Ovarian suppression using luteinizing hormonereleasing hormone agonists during chemotherapy to preserve ovarian function and fertility of breast cancer patients: A meta-analysis of randomized studies

Background: The role of temporary ovarian suppression with luteinizing hormone-releasing hormone agonists (LHRHa) in the prevention of chemotherapy-induced premature ovarian failure (POF) is still controversial. Our meta-analysis of randomized, controlled trials (RCTs) investigates whether the use of LHRHa during chemotherapy in premenopausal breast cancer patients reduces treatment-related POF rate, increases pregnancy rate, and impacts disease-free survival (DFS). Methods: A literature search using PubMed, Embase, and the Cochrane Library, and the proceedings of major conferences, was conducted up to 30 April 2015. Odds ratios (ORs) and 95% confidence intervals (CIs) for POF (i.e. POF by study definition, and POF defined as amenorrhea 1 year after chemotherapy completion) and for patients with pregnancy, as well hazard ratios (HRs) and 95% CI for DFS, were calculated for each trial. Pooled analysis was carried out using the fixed- and random-effects models. Results: A total of 12 RCTs were eligible including 1231 breast cancer patients. The use of LHRHa was associated with a significant reduced risk of POF (OR 0.36, 95% CI 0.23-0.57; P < 0.001), yet with significant heterogeneity (I2 = 47.1%, Pheterogeneity = 0.026). In eight studies reporting amenorrhea rates 1 year after chemotherapy completion, the addition of LHRHa reduced the risk of POF (OR 0.55, 95% CI 0.41-0.73, P < 0.001) without heterogeneity (I2 = 0.0%, Pheterogeneity = 0.936). In five studies reporting pregnancies, more patients treated with LHRHa achieved pregnancy (33 versus 19 women; OR 1.83, 95% CI 1.02-3.28, P = 0.041; I2 = 0.0%, Pheterogeneity = 0.629). In three studies reporting DFS, no difference was observed (HR 1.00, 95% CI 0.49-2.04, P = 0.939; I2 = 68.0%, Pheterogeneity = 0.044). Conclusion: Temporary ovarian suppression with LHRHa in young breast cancer patients is associated with a reduced risk of chemotherapy-induced POF and seems to increase the pregnancy rate, without an apparent negative consequence on prognosis.

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.

A Monthly Volatility Index for the US Economy

We estimate the monthly volatility of the US economy from 1968 to 2006 by extending the coincidentindex model of Stock and Watson (1991). Our volatility index, which we call VOLINX, hasfour applications. First, it sheds light on the Great Moderation. VOLINX captures the decrease in thevolatility in the mid-80s as well as the different episodes of stress over the sample period. In the 70sand early 80s the stagflation and the two oil crises marked the pace of the volatility whereas 09/11 is themost relevant shock after the moderation. Second, it helps to understand the economic indicators thatcause volatility. While the main determinant of the coincident index is industrial production, VOLINXis mainly affected by employment and income. Third, it adapts the confidence bands of the forecasts.In and out-of-sample evaluations show that the confidence bands may differ up to 50% with respect to amodel with constant variance. Last, the methodology we use permits us to estimate monthly GDP, whichhas conditional volatility that is partly explained by VOLINX. These applications can be used by policymakers for monitoring and surveillance of the stress of the economy.

Animal Models of Friedreich Ataxia

Friedreich ataxia (FRDA) is the most common form of autosomal-recessive ataxia. Common nonmotor features include cardiomyopathy and diabetes mellitus. At present, no effective treatments are available to prevent disease progression. Age of onset varies from infancy to adulthood. In the majority of patients, FRDA is caused by intronic GAA expansions in FXN, which encodes a highly-conserved small mitochondrial matrix protein, frataxin. A mouse model of FRDA has been difficult to generate because complete loss of frataxin causes early embryonic lethality. Although there are some controversies about the function of frataxin, recent biochemical and structural studies have confirmed that it is a component of the multiprotein complex that assembles iron-sulfur clusters in the mitochondrial matrix. The main consequences of frataxin deficiency are energy deficit, altered iron metabolism, and oxidative damage.