Publications by authors named "Nina Haug"

There are no clear guidelines regarding the optimal treatment sequence for advanced pancreatic cancer, as head-to-head phase III randomised trials are missing. We assess real-world effectiveness of three common sequential treatment strategies by emulating a hypothetical randomised trial. This analysis included 1551 patients with advanced pancreatic cancer from the prospective, clinical cohort study Tumour Registry Pancreatic Cancer receiving FOLFIRINOX (n = 613) or gemcitabine/nab-paclitaxel (GEMNAB; n = 938) as palliative first-line treatment.

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Purpose: Many studies on cancer patients investigate the impact of treatment on health-related quality of life (QoL). Typically, QoL is measured longitudinally, at baseline and at predefined timepoints thereafter. The question is whether, at a given timepoint, patients who return their questionnaire (available cases, AC) have a different QoL than those who do not return their questionnaire (non-AC).

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Assessing the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, R, of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools.

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In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs.

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Background: Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases.

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