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.

Highly multiplexed single-molecule FISH has emerged as a promising approach to spatially resolved single-cell transcriptomics because of its ability to directly image and profile numerous RNA species in their native cellular context. However, backgroundfrom off-target binding of FISH probes and cellular autofluorescence-can become limiting in a number of important applications, such as increasing the degree of multiplexing, imaging shorter RNAs, and imaging tissue samples. Here, we developed a sample clearing approach for FISH measurements. We identified off-target binding of FISH probes to cellular components other than RNA, such as proteins, as a major source of background. To remove this source of background, we embedded samples in polyacrylamide, anchored RNAs to this polyacrylamide matrix, and cleared cellular proteins and lipids, which are also sources of autofluorescence. To demonstrate the efficacy of this approach, we measured the copy number of 130 RNA species in cleared samples using multiplexed error-robust FISH (MERFISH). We observed a reduction both in the background because of off-target probe binding and in the cellular autofluorescence without detectable loss in RNA. This process led to an improved detection efficiency and detection limit of MERFISH, and an increased measurement throughput via extension of MERFISH into four color channels. We further demonstrated MERFISH measurements of complex tissue samples from the mouse brain using this matrix-imprinting and -clearing approach. We envision that this method will improve the performance of a wide range of in situ hybridization-based techniques in both cell culture and tissues.tissue clearing | fluorescence in situ hybridization | multiplexed imaging | single-cell transcriptomics | brain S ingle-molecule FISH (smFISH) is a powerful technique that allows the direct imaging of individual RNA molecules within single cells (1, 2). In this approach, RNAs are labeled via the hybridization of fluorescently labeled oligonucleotide probes, producing bright fluorescent spots for single RNA molecules, which reveal both the abundance and the spatial distribution of these RNAs inside cells (1, 2). The ability of smFISH to image gene expression at the single-cell level in both cell culture and tissue has led to exciting advances in our understanding of the natural noise in gene expression and its role in cellular response (3, 4), the intracellular spatial organization of RNAs and its role in posttranscriptional regulation (5, 6), and the spatial variation in gene expression within complex tissues and its role in the molecular definition of cell types and tissue functions (6, 7).To extend the benefits of this technique to systems-level questions and high-throughput gene-expression profiling, approaches to increase the multiplexing of smFISH (i.e., the number of different RNA species that can be simultaneously quantified within the same cell) have been developed (8-13). Most of these approaches take advantage of color multiplexing, which has allowed a few tens...

For designing, monitoring, and analyzing a longitudinal study with an event time as the outcome variable, the restricted mean event time (RMET) is an easily interpretable, clinically meaningful summary of the survival function in the presence of censoring. The RMET is the average of all potential event times measured up to a time point τ and can be estimated consistently by the area under the Kaplan-Meier curve over $[0, \tau ]$. In this paper, we study a class of regression models, which directly relates the RMET to its "baseline" covariates for predicting the future subjects' RMETs. Since the standard Cox and the accelerated failure time models can also be used for estimating such RMETs, we utilize a cross-validation procedure to select the "best" among all the working models considered in the model building and evaluation process. Lastly, we draw inferences for the predicted RMETs to assess the performance of the final selected model using an independent data set or a "hold-out" sample from the original data set. All the proposals are illustrated with the data from the an HIV clinical trial conducted by the AIDS Clinical Trials Group and the primary biliary cirrhosis study conducted by the Mayo Clinic.

Summary For a study with an event time as the endpoint, its survival function contains all the information regarding the temporal , stochastic profile of this outcome variable. The survival probability at a specific time point, say t, however, does not transparently capture the temporal profile of this endpoint up to t. An alternative is to use the restricted mean survival time (RMST) at time t to summarize the profile. The RMST is the mean survival time of all subjects in the study population followed up to t, and is simply the area under the survival curve up to t. The advantages of using such a quantification over the survival rate have been discussed in the setting of a fixed-time analysis. In this article, we generalize this approach by considering a curve based on the RMST over time as an alternative summary to the survival function. Inference, for instance, based on simultaneous confidence bands for a single RMST curve and also the difference between two RMST curves are proposed. The latter is informative for evaluating two groups under an equivalence or non-inferiority setting, and quantifies the difference of two groups in a time scale. The proposal is illustrated with the data from two clinical trials, one from oncology and the other from cardiology.