Sensitivity, which denotes the proportion of subjects correctly given a positive assignment out of all subjects who are actually positive for the outcome, indicates how well a test can classify subjects who truly have the outcome of interest. Specificity, which denotes the proportion of subjects correctly given a negative assignment out of all subjects who are actually negative for the outcome, indicates how well a test can classify subjects who truly do not have the outcome of interest. Positive predictive value reflects the proportion of subjects with a positive test result who truly have the outcome of interest. Negative predictive value reflects the proportion of subjects with a negative test result who truly do not have the outcome of interest. Sensitivity and specificity are inversely related, wherein one increases as the other decreases, but are generally considered stable for a given test, whereas positive and negative predictive values do inherently vary with pre-test probability (e.g., changes in population disease prevalence). This article will further detail the concepts of sensitivity, specificity, and predictive values using a recent real-world example from the medical literature.

Blinding mitigates several sources of bias which, if left unchecked, can quantitively affect study outcomes. Blinding remains under-utilized, particularly in non-pharmaceutical clinical trials, but is often highly feasible through simple measures. Although blinding is generally viewed as an effective method by which to eliminate bias, blinding does also pose some inherent limitations, and it behooves clinicians and researchers to be aware of such caveats. This article will review general principles for blinding in clinical trials, including examples of useful blinding techniques for both pharmaceutical and non-pharmaceutical trials, while also highlighting the limitations and potential consequences of blinding. Appropriate reporting on blinding in trial protocols and manuscripts, as well as future directions for blinding research, will also be discussed.

Evidence-based medicine is predicated on the integration of best available research evidence with clinical expertise and patient values to inform care. In medical research, several distinct measures are commonly used to describe the associations between variables, and a sound understanding of these pervasive measures is foundational in the clinician’s ability to interpret, synthesize, and apply available evidence from the medical literature. Accordingly, this article aims to provide an educational tutorial/topic primer on some of the most ubiquitous measures of association and risk quantification in medical research, including odds ratios, relative risk, absolute risk, and number needed to treat, using several real-world examples from the medical literature.

Left ventricular hypertrophy (LVH) is an independent predictor of major cardiovascular events. Cardiovascular risk is increased among human immunodeficiency virus (HIV)-infected patients. To assess LV mass/hypertrophy in HIV infection, 654 women enrolled in the Women's Interagency HIV Study underwent transthoracic echocardiography. There were 454 HIV-infected and 200 uninfected women, mean age 40.8 ± 9.3 years. LV mass/height^2.7 was similar between the HIV-infected and the HIV-uninfected groups (41.4 ± 11.1 vs. 39.9 ± 10.3 g/h^2.7; p = 0.37). The prevalence of LVH was similar between the two groups (LVH by LV mass/height^2.7 criteria 15.0% vs. 13.0%, p = 0.29). Relative wall thickness (RWT), defined as the ratio of LV wall thickness to cavity diameter, was also similar between the HIV-infected and HIV-uninfected groups (0.36 ± 0.05 vs. 0.37 ± 0.06, p = 0.16). On multiple linear regression analysis adjusting for age, W/H ratio, triceps skinfold thickness, systolic/diastolic BP, diabetes, hypertension and dyslipidemia; HIV status (b = 2.08, p = 0.02, CI 0.27–3.88); weight (b per kg = 0.15, p < 0.01, CI 0.08–0.22); and smoking duration (b per one-year increase = 0.08, p = 0.03, CI 0.01–0.16) were independent correlates of LV mass/height^2.7 (Model R² = 0.20, p < 0.001). Weight (aOR = 1.04, CI 1.01–1.06) and smoking duration (aOR = 1.03, CI 1.01–1.06) were independent correlates of LVH. Being HIV negative, increased age, increased triceps skinfold thickness, and higher W/H ratio were independent correlates of higher RWT. Among HIV-infected women, higher LV mass was not associated with a history of AIDS-defining illness, nadir CD4⁺ count <200 cells/μl, or with the duration of highly active antiretroviral therapy (HAART). Women taking NRTIs had higher LV mass. Higher RWT was associated with current CD4⁺ count. In conclusion, HIV infection is associated with greater LV mass but not with a higher prevalence of LVH. Among HIV-infected women, RWT, but not LV mass, is associated with the degree of immunosuppression.

Sphingomyelin (SM) is an abundant phospholipid in cell membranes and in lipoproteins. In human plasma, SM is mainly found in atherogenic lipoproteins; therefore, higher levels of SM may promote atherogenesis. We investigated the relations between plasma SM levels and the presence of angiographic coronary heart disease (CHD) and left ventricular systolic dysfunction. We studied 732 patients referred for coronary angiography. Median SM levels were higher among patients with CHD and in those with LV systolic dysfunction (LVEF<50%) than in patients without CHD or LV dysfunction. SM levels were significantly correlated with fibrinogen levels, diabetes, apoB, and triglyceride levels. On multivariate analyses, higher median SM levels were associated with a higher risk of CHD and lower LV ejection fraction. The pro-atherogenic property of plasma SM might be related to 1) CHD; 2) LV systolic dysfunction; and 3) metabolism of apoB-containing or triglyceride-rich lipoproteins.