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Methodologic considerations in the design and analysis of nested case-control studies: association between cytokines and postoperative delirium

Overview of attention for article published in BMC Medical Research Methodology, June 2017
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Title
Methodologic considerations in the design and analysis of nested case-control studies: association between cytokines and postoperative delirium
Published in
BMC Medical Research Methodology, June 2017
DOI 10.1186/s12874-017-0359-8
Pubmed ID
Authors

Long H. Ngo, Sharon K. Inouye, Richard N. Jones, Thomas G. Travison, Towia A. Libermann, Simon T. Dillon, George A. Kuchel, Sarinnapha M. Vasunilashorn, David C. Alsop, Edward R. Marcantonio

Abstract

The nested case-control study (NCC) design within a prospective cohort study is used when outcome data are available for all subjects, but the exposure of interest has not been collected, and is difficult or prohibitively expensive to obtain for all subjects. A NCC analysis with good matching procedures yields estimates that are as efficient and unbiased as estimates from the full cohort study. We present methodological considerations in a matched NCC design and analysis, which include the choice of match algorithms, analysis methods to evaluate the association of exposures of interest with outcomes, and consideration of overmatching. Matched, NCC design within a longitudinal observational prospective cohort study in the setting of two academic hospitals. Study participants are patients aged over 70 years who underwent scheduled major non-cardiac surgery. The primary outcome was postoperative delirium from in-hospital interviews and medical record review. The main exposure was IL-6 concentration (pg/ml) from blood sampled at three time points before delirium occurred. We used nonparametric signed ranked test to test for the median of the paired differences. We used conditional logistic regression to model the risk of IL-6 on delirium incidence. Simulation was used to generate a sample of cohort data on which unconditional multivariable logistic regression was used, and the results were compared to those of the conditional logistic regression. Partial R-square was used to assess the level of overmatching. We found that the optimal match algorithm yielded more matched pairs than the greedy algorithm. The choice of analytic strategy-whether to consider measured cytokine levels as the predictor or outcome-- yielded inferences that have different clinical interpretations but similar levels of statistical significance. Estimation results from NCC design using conditional logistic regression, and from simulated cohort design using unconditional logistic regression, were similar. We found minimal evidence for overmatching. Using a matched NCC approach introduces methodological challenges into the study design and data analysis. Nonetheless, with careful selection of the match algorithm, match factors, and analysis methods, this design is cost effective and, for our study, yields estimates that are similar to those from a prospective cohort study design.

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Mendeley readers

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The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 91 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 15%
Student > Master 14 15%
Student > Ph. D. Student 12 13%
Other 10 11%
Student > Bachelor 6 7%
Other 18 20%
Unknown 18 20%
Readers by discipline Count As %
Medicine and Dentistry 28 30%
Nursing and Health Professions 11 12%
Agricultural and Biological Sciences 4 4%
Neuroscience 4 4%
Psychology 4 4%
Other 14 15%
Unknown 27 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 April 2018.
All research outputs
#17,898,929
of 22,979,862 outputs
Outputs from BMC Medical Research Methodology
#1,692
of 2,028 outputs
Outputs of similar age
#226,916
of 317,259 outputs
Outputs of similar age from BMC Medical Research Methodology
#24
of 34 outputs
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