Quality of care for cancer patients on home parenteral nutrition: Development of key interventions and outcome indicators using a two-round Delphi approach

Purpose: Clear recommendations on how to guide patients with cancer on home parenteral nutrition (HPN) are lacking as the use of HPN in this population remains a controversial issue. Therefore, the aims of this study were to rank treatment recommendations and main outcome indicators to ensure high-quality care and to indicate differences in care concerning benign versus malignant patients. Methods: Treatment recommendations, identified from published guidelines, were used as a starting point for a two-round Delphi approach. Comments and additional interventions proposed in the first round were reevaluated in the second round. Ordinal logistic regression with SPSS 2.0 was used to identify differences in care concerning benign versus malignant patients. Results: Twenty-seven experts from five European countries completed two Delphi rounds. After the second Delphi round, the top three most important outcome indicators were (1) quality of life (QoL), (2) incidence of hospital readmission and (3) incidence of catheter-related infections. Forty-two interventions were considered as important for quality of care (28/42 based on published guidelines; 14/42 newly suggested by Delphi panel). The topics 'Liver disease' and 'Metabolic bone disease' were considered less important for cancer patients, together with use of infusion pumps (p = 0.004) and monitoring of vitamins and trace elements (p = 0.000). Monitoring of QoL is considered more important for cancer patients (p = 0.03). Conclusion: Using a two-round Delphi approach, we developed a minimal set of 42 interventions that may be used to determine quality of care in HPN patients with malignancies. This set of interventions differs from a similar set developed for benign patients. © 2012 Springer-Verlag Berlin Heidelberg.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.