Head-to-head comparison of treatment sequences in advanced pancreatic cancer-Real-world data from the prospective German TPK clinical cohort study.

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. We used marginal structural modelling to compare overall survival (OS) and time to deterioration (TTD) of health-related quality of life (HRQoL) between three common first- to second-line treatment sequences, adjusting for time-varying potential confounding. The sequences were: FOLFIRINOX→GEMNAB, GEMNAB→FOLFOX/OFF and GEMNAB→nanoliposomal irinotecan (NALIRI) + 5-fluorouracil. Outcome was also calculated stratified by patients' prognostic risk according to the Pancreatic Cancer Score. Median OS and TTD of HRQoL independent of risk were 10.7 [8.9, 11.9] and 6.4 [4.8, 7.7] months for FOLFIRINOX→GEMNAB, 8.4 [7.4, 9.7] and 5.8 [4.6, 7.1] months for GEMNAB→FOLFOX/OFF and 8.9 [7.8, 10.4] and 4.6 [4.1, 6.1] months for GEMNAB→NALIRI+5-fluorouracil. Compared to FOLFIRINOX→GEMNAB, OS and TTD were worse for poor-risk patients with GEMNAB→FOLFOX/OFF (OS: HR 2.09 [1.47, 2.98]; TTD: HR 1.97 [1.19, 3.27]) and those with GEMNAB→NALIRI+5-fluorouracil (OS: HR 1.35, [0.76, 2.39]; TTD: HR 2.62 [1.56, 4.42]). Brackets denote 95%-confidence intervals. The estimated real-world effectiveness of the three treatment sequences evaluated were largely comparable. Poor-risk patients might benefit from intensified treatment with FOLFIRINOX→GEMNAB in terms of clinical and patient-reported outcomes. Future randomised trials on sequential treatments in advanced pancreatic cancer are warranted.

Quantifying bias due to missing data in quality of life surveys of advanced-stage cancer patients.

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). METHODS: We employed augmented inverse probability weighting (AIPW) to estimate the average QoL of non-AC in two studies on advanced-stage cancer patients. The AIPW estimator assumed data to be missing at random (MAR) and used machine learning (ML)-based methods to estimate answering probabilities of individuals at given timepoints as well as their reported QoL, as a function of auxiliary variables. These auxiliary variables were selected by medical oncologists based on domain expertise. We aggregated results both by timepoint and by time until death and compared AIPW estimates to the AC averages. Additionally, we used a pattern mixture model (PMM) to check sensitivity of our AIPW estimates against violation of the MAR assumption. RESULTS: Our study included 1927 patients with advanced pancreatic and 797 patients with advanced breast cancer. The AIPW estimate for average QoL of non-AC was below the average QoL of AC when aggregated by timepoint. The difference vanished when aggregated by time until death. PMM estimates were below AIPW estimates. CONCLUSIONS: Our results indicate that non-AC have a lower average QoL than AC. However, estimates for QoL of non-AC are subject to unverifiable assumptions about the missingness mechanism.

Ranking the effectiveness of worldwide COVID-19 government interventions.

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, Rt, of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools. We validate our findings with two external datasets recording 42,151 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less disruptive and costly NPIs can be as effective as more intrusive, drastic, ones (for example, a national lockdown). Using country-specific 'what-if' scenarios, we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions.

A structured open dataset of government interventions in response to COVID-19.

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. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.

High-risk multimorbidity patterns on the road to cardiovascular mortality.

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. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. METHODS: Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. RESULTS: In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2-0.3%), medium (0.3-1%) and high in-hospital mortality (2-11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50-59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. CONCLUSIONS: Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.