Journal of clinical epidemiology
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Propensity score (PS) analysis allows an unbiased estimate of treatment effects but assumes that all confounders are measured. We assessed the impact of omitting confounders from a PS analysis on clinical decision making. ⋯ The omission of strongly negative confounding variables from a PS analysis can lead to incorrect clinical decision making. However, omitting these variables also decreases the analysis power, which may prevent the reporting of significant but misleading effects.
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The objective of this systematic review is to investigate the use of Bayesian data analysis in epidemiology in the past decade and particularly to evaluate the quality of research papers reporting the results of these analyses. ⋯ Though available guidance papers concerned with reporting of Bayesian analyses emphasize the importance of transparent prior specification, the results obtained in this systematic review show that these guidance papers are often not used. Additional efforts should be made to increase the awareness of the existence and importance of these checklists to overcome the controversy with respect to the use of Bayesian techniques. The reporting quality in epidemiological literature could be improved by updating existing guidelines on the reporting of frequentist analyses to address issues that are important for Bayesian data analyses.
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Observational Study
Survival biases lead to flawed conclusions in observational treatment studies of influenza patients.
Several observational studies reported that Oseltamivir (Tamiflu) reduced mortality in infected and hospitalized patients. Because of the restriction of observation to hospital stay and time-dependent treatment assignment, such findings were prone to common types of survival bias (length, time-dependent and competing risk bias). ⋯ The impact of each of the three survival biases was remarkable, and it can make neuraminidase inhibitors appear more effective or even harmful. Incorrect and misclassified risk sets were the primary sources of biased hazard rates.
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There is a growing number of studies evaluating the physical, cognitive, mental health, and health-related quality of life (HRQOL) outcomes of adults surviving critical illness. However, there is little consensus on the most appropriate instruments to measure these outcomes. To inform the development of such consensus, we conducted a systematic review of the performance characteristics of instruments measuring physical, cognitive, mental health, and HRQOL outcomes in adult intensive care unit (ICU) survivors. ⋯ Although an increasing number of studies measure physical, cognitive, mental health, and HRQOL outcomes in adult ICU survivors, data on the measurement properties of such instruments are sparse and generally of poor to fair quality. Empirical analyses evaluating the performance of instruments in adult ICU survivors are needed to advance research in this field.
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To assess the characteristics and core statistical methodology specific to network meta-analyses (NMAs) in clinical research articles. ⋯ Many NMAs published in the medical literature have significant limitations in both the conduct and reporting of the statistical analysis and numerical results. The situation has, however, improved in recent years, in particular with respect to the evaluation of the underlying assumptions, but considerable room for further improvements remains.