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JournalISSN: 0962-2802

Statistical Methods in Medical Research 

SAGE Publishing
About: Statistical Methods in Medical Research is an academic journal published by SAGE Publishing. The journal publishes majorly in the area(s): Medicine & Covariate. It has an ISSN identifier of 0962-2802. Over the lifetime, 2312 publications have been published receiving 84733 citations.


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Journal ArticleDOI
TL;DR: 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 expected to lie.
Abstract: 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 expected to lie. We describe how graphical methods can be used to investigate the assumptions of the method and we also give confidence intervals. We extend the basic approach to data where there is a relationship between difference and magnitude, both with a simple logarithmic transformation approach and a new, more general, regression approach. We discuss the importance of the repeatability of each method separately and compare an estimate of this to the limits of agreement. We extend the limits of agreement approach to data with repeated measurements, proposing new estimates for equal numbers of replicates by each method on each subject, for unequal numbers of replicates, and for replicated data collected in pairs, where the underlying value of the quantity being measured is changing. Finally, we describe a nonparametric approach to comparing methods.

7,976 citations

Journal ArticleDOI
TL;DR: Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
Abstract: 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.

3,387 citations

Journal ArticleDOI
TL;DR: FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable, but its statistical properties are difficult to establish.
Abstract: 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 exist: joint modeling (JM) and fully conditional specification (FCS). JM is based on parametric statistical theory, and leads to imputation procedures whose statistical properties are known. JM is theoretically sound, but the joint model may lack flexibility needed to represent typical data features, potentially leading to bias. FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable. FCS provides tremendous flexibility and is easy to apply, but its statistical properties are difficult to establish. Simulation work shows that FCS behaves very well in ...

2,119 citations

Journal ArticleDOI
TL;DR: This article investigates the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives and proposes estimators capable to serve as “rules of thumb” and will be widely applied in evidence-based medicine.
Abstract: 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 of a treatment effectiveness. The sample mean and standard deviation are two commonly used statistics in meta-analysis but some trials use the median, the minimum and maximum values, or sometimes the first and third quartiles to report the results. Thus, to pool results in a consistent format, researchers need to transform those information back to the sample mean and standard deviation. In this article, we investigate the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives. A major drawback in the literature is that the sample size, needless to say its imp...

1,353 citations

Journal ArticleDOI
TL;DR: It is found that Bonferroni-related tests offer little improvement over Bonferronsi, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom.
Abstract: Functional neuroimaging data embodies a massive multiple testing problem, where 100 000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is ...

1,146 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202368
2022147
2021187
2020242
2019240
2018248