Sensitivity analyses play a crucial role in assessing the robustness of the findings or conclusions based on primary analyses of data in clinical trials. They are a critical way to assess the impact, effect or influence of key assumptions or variations—such as different methods of analysis, definitions of outcomes, protocol deviations, missing data, and outliers—on the overall conclusions of a study. The current paper is the second in a series of tutorial-type manuscripts intended to discuss and clarify aspects related to key methodological issues in the design and analysis of clinical trials. In this paper we will provide a detailed exploration of the key aspects of sensitivity analyses including: 1) what sensitivity analyses are, why they are needed, and how often they are used in practice; 2) the different types of sensitivity analyses that one can do, with examples from the literature; 3) some frequently asked questions about sensitivity analyses; and 4) some suggestions on how to report the results of sensitivity analyses in clinical trials. When reporting on a clinical trial, we recommend including planned or posthoc sensitivity analyses, the corresponding rationale and results along with the discussion of the consequences of these analyses on the overall findings of the study.

There is increasing evidence regarding the relationship between metabolic syndrome and mortality. However, previous research examining metabolic syndrome and mortality in older populations has produced mixed results. In addition, there is a clear need to identify and manage individual components of metabolic syndrome to decrease cardiovascular disease (CVD) mortality. In this meta-analysis, we searched the MEDLINE databases using PubMed, Cochrane Library, and EMBASE databases. Based on 20 prospective cohort studies, metabolic syndrome was associated with a higher risk of all-cause mortality [relative risk (RR), 1.23; 95% confidence interval (CI), 1.15–1.32; I2 = 55.9%] and CVD mortality (RR, 1.24; 95% CI, 1.11–1.39; I2 = 58.1%). The risk estimates of all-cause mortality for single components of metabolic syndrome were significant for higher values of waist circumference or body mass index (RR, 0.94; 95% CI, 0.88–1.00), higher values of blood glucose (RR, 1.19; 95% CI, 1.05–1.34), and lower values of high-density lipoprotein (HDL) cholesterol (RR, 1.11; 95% CI, 1.02–1.21). In the elderly population, metabolic syndrome was associated with an increased risk of all-cause and CVD mortality. Among the individual components of metabolic syndrome, increased blood glucose and HDL cholesterol levels were significantly associated with increased mortality. However, older obese or overweight individuals may have a decreased mortality risk. Thus, the findings of the current meta-analysis raise questions about the utility of the definition of metabolic syndrome in predicting all-cause mortality and CVD mortality in the elderly population.

The Comet assay has gained increasing popularity for use in human biomonitoring or epidemiologic studies; however, one of the shortcomings of the Comet assay is a lack of agreement on a single appropriate Comet parameter that is capable of adequately describing observed DNA damages. Among the tail parameters of Comet features, the most frequently used are the tail moments (both the Olive tail moment and the extent tail moment), the tail DNA, and the tail length. Some studies comparing Comet parameters have been found in cell toxicity research, but there are few comparative studies that use human biomonitoring or epidemiologic data. In this study, we evaluate those four tail parameters in both high and low DNA damaged cells with the use of epidemiologic data. To do this, a new graphical approach, the so-called quantile dispersion graphs (QDGs) are used. In a comparison of an exposed group and a control group, either the tail moment or tail DNA is preferable to the tail length. With respect to providing smaller variability in quantiles for the amount of DNA damage, however, the tail moment is the preferred parameter for both groups. Moreover, the tail moment provides the most stable estimates for DNA damage because it has a larger degree of uniformity in quantile dispersions. To study high degrees of damage from toxic exposure using B cells or G cells, however, the tail DNA showed more significant discrepancies than the other parameters, in terms of both the mean differences and the graphical differences between the two groups. In view of this result, it is suggested that both the tail moment and the tail DNA be presented as tail parameters in human biomonitoring studies.

Vitamin D deficiency and frailty are common with aging. Previous studies examining vitamin D status and frailty have produced mixed results, and in particular, the shape of the association has not been well established. We examined the association between 25-hydroxyvitamin D (25OHD) serum levels and frailty by performing a systematic review and dose-response meta-analysis. We searched the PubMed, EMBASE and Cochrane Library databases of Elsevier through February 2017. Cross-sectional and cohort studies that reported adjusted risk ratios with 95% confidence intervals (CI) for frailty with ≥3 categories of 25OHD serum levels were selected. Data extraction was performed independently by two authors. The reported risk estimates for 25OHD categories were recalculated, employing a comprehensive trend estimation from summarized dose-response data. The pooled risk estimate of frailty syndrome per 25 nmol/L increment in serum 25OHD concentration was 0.88 (95% CI = 0.82–0.95, I2 = 86.8%) in the 6 cross-sectional studies and 0.89 (95% CI = 0.85–0.94, I2 = 0.0%) in the 4 prospective cohort studies. Based on the Akaike information criteria (AIC), a linear model was selected (AIC for the nonlinear model: − 5.4, AIC for the linear model: − 6.8 in the prospective cohort studies; AIC for the linear model: − 13.6, AIC for the nonlinear model: − 1.77 in the cross-sectional studies). This dose-response meta-analysis indicates that serum 25OHD levels are significantly and directly associated with the risk of frailty. Further studies should address the underlying mechanisms to explain this relationship and to determine whether vitamin D supplementation is effective for preventing frailty syndrome.