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The potential association between bivalent COVID-19 vaccination and ischemic stroke remains uncertain, despite several studies conducted thus far. This study aimed to evaluate the risk of ischemic stroke following bivalent COVID-19 vaccination during the 2022-2023 season. A self-controlled case series study was conducted among members aged 12 years and older who experienced ischemic stroke between September 1, 2022, and March 31, 2023, in a large health care system. Ischemic strokes were identified using International Classification of Diseases, Tenth Revision codes in emergency departments and inpatient settings. Exposures were Pfizer-BioNTech or Moderna bivalent COVID-19 vaccination. Risk intervals were prespecified as 1-21 days and 1-42 days after bivalent vaccination; all non-risk-interval person-time served as the control interval. The incidence of ischemic stroke was compared in the risk interval and control interval using conditional Poisson regression. We conducted overall and subgroup analyses by age, history of SARS-CoV-2 infection, and coadministration of influenza vaccine. When an elevated risk was detected, we performed a chart review of ischemic strokes and analyzed the risk of chart-confirmed ischemic stroke. With 4933 ischemic stroke events, we found no increased risk within the 21-day risk interval for the 2 vaccines and by subgroups. However, risk of ischemic stroke was elevated within the 42-day risk interval among individuals aged younger than 65 years with coadministration of Pfizer-BioNTech bivalent and influenza vaccines on the same day; the relative incidence (RI) was 2.13 (95% CI 1.01-4.46). Among those who also had a history of SARS-CoV-2 infection, the RI was 3.94 (95% CI 1.10-14.16). After chart review, the RIs were 2.34 (95% CI 0.97-5.65) and 4.27 (95% CI 0.97-18.85), respectively. Among individuals aged younger than 65 years who received Moderna bivalent vaccine and had a history of SARS-CoV-2 infection, the RI was 2.62 (95% CI 1.13-6.03) before chart review and 2.24 (95% CI 0.78-6.47) after chart review. Stratified analyses by sex did not show a significantly increased risk of ischemic stroke after bivalent vaccination. While the point estimate for the risk of chart-confirmed ischemic stroke was elevated in a risk interval of 1-42 days among individuals younger than 65 years with coadministration of Pfizer-BioNTech bivalent and influenza vaccines on the same day and among individuals younger than 65 years who received Moderna bivalent vaccine and had a history of SARS-CoV-2 infection, the risk was not statistically significant. The potential association between bivalent vaccination and ischemic stroke in the 1-42-day analysis warrants further investigation among individuals younger than 65 years with influenza vaccine coadministration and prior SARS-CoV-2 infection. Furthermore, the findings on ischemic stroke risk after bivalent COVID-19 vaccination underscore the need to evaluate monovalent COVID-19 vaccine safety during the 2023-2024 season.

To update previous Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidance by addressing inconsistencies and interpreting subgroup analyses. Using an iterative process, we consulted with members of the GRADE working group through multiple rounds of written feedback and discussions at GRADE working group meetings. The guidance complements previous guidance with clarification in two areas: (1) assessing inconsistency and (2) assessing the credibility of possible effect modifiers that might explain inconsistency. Specifically, the guidance clarifies that inconsistency refers to variability in results, not in study characteristics; that inconsistency assessment for binary outcomes requires consideration of both relative and absolute effects; how to decide between narrower and broader questions in systematic reviews and guidelines; that, with the same evidence, ratings of inconsistency may differ depending on the target of certainty rating; and how GRADE inconsistency ratings relate to a statistical measure of inconsistency I2 depending on the context in which one views results. The second part of the guidance illustrates, based on a worked example, the use of the instrument to assess the credibility of effect modification analyses. The guidance explains the stepwise process of moving from a subgroup analysis to assessing the credibility of effect modification and, if found credible, to subgroup-specific effect estimates and GRADE certainty ratings. This updated guidance addresses specific conceptual and practical issues that systematic review authors frequently face when considering the degree of inconsistency in estimates of treatment effects across studies. •When assessing inconsistency, GRADE users should consider both relative and absolute effects.•Ratings of inconsistency may differ depending on the target of certainty rating.•Reviewers need to understand contexts when I2 is large, but inconsistency is not important.•The ICEMAN instrument for assessing the credibility of subgroup effects is fully compatible with GRADE's approach to rating inconsistency.

Subgroup analysis is the process of comparing a treatment effect for two or more variants of an intervention—to ask, for example, if an intervention’s impact is affected by the setting (school versus community), by the delivery agent (outside facilitator versus regular classroom teacher), by the quality of delivery, or if the long-term effect differs from the short-term effect. While large-scale studies often employ subgroup analyses, these analyses cannot generally be performed for small-scale studies, since these typically include a homogeneous population and only one variant of the intervention. This limitation can be bypassed by using meta-analysis. Meta-analysis allows the researcher to compare the treatment effect in different subgroups, even if these subgroups appear in separate studies. We discuss several statistical issues related to this procedure, including the selection of a statistical model and statistical power for the comparison. To illustrate these points, we use the example of a meta-analysis of obesity prevention.

Latent class analysis (LCA) offers a powerful analytical approach for categorizing groups (or “classes”) within a heterogenous population. LCA identifies these hidden classes by a set of predefined features, known as “indicators”. Unlike many other grouping analytical approaches, LCA derives classes using a probabilistic approach. In this first paper, we describe the common applications of LCA, and outline its advantages over other analytical subgrouping methods.

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. Supplementary materials for this article are available online.

Latent class analysis (LCA) is an analytical approach for the identification of more homogeneous subgroups within an otherwise dissimilar patient population. In the current paper, Part II, we present a practical step-by-step guide for LCA of clinical data, including when LCA might be applied, selecting indicator variables, and choosing a final class solution. We also identify common pitfalls of LCA, and related solutions.

In a meta-analysis, a question always arises. Is it worthwhile to combine estimates from studies of different populations using various formulations of an intervention, evaluating outcomes measured differently? Sometimes even study designs differ. Differences are expected in a meta-analysis. These may be negligible, and a pooled estimate of effect can guide the clinical decision. However, when the differences are large, this estimate may mislead. Effect estimates from study to study differ because of real differences (between-study variability) and because of chance (within-study variability). To combine estimates when there is heterogeneity (between-study differences are large) may not be sensible. Two complementary methods may be used to detect heterogeneity: visual inspection of the forest plot and calculating numerical measures of heterogeneity (I2 and Q). Visual inspection can show effects that are different from the rest. A large I2 (proportion of overall variability attributed to between-study variation) or a small P-value associated with Q may suggest heterogeneity. Large P-values, however, do not mean the absence of heterogeneity. It is more informative to report the confidence interval of the I2. If there is no heterogeneity, a pooled estimate of the true effect may be generated using only within-study variation (fixed-effect model). If there is substantial heterogeneity, reasons should be sought. Subgroup analysis or meta-regression using study-level characteristics may be done. Although more involved and potentially challenging, individual-level data (Individual Participant Data, IPD) may also be used. In the case of unexplained heterogeneity, both within- and between-study variation should be used to generate a pooled estimate (random-effects model). This estimate does not estimate a single true effect but estimates the average of a range of effects of the intervention on populations represented by the studies. If precise enough (narrow confidence interval), this estimate, together with the prediction interval (a measure of uncertainty in the effect one might see in a particular context), can guide clinical and policy decisions. •While differences are expected in a meta-analysis, these may be negligible, and a pooled estimate can guide the clinical decision. However, when the differences are large, this estimate may mislead.•The danger of reporting pooled estimates is that readers may overlook the overall picture—some studies having bigger effects than the other studies, some effects with different directions (harm) from the benefit shown by most studies. A careful inspection of the forest plot can help detect these differences; we refer to as heterogeneity.•Visual inspection should be used together with measures of heterogeneity–I2 and Q. High values of I2 and small P-values associated with Q may suggest heterogeneity. But large P-values do not mean the absence of heterogeneity. It is more informative to report the confidence interval of I2.•If heterogeneity is detected, an explanation must be sought, and analysis using study-level characteristics (subgroup analysis or meta-regression) may be done. Although intensive, analysis using individual-level data (Individual Participant Data) may also be done.•In case of unexplained heterogeneity, a pooled estimate using the random-effects model may be used. This estimate no longer estimates a single unknown effect but the average of the effects of the intervention in the populations represented by the studies. If precise enough (narrow confidence interval), this estimate, together with the prediction interval (a measure of uncertainty in the effect one might see in a particular context), can guide clinical and policy decisions.

We present an illustrative application of methods that account for covariates in receiver operating characteristic (ROC) curve analysis, using individual patient data on D-dimer testing for excluding pulmonary embolism. Bayesian nonparametric covariate-specific ROC curves were constructed to examine the performance/positivity thresholds in covariate subgroups. Standard ROC curves were constructed. Three scenarios were outlined based on comparison between subgroups and standard ROC curve conclusion: (1) identical distribution/identical performance, (2) different distribution/identical performance, and (3) different distribution/different performance. Scenarios were illustrated using clinical covariates. Covariate-adjusted ROC curves were also constructed. Age groups had prominent differences in D-dimer concentration, paired with differences in performance (Scenario 3). Different positivity thresholds were required to achieve the same level of sensitivity. D-dimer had identical performance, but different distributions for YEARS algorithm items (Scenario 2), and similar distributions for sex (Scenario 1). For the later covariates, comparable positivity thresholds achieved the same sensitivity. All covariate-adjusted models had AUCs comparable to the standard approach. Subgroup differences in performance and distribution of results can indicate that the conventional ROC curve is not a fair representation of test performance. Estimating conditional ROC curves can improve the ability to select thresholds with greater applicability.

Traffic accidents cause considerable economic losses and injuries. Although the adverse effects of a change in ambient temperatures on human health have been widely documented, its effects on road traffic safety are still debated. This systematic review and meta-analysis was performed to synthesize available data on the association between ambient temperature and the risks of road traffic accidents (RTAs) and traffic accident injuries (TAIs). We searched 7 different databases to locate studies. The subgroup analyses were stratified by temperature type, temperature exposure, region, mean temperature, mortality, study period, statistical model, and source of injury data. This study was registered with PROSPERO under the number CRD42021264660. This is the first meta-analysis to investigate the association between ambient temperature and road traffic safety. A total of 34 high-temperature effect estimates were reported, and two additional studies reported the relationship between low temperatures and TAI risk. The meta-analysis results found a significant association between the high temperature and RTAs, and the pooled RR was 1.025 (95%CI 1.014, 1.035). The risk of TAI was also significantly associated with temperature increases. Subgroup analyses found that using daily mean temperatures, the RR value of road traffic accidents was 1.024 (95%CI 0.939, 1.116), and the RR value of road traffic injuries was 1.052 (95%CI 1.024, 1.080). Hourly temperatures significantly increased the risk of RTA, while the risk of TAI was not significantly increased by hourly temperature. The sensitivity analysis indicated that the results were stable, and no obvious publication bias was detected. The results of this systematic review and meta-analysis suggest that increases in ambient temperature are associated with an increased risk of RTAs and TAIs. These findings add to the evidence of the impact of ambient temperature on road traffic safety. Graphical abstract

The overall goal of this study is to introduce latent class analysis (LCA) as an alternative approach to latent subgroup analysis. Traditionally, subgroup analysis aims to determine whether individuals respond differently to a treatment based on one or more measured characteristics. LCA provides a way to identify a small set of underlying subgroups characterized by multiple dimensions which could, in turn, be used to examine differential treatment effects. This approach can help to address methodological challenges that arise in subgroup analysis, including a high Type I error rate, low statistical power, and limitations in examining higher-order interactions. An empirical example draws on N  = 1,900 adolescents from the National Longitudinal Survey of Adolescent Health. Six characteristics (household poverty, single-parent status, peer cigarette use, peer alcohol use, neighborhood unemployment, and neighborhood poverty) are used to identify five latent subgroups: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches for examining differential treatment effects are demonstrated using a simulated outcome: 1) a classify-analyze approach and, 2) a model-based approach based on a reparameterization of the LCA with covariates model. Such approaches can facilitate targeting future intervention resources to subgroups that promise to show the maximum treatment response.