In a longitudinal clinical study to compare two groups, the primary end point is often the time to a specific event (eg, disease progression, death). The hazard ratio estimate is routinely used to empirically quantify the between-group difference under the assumption that the ratio of the two hazard functions is approximately constant over time. When this assumption is plausible, such a ratio estimate may capture the relative difference between two survival curves. However, the clinical meaning of such a ratio estimate is difficult, if not impossible, to interpret when the underlying proportional hazards assumption is violated (ie, the hazard ratio is not constant over time). Although this issue has been studied extensively and various alternatives to the hazard ratio estimator have been discussed in the statistical literature, such crucial information does not seem to have reached the broader community of health science researchers. In this article, we summarize several critical concerns regarding this conventional practice and discuss various well-known alternatives for quantifying the underlying differences between groups with respect to a time-to-event end point. The data from three recent cancer clinical trials, which reflect a variety of scenarios, are used throughout to illustrate our discussions. When there is not sufficient information about the profile of the between-group difference at the design stage of the study, we encourage practitioners to consider a prespecified, clinically meaningful, model-free measure for quantifying the difference and to use robust estimation procedures to draw primary inferences.

Objective The APOE4 allele is the strongest genetic risk factor for sporadic Alzheimer’s disease (AD). Case-control studies suggest the APOE4 link to AD is stronger in women. We examined the APOE4-by-sex interaction in conversion risk (from healthy aging to mild cognitive impairment (MCI)/AD or from MCI to AD) and cerebrospinal fluid (CSF) biomarker levels. Methods Cox proportional hazards analysis was used to compute hazards ratios (HR) for an APOE-by-sex interaction on conversion in controls (N=5,496) and MCI patients (N=2,588). The interaction was also tested in CSF biomarker levels of 980 subjects from the AD Neuroimaging Initiative. Results Among controls, male and female carriers were more likely to convert to MCI/AD, but the effect was stronger in women (HR=1.81 women; HR=1.27 men; interaction P=0.0106). The interaction remained significant in a pre-defined sub-analysis restricted to APOE3/3 and APOE3/4 genotypes. Among MCI patients, male and female carriers were more likely to convert to AD (HR=2.16 women; HR=1.64 men). The effect was nominally stronger in women, but the interaction was not significant (P=0.136). In the sub-analysis restricted to APOE3/3 and APOE 3/4 genotypes, the interaction was significant (P= 0.022; HR=2.17 women; HR=1.51 men). The APOE4-by-sex interaction on biomarker levels was significant for MCI patients in total-tau and the tau-to-Abeta-ratio (P=0.0088 and P=0.020, respectively; more AD-like in women). Interpretation APOE4 confers greater AD risk in women. Biomarker results suggest that increased APOE-related risk in women may be associated with tau pathology. These findings have important clinical implications and suggest novel research approaches into AD pathogenesis.

We consider a setting in which we have a treatment and a potentially large number of covariates for a set of observations, and wish to model their relationship with an outcome of interest. We propose a simple method for modeling interactions between the treatment and covariates. The idea is to modify the covariate in a simple way, and then fit a standard model using the modified covariates and no main effects. We show that coupled with an efficiency augmentation procedure, this method produces clinically meaningful estimators in a variety of settings. It can be useful for practicing personalized medicine: determining from a large set of biomarkers the subset of patients that can potentially benefit from a treatment. We apply the method to both simulated datasets and real trial data. The modified covariates idea can be used for other purposes, for example, large scale hypothesis testing for determining which of a set of covariates interact with a treatment variable.

Shirai et al. show that the glycolytic enzyme PKM2 serves as a molecular integrator of metabolic dysfunction, oxidative stress and tissue inflammation in macrophages from patients with atherosclerotic coronary artery disease.

To promote their pathology, CD4 T-cells from patients with rheumatoid arthritis (RA) have to clonally expand and differentiate into cytokine-producing effector cells. In contrast to healthy T-cells, naïve RA T-cells have a defect in glycolytic flux due to upregulation of glucose-6-phosphate dehydrogenase (G6PD). Excess G6PD shunts glucose into the pentose phosphate pathway (PPP), resulting in NADPH accumulation and ROS consumption. With surplus reductive equivalents, RA T-cells insufficiently activate the redox-sensitive kinase ATM; bypass the G2/M cell cycle checkpoint and hyperproliferate. Insufficient ATM activation biases T-cell differentiation towards the Th1 and Th17 lineages, imposing a hyper-inflammatory phenotype. We have identified several interventions that replenishing intracellular ROS, correct the abnormal proliferative behavior of RA T-cells and successfully suppress synovial inflammation. Rebalancing glucose utilization and restoring oxidant signaling may provide a novel therapeutic strategy to prevent autoimmunity in RA.