Hu, Liangyuan Gu, Chenyang Lopez, Michael Ji, Jiayi Wisnivesky, Juan
Published in
Statistical methods in medical research
There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been propose...
Ge, Xinyi Peng, Yingwei Tu, Dongsheng
Published in
Statistical methods in medical research
Identification of a subset of patients who may be sensitive to a specific treatment is an important problem in clinical trials. In this paper, we consider the case where the treatment effect is measured by longitudinal outcomes, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuo...
Sampson, Joshua N Gail, Mitchell H
Published in
Statistical methods in medical research
We provide methods to estimate the confidence interval for the difference between two relative risks. Letting p0, p1, and p2 be the probabilities of an event in three groups (i.e. control, treatment 1, treatment 2), our methods estimate a confidence interval for r = p1/p0 - p2/p0. We highlight that our methods can handle small sample sizes, covaria...
Kim, Byung-Jun Kim, Inyoung
Published in
Statistical methods in medical research
The matched case-crossover study design is used in public health, biomedical, and epidemiological research with clustered binary outcomes. Conditional logistic regression is commonly used for analysis because any effects associated with the matching covariates by stratum can be removed. However, some matching covariates often play an important role...
Li, Yang Wang, Fan Li, Rong Sun, Yifan
Published in
Statistical methods in medical research
In genomic analysis, it is significant though challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and the characteristics of datasets, we propose a novel integrative interaction approach under a semiparametric model, in which genetic and environmental factors are included as t...
Stark, Maria Zapf, Antonia
Published in
Statistical methods in medical research
In a confirmatory diagnostic accuracy study, sensitivity and specificity are considered as co-primary endpoints. For the sample size calculation, the prevalence of the target population must be taken into account to obtain a representative sample. In this context, a general problem arises. With a low or high prevalence, the study may be overpowered...
Lee, Donghwan Choi, Dongseok Lee, Youngjo
Published in
Statistical methods in medical research
In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster...
Graf, Alexandra Christine Magirr, Dominic Dmitrienko, Alex Posch, Martin
Published in
Statistical methods in medical research
An important step in the development of targeted therapies is the identification and confirmation of sub-populations where the treatment has a positive treatment effect compared to a control. These sub-populations are often based on continuous biomarkers, measured at baseline. For example, patients can be classified into biomarker low and biomarker...
Ugidos, Manuel Tarazona, Sonia Prats-Montalbán, José M Ferrer, Alberto Conesa, Ana
Published in
Statistical methods in medical research
Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in publ...
Nikoloulopoulos, Aristidis K
Published in
Statistical methods in medical research
Diagnostic test accuracy studies observe the result of a gold standard procedure that defines the presence or absence of a disease and the result of a diagnostic test. They typically report the number of true positives, false positives, true negatives and false negatives. However, diagnostic test outcomes can also be either non-evaluable positives ...
