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Statistical Methods in Medical Research is a peer-reviewed journal published by SAGE, covering Statistical Methods and Bayesian Inference, Statistical Methods and Inference, Statistical Methods in Clinical Trials. Over its lifetime it has published 3,262 papers, which have received 120,494 citations.
| Publisher | SAGE |
|---|---|
| Reference style | Numbered (Vancouver) Numbered — [1], [2] in the text 1. Smith A, Jones B, Lee C. A representative article title. Statistical Methods in Medical Research. 2023;12(3):45–58.
Formats any DOI in Statistical Methods in Medical Research style. No sign-up. |
| Publishes research in | Statistical Methods and Bayesian Inference Statistical Methods and Inference Statistical Methods in Clinical Trials Advanced Causal Inference Techniques Health Systems, Economic Evaluations, Quality of Life |
| ISSN | 0962-2802 |
| Citation impact (2-yr) | 1.78 |
| h-index | 142 |
| i10-index | 1,405 |
| Top institutions publishing here | Harvard University |
| Journal website | smm.sagepub.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.
Agreement between two methods of clinical measurement can be quantified using the differences between observations made using the two methods on the same subjects. The 95% limits of agreement, estimated by mean difference +/- 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are…
The era of big data is coming, and evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well designed and conducted clinical research. Meta-analysis is a statistical technique widely used in evidence-based medicine for analytically combining the findings from independent clinical trials to provide an overall estimation…
In recent years, multiple imputation has emerged as a convenient and flexible paradigm for analysing data with missing values. Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data. Two approaches for imputing multivariate data…
