The phrase "approach for sensitivity" is not correct and may be unclear in written English.
It could be used in contexts discussing methods or strategies to assess or address sensitivity, but it lacks clarity.
Example: "We need to develop an approach for sensitivity analysis in our research."
Alternatives: "method for sensitivity" or "strategy for sensitivity".
The statistical literature is rich with methods for handling incomplete data, including approaches for sensitivity analysis and MNAR data.
Thirdly, researchers should develop systematic approaches for sensitivity analyses that assess the influence of the various assumptions commonly made.
By studying agreement between different strategies via sensitivity analyses in the statistical report, such as simple tests for the primary analysis together with modeling approaches for sensitivity analyses, it is possible to explore robustness and accurate estimation of the treatment effects.
The model assumes a bivariate normal distribution for the logit-transformed sensitivity and specificity across studies, using a random effects approach for both sensitivity and specificity, allowing for heterogeneity beyond chance due to clinical or methodological differences between studies.
In this article new approach for high sensitivity measurements of electric surface properties using scanning probe microscopy is presented.
This study presents a semi-analytical approach for the sensitivity analysis of the response of linear discretized structures subjected to stationary multi-correlated Gaussian random excitation.
The model uses a random effects approach for both sensitivity and specificity, allowing for heterogeneity beyond chance due to clinical and methodological differences among studies.
The bivariate model uses a random effects approach for both sensitivity and specificity, allowing for heterogeneity beyond chance due to clinical or methodological differences between studies.
However, SAS (SAS/STAT 13.1 [ 27]) and R (SensMice package [ 28]) have recently introduced the pattern-mixture approach for performing sensitivity analyses to the MAR assumption.
