävention zu messen. Nutzer:innen von Internet-Suchmaschinen, wie Google, hinterlassen bei ihrer Suche digitale Spuren, die zu diesem Zweck nutzbar gemacht werden können. Studien aus dem US-amerikanischen Raum konnten signifikante Korrelationen zwischen dem Suchvolumen (die Anzahl an getätigten Suchanfragen) und Krebsinzidenzen feststellen

Micro-simulation analysis of the cost-effectiveness of competing lung cancer screening strategies in Germany

The US National Lung Cancer Screening Trial (NLST), the Dutch-Belgian Randomized Lung Cancer Screening Trial (NELSON), the the German Lung Cancer Screening Intervention (LUSI) Trial all showed that lung cancer screening using Low-dose computed tomography (LCDT) reduced lung-cancer mortality. Following favorable results from the trials that lung cancer screening with LDCT could save lives, expert panels in different countries have released recommendations and position statements to support the implementation of population-based screening programmes in Europe, including in Germany. However, more detailed questions remain with regard to the optimization of screening programmes, such that one obtains a maximum benefit (gain in life years) at an acceptable financial cost and at acceptable overall risk for negative side effects that may be caused by false-positive screen tests or over-diagnosis. Although several microsimulation studies have been performed to evaluate cost-effectiveness of screening with LCDT in different regions, so far no extensive modeling has been performed for the evaluation of the expected benefits, risks of harms and financial costs of diverse lung cancer screening scenarios in Germany. For instance, prevalence rates of smoking and smoking intensity are higher in Europe and in Germany compared to the US and Canada, where most quantitative modeling studies have been conducted. Here, we propose to investigate the benefits, harms and cost-effectiveness of numerous lung cancer screening implementation scenarios for the population of Germany

A health economic evaluation of prostate-specific antigen-based risk-stratified prostate cancer screening strategies in Germany

Prostate cancer (PCa) screening remains a controversial topic. The value of population-based screening in reducing premature deaths remains unclear, primarily due to over-diagnosis and overtreatments. However, with an increased understanding of the strong linkage between prostate-specific antigen (PSA) dynamics and PCa progression, we can hypothesise that a PSA-based risk-stratified PCa screening can reduce overdiagnosis and overtreatment associated with the current PCa screening strategies. Therefore, our objective is to evaluate if a PSA-based risk-adapted PCa screening could potentially reduce the total number of screens for the entire population and the screening costs while producing a similar level or better health outcomes, which may lead to a cost-effective screening strategy. To answer these relevant policy and decision-making questions, the Division of Health Economics at the German Cancer Research Center (DKFZ) is currently adapting, extending and recalibrating a prostate cancer natural history model to the German setting. Model calibration involves the identification of suitable observable parameters with sufficiently robust data available (calibration targets). The most commonly used calibration targets are the disease epidemiological parameters that include but are not limited to incidences, stage distribution at diagnosis, and survival and mortality data. Once the natural history model is calibrated and validated, it can answer several critical questions regarding the cost and value questions related to prostate cancer screening. The project will highly benefit from accessing anonymised Robert Koch Institute individual-level data, primarily used to estimate the relevant epidemiological parameters (calibration targets)

Überleben bei Eierstockkrebs in Deutschland: eine bundesweite Analyse nach Stadium und Histotyp

Epithelial ovarian cancer (EOC) is a lethal gynecologic malignancy, with poor long-term survival. Early symptoms lack specificity, and currently there are no sufficient early detection strategies to identify this disease before it progresses to an advanced stage. In Germany the 5-year relative survival is approximately 43%. Ovarian cancer is a heterogeneous disease with disease histotypes originating from different cells and tissues of origin, and with different somatic mutations, progression profiles, and treatment responses and there is a need for population-based ovarian cancer survival analyses stratified by disease histology and stage at diagnosis. The most recent German cancer registry study was completed in 2013 by Chen et al. A updated analysis in the context of current clinical practice and contemporary classification of disease subtype is needed. Through the utilization of data from the ZfKD, we will be able to provide an updated characterization of ovarian cancer survival by histotype and stage in Germany, drawing upon the pooling of data from national registries. The study population will comprise of women with a diagnosis of invasive epithelial ovarian (ICD-O-3: C569), fallopian tube (C570) or primary peritoneal (C480, C481, C482, C488) in Germany from 1997 - present (most recent case data requested). Subjects will be followed until the earliest of: date of death, date of last follow-up, or December 31, 2022 (or last date available for survival outcomes). The major objectives of this project are to characterize survival over time by histotype and stage, and to assess the overall and relative 1-, 3-, and 5-year survival following an ovarian cancer diagnosis, with analyses stratified by disease stage (FIGO where available) and histotype. Please see the attached statistical methods section for a more detailed description of the analysis plan.Epitheliales Ovarialkarzinom (EOC) ist eine tödlich verlaufende gynäkologische Krebsart mit einer schlechten Langzeitüberlebensrate. Die Frühsymptome sind nicht spezifisch, und derzeit gibt es keine ausreichenden Früherkennungsstrategien, um diese Krankheit zu identifizieren, bevor sie ein fortgeschrittenes Stadium erreicht hat. In Deutschland liegt die relative 5-Jahres-Überlebensrate bei etwa 43 %. Das Ovarialkarzinom ist eine heterogene Erkrankung mit verschiedenen Histotypen, die aus unterschiedlichen Zellen und Ursprungsgeweben stammen, mit unterschiedlichen somatischen Mutationen, Verlaufsprofilen und Behandlungsreaktionen. Daher besteht ein Bedarf an bevölkerungsbasierten Überlebensanalysen des Ovarialkarzinoms, die nach der Histologie der Erkrankung und dem Stadium bei der Diagnose stratifiziert sind. Die jüngste Studie des deutschen Krebs Registers wurde 2013 von Chen et al. abgeschlossen. Eine aktualisierte Analyse im Kontext der aktuellen klinischen Praxis und einer Klassifizierung des Krankheitssubtyps ist erforderlich. Indem wir uns auf die Zusammenführung von Daten aus nationalen Registern stützen, werden wir durch die Nutzung der Daten des ZfKD in der Lage sein, eine aktualisierte Charakterisierung des Überlebens von Ovarialkarzinomen nach Histotyp und Stadium in Deutschland zu liefern

Statistics of Large Scale Sequence Searching

Motivation: Database search programs such as FASTA (Lipman & Pearson, 1985; Pearson & Lipman, 1988), BLAST (Altschulet el., 1990), or a rigorous Smith-Waterman algorithm (Smith 85 Waterman, 1981) produce lists of database entries, which are assumed to be related to the query. The computation of statistical significance of similarity scores is well established for single pairs of sequences and using purely random models. However, the multi-trial context of a database search poses new problems. The credibility of a certain score obtained in a database search decreases with the amount of data that is compared. To improve p-value computation for database search experiments, statistical properties of the databases such as the distribution of sequence length and effects induced by frequently repeated sequence patterns need to be taken into account