17 papers · ranked by Valyu relevance
Michael J Sweeting, Simon G Thompson
Shared random effects joint models are becoming increasingly popular for investigating the relationship between longitudinal and time-to-event data. Although appealing, such complex models are computationally intensive, and quick, approximate methods may provide a reasonable alternative. In this paper, we first compare…
Shaun R Seaman, Menelaos Pavlou, Andrew J Copas
Clustered data commonly arise in epidemiology. We assume each cluster member has an outcome Y and covariates . When there are missing data in Y, the distribution of Y given in all cluster members (“complete clusters”) may be different from the distribution just in members with observed Y (“observed clusters”). Often…
Stefan Konigorski, Mathias Ried‐Larsen, Christopher H. Schmid
Traditionally, studies in experimental physiology have been conducted in small groups of human participants, animal models or cell lines. Identifying optimal study designs that achieve sufficient power for drawing proper statistical inferences to detect group level effects with small sample sizes has been challenging.…
Christopher Schmid, Jiabei Yang
We describe Bayesian models for data from N-of-1 trials, reviewing both the basics of Bayesian inference and applications to data from single trials and collections of trials sharing the same research questions and data structures. Bayesian inference is natural for drawing inferences from N-of-1 trials because it can…
Tingxuan Wu, Cindy Feng, Longhai Li
This paper compares six different parameter estimation methods for shared frailty models via a series of simulation studies. A shared frailty model is a survival model that incorporates a random effect term, where the frailties are common or shared among individuals within specific groups. Several parameter estimation…
Alexander Volkmann, Nikolaus Umlauf, Sonja Greven
The joint modeling of multiple longitudinal biomarkers together with a time-to-event outcome is a challenging modeling task of continued scientific interest. In particular, the computational complexity of high dimensional (generalized) mixed effects models often restricts the flexibility of shared parameter joint…
Sarah Margaret Urbut, Gao Wang, Matthew Stephens
We introduce new statistical methods for analyzing genomic datasets that measure many effects in many conditions (e.g. gene expression changes under many treatments). These new methods improve on existing methods by allowing for arbitrary correlations among conditions. This flexible approach increases power, improves…
Gulnara R. Svishcheva, Evgeny S. Tiys, Elizaveta E. Elgaeva, Sofia G. Feoktistova + 4 more
We propose a novel effective framework for analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance…
Ruth Walker, Bob Phillips, Sofia Dias, Haitao Pan
There are challenges associated with recruiting children to take part in randomised clinical trials and as a result, compared to adults, in many disease areas we are less certain about which treatments are most safe and effective. This can lead to weaker recommendations about which treatments to prescribe in practice.…
Dylan G.E. Gomes
As (generalized) linear mixed-effects models (GLMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common ‘rule of thumb’ is that one needs at least five levels of a random effect. Having such few levels makes the estimation of the variance of random…
E. Lance Howe, James J. Murphy, Drew Gerkey, Colin Thor West + 1 more
'Sabine Windmann'] Integrating information from existing research, qualitative ethnographic interviews, and participant observation, we designed a field experiment that introduces idiosyncratic environmental risk and a voluntary sharing decision into a standard public goods game. Conducted with subsistence resource…
Verena Zuber, Alex Lewin, Michael G. Levin, Alexander Haglund + 6 more
The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related…
Charles E. McCulloch, John Neuhaus
Statistical models that include random effects are commonly used to analyze longitudinal and correlated data, often with strong and parametric assumptions about the random effects distribution. There is marked disagreement in the literature as to whether such parametric assumptions are important or innocuous. In the…
Guido M. Kuersteiner, Ingmar R. Prucha, Ying Zeng
We study linear peer effects models where peers interact in groups and individual's outcomes are linear in the group mean outcome and characteristics. We allow for unobserved random group effects as well as observed fixed group effects. The specification is in part motivated by the moment conditions imposed in Graham…
Nicholas C. Henderson, Thomas A. Louis, Chenguang Wang, Ravi Varadhan
Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the…
Arvid Sjölander, Sara Öberg, Thomas Frisell
Sibling comparison studies have the attractive feature of being able to control for unmeasured confounding by factors that are shared within families. However, there is sometimes a concern that these studies may have poor generalizability (external validity) due to the implicit restriction to families that are…
Susan Athey, Guido W. Imbens
In this chapter, we present econometric and statistical methods for analyzing randomized experiments. For basic experiments we stress randomization-based inference as opposed to sampling-based inference. In randomization-based inference, uncertainty in estimates arises naturally from the random assignment of the…