Write in a clean editor, then format for Biostatistics & Epidemiology in one click — DocuGuru applies the official Taylor & Francis template with author–year references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Biostatistics & Epidemiology is a peer-reviewed journal published by Taylor & Francis, covering Statistical Methods and Inference, Statistical Methods and Bayesian Inference, Advanced Causal Inference Techniques.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Biostatistics & Epidemiology 12 (3): 45–58.
Formats any DOI in Biostatistics & Epidemiology style. No sign-up. |
| Publishes research in | Statistical Methods and Inference Statistical Methods and Bayesian Inference Advanced Causal Inference Techniques Statistical Methods in Clinical Trials COVID-19 epidemiological studies |
| ISSN | 2470-9360 |
| Citation impact (2-yr) | 0.38 |
| h-index | 13 |
| i10-index | 18 |
| Total citations | 1,634 |
| Top institutions publishing here | University of Washington |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent advances regarding confidence bands and geometric/topological features. We begin with a discussion of basic properties of KDE: the convergence rate under various metrics, density derivative estimation, and bandwidth selection. Then, we introduce common approaches to the construction of confidence intervals/bands, and we…
Quasi-experiments are similar to randomized controlled trials in many respects, but there are many challenges in designing and conducting a quasi-experiment when internal validity threats are introduced from the absence of randomization. This paper outlines design, measurement and statistical issues that must be considered prior to the conduct of a quasi-experimental evaluation. We discuss challenges…
Statistical modeling methods are widely used in clinical science, epidemiology, and health services research to analyze data that has been collected in clinical trials as well as observational studies of existing data sources, such as claims files and electronic health records. Diagnostic and prognostic inferences from statistical models are critical to researchers advancing science, clinical…
Longitudinal changes in a population of interest are often heterogeneous and influenced by a combination of baseline factors. In such cases, classical linear mixed effects models [Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974.] for the mean structure provide poor fit to the data. We propose regression tree methodology for the longitudinal…
Causal inference refers to the process of inferring what would happen in the future if we change what we are doing, or inferring what would have happened in the past, if we had done something different in the distant past. Humans adjust our behaviors by anticipating what will happen if we act in different ways,…
