Dienstag, 23.04.2024, 12-13 Uhr in W9-109
Julia Dyck, M.Sc.
Universität Bielefeld
Signal detection of adverse drug reactions: The Bayesian Power generalized Weibull Shape Parameter test
After release of a drug on the market, pharmacovigilance monitors the occurrence and changes in known adverse drug reactions (ADRs) as well as detects new ADRs in the population. This is done to keep a drug‘s harm profile updated and can potentially result in adjustments of the prescription labeling or even a recall of the product from the market. In recent years the interest in the use of longitudinal electronic health records for pharmacovigilance increased. Sauzet and Cornelius (2022) provided a test based on the power generalized Weibull (PgW) distribution shape parameters (PgWSP). If both shape parameters of the PgW distribution are equal to one, the distribution reduces to an exponential distribution with constant hazard over time. This is interpreted as no temporal association between a drug and an adverse event. Signal detection can be improved by incorporating existing knowledge about the ADR profile of drugs from the same family or based on expert knowledge about the drug mechanism. Therefore, we propose the development of a Bayesian PgWSP test. The test compares a region of practical equivalence (ROPE) around one reflecting the null hypothesis with the estimated credibility intervals (Krushke, 2015). Depending on the single parameters’ outcome and the chosen combination rule, the tool raises a signal. We performed a simulation study to tune the optimal ROPE and credibility interval for signal detection using a Bayesian PgWSP approach. Samples are generated under varying conditions regarding sample size, prevalence, and proportion of adverse events. Prior assumptions considered are no ADR, ADR at the beginning, middle, or end of the observation period. A range of ROPE and credibility interval types as well as combination rules are considered. The optimal ROPE and credibility interval tuning parameters are determined based on the area under the curve.
Dienstag, 07.05.2024, 12-13 Uhr in W9-109
Prof. Dr. Harry Haupt
Universtät Passau
Fixed-event forecasting systems and efficiency measurement
Fixed-events forecasting systems are regularly used in economic or political analysis. Prominent examples are professional forecasters for key macroeconomic variables or opinion researchers for elections. Despite their widespread use as prima facie forecasts and inputs for economic policy, sentiment analysis or forecasting algorithms, there is considerable uncertainty regarding the definition and measurement of the efficiency of such forecasting systems. The prevailing paradigm defines efficient forecasts by sequential revisions of forecasts that represent martingale differences. Alternative concepts require either knowledge of the forecasters’ dgp or their loss and allow the derivation of criteria analogous to optimal time series forecasts. This paper first examines the theoretical conditions for the weak efficiency concept of forecasting systems for fixed events based on L2-loss and the conditional expectation function. Our main contribution are statistical extensions of existing concepts and their application to German elections between 2000 and 2023.
Dienstag, 21.05.2024, 12-13 Uhr in W9-109
Prof. Dr. Göran Kauermann
Institut für Statistik der Ludwig-Maximilians- Universität München
Statistical Contributions in Conflict Research
The talk introduces to the field of statistical conflict research. We consider armed conflicts in (regions of) Africa with at least one fatality. As data source we make us of the quite reliable data provided by the Peace Research Institut Oslo (PRIO). In the first part of the talk we demonstrate how statical models can be used for predicting future conflicts. We apply a hurdle model, which allows to properly incorporate quantities, relavant from the political science perspective. Secondly, we look at Syria and explore how remote sensing data from satellites can improve prediction accuracy. In particular we include machine learning tools and compare them with statistical approaches. We find the remote sensing can indeed increase the precision of prediction. Finally, we look at the spread of conflicts in time and space, which is often omitted in purely machine learning approaches. The talk ends by posing numerous remaining interesting research questions.
Dienstag, 04.06.2024, 12-13 Uhr in W9-109
Dienstag, 18.06.2024, 12-13 Uhr in W9-109
Dienstag, 02.07.2024, 12-13 Uhr in W9-109
J.-Prof. Dr. Timo Adam
Universität Bielefeld
Titel folgt
Dienstag, 16.07.2024, 12-13 Uhr in W9-109