1,241 research outputs found
MATS: Inference for potentially Singular and Heteroscedastic MANOVA
In many experiments in the life sciences, several endpoints are recorded per
subject. The analysis of such multivariate data is usually based on MANOVA
models assuming multivariate normality and covariance homogeneity. These
assumptions, however, are often not met in practice. Furthermore, test
statistics should be invariant under scale transformations of the data, since
the endpoints may be measured on different scales. In the context of
high-dimensional data, Srivastava and Kubokawa (2013) proposed such a test
statistic for a specific one-way model, which, however, relies on the
assumption of a common non-singular covariance matrix. We modify and extend
this test statistic to factorial MANOVA designs, incorporating general
heteroscedastic models. In particular, our only distributional assumption is
the existence of the group-wise covariance matrices, which may even be
singular. We base inference on quantiles of resampling distributions, and
derive confidence regions and ellipsoids based on these quantiles. In a
simulation study, we extensively analyze the behavior of these procedures.
Finally, the methods are applied to a data set containing information on the
2016 presidential elections in the USA with unequal and singular empirical
covariance matrices
Debt rule federalism: the case of Germany
In 2009, Germany introduced a new debt rule in its federal constitution (Grundgesetz). The socalled
‘debt brake’ prescribes a balanced budget for both the federal level and the states. However,
the states have leeway regarding transposition and specification of the national requirements
into their own state constitutions and budgetary laws. This analysis presents a comprehensive
comparison of the 16 state provisions. We develop an indicator which quantifies the
stringency of state rules (Strength of Fiscal Rule Indicator). Two results emerge: First, despite
the common constitutional rule at the federal level, the analysis reveals a considerable heterogeneity
across German states. Second, several highly indebted states miss the chance to make
their fiscal regime more credible. This finding corresponds to the disincentives of the German
federation. Due to bailout-guarantees enshrined in German federalism, German states do not
have incentives to impress bond markets through particularly strict budgetary rules
Causal inference methods for small non-randomized studies: methods and an application in COVID-19
The usual development cycles are too slow for the development of vaccines,
diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2
pandemic. Given the pressure in such a situation, there is a risk that findings
of early clinical trials are overinterpreted despite their limitations in terms
of size and design. Motivated by a non-randomized open-label study
investigating the efficacy of hydroxychloroquine in patients with COVID-19, we
describe in a unified fashion various alternative approaches to the analysis of
non-randomized studies and apply them to the example study exploring the
question whether different methods might have led to different conclusions. A
widely used tool to reduce the impact of treatment-selection bias are so-called
propensity score (PS) methods. Conditioning on the propensity score allows one
to replicate the design of a randomized controlled trial, conditional on
observed covariates. Extensions include the doubly robust g-computation, which
is less frequently applied, in particular in clinical studies. Here, we
investigate the properties of propensity score based methods including
g-computation in small sample settings, typical for early trials, in a
simulation study. We conclude that the doubly robust g-computation has some
desirable properties and should be more frequently applied in clinical
research. In the hydroxychloroquine study, g-computation resulted in a very
wide confidence interval indicating much uncertainty. We speculate that
application of the method might have prevented some of the hype surrounding
hydroxychloroquine in the early stages of the SARS-CoV-2 pandemic. R code for
the g-computation is provided
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