On approximate equality of Z-values of the statistical tests for win statistics (win ratio, win odds, and net benefit).

Dong et al. (2023) showed that the win statistics (win ratio, win odds, and net benefit) can complement each another to demonstrate the strength of treatment effects in randomized trials with prioritized multiple outcomes. This result was built on the connections among the point and variance estimates of the three statistics, and the approximate equality of Z-values in their statistical tests.

Evaluating the Clinical Utility of Epstein-Barr Virus Antibodies as Biomarkers in Multiple Sclerosis: A Systematic Review.

Background: EBV is a necessary but not sufficient factor in the pathophysiology of multiple sclerosis (MS). EBV antibodies to the nuclear antigen (EBNA1) and viral capsid antigen (VCA) rise rapidly prior to MS disease manifestations, and their absence has clinical utility with a high negative predictive value. It remains unclear whether EBV levels act as prognostic, monitoring, or pharmacodynamic/response biomarkers.

Estimation of heterogeneity variance based on a generalized Q statistic in meta-analysis of log-odds-ratio.

For estimation of heterogeneity variance in meta-analysis of log-odds-ratio, we derive new mean- and median-unbiased point estimators and new interval estimators based on a generalized statistic, , in which the weights depend on only the studies' effective sample sizes. We compare them with familiar estimators based on the inverse-variance-weights version of , In an extensive simulation, we studied the bias (including median bias) of the point estimators and the coverage (including left and right coverage error) of the confidence intervals. Most estimators add to each cell of the table when one cell contains a zero count; we include a version that always adds .

On the Q statistic with constant weights in meta-analysis of binary outcomes.

Background: Cochran's Q statistic is routinely used for testing heterogeneity in meta-analysis. Its expected value (under an incorrect null distribution) is part of several popular estimators of the between-study variance, [Formula: see text]. Those applications generally do not account for use of the studies' estimated variances in the inverse-variance weights that define Q (more explicitly, [Formula: see text]).

The stratified win statistics (win ratio, win odds, and net benefit).

The win odds and the net benefit are related directly to each other and indirectly, through ties, to the win ratio. These three win statistics test the same null hypothesis of equal win probabilities between two groups. They provide similar p-values and powers, because the Z-values of their statistical tests are approximately equal.

The win odds: statistical inference and regression.

Generalized pairwise comparisons and win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials.

Win statistics (win ratio, win odds, and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes.

Conventional analyses of a composite of multiple time-to-event outcomes use the time to the first event. However, the first event may not be the most important outcome. To address this limitation, generalized pairwise comparisons and win statistics (win ratio, win odds, and net benefit) have become popular and have been applied to clinical trial practice.

On the Q statistic with constant weights for standardized mean difference.

Cochran's Q statistic is routinely used for testing heterogeneity in meta-analysis. Its expected value is also used in several popular estimators of the between-study variance, . Those applications generally have not considered the implications of its use of estimated variances in the inverse-variance weights.

Exploring consequences of simulation design for apparent performance of methods of meta-analysis.

Contemporary statistical publications rely on simulation to evaluate performance of new methods and compare them with established methods. In the context of random-effects meta-analysis of log-odds-ratios, we investigate how choices in generating data affect such conclusions. The choices we study include the overall log-odds-ratio, the distribution of probabilities in the control arm, and the distribution of study-level sample sizes.

A Q statistic with constant weights for assessing heterogeneity in meta-analysis.

The conventional Q statistic, using estimated inverse-variance (IV) weights, underlies a variety of problems in random-effects meta-analysis. In previous work on standardized mean difference and log-odds-ratio, we found superior performance with an estimator of the overall effect whose weights use only group-level sample sizes. The Q statistic with those weights has the form proposed by DerSimonian and Kacker.

Adjusting win statistics for dependent censoring.

For composite outcomes whose components can be prioritized on clinical importance, the win ratio, the net benefit and the win odds apply that order in comparing patients pairwise to produce wins and subsequently win proportions. Because these three statistics are derived using the same win proportions and they test the same hypothesis of equal win probabilities in the two treatment groups, we refer to them as win statistics. These methods, particularly the win ratio and the net benefit, have received increasing attention in methodological research and in design and analysis of clinical trials.

Estimation in meta-analyses of response ratios.

Background: For outcomes that studies report as the means in the treatment and control groups, some medical applications and nearly half of meta-analyses in ecology express the effect as the ratio of means (RoM), also called the response ratio (RR), analyzed in the logarithmic scale as the log-response-ratio, LRR.

Methods: In random-effects meta-analysis of LRR, with normal and lognormal data, we studied the performance of estimators of the between-study variance, τ, (measured by bias and coverage) in assessing heterogeneity of study-level effects, and also the performance of related estimators of the overall effect in the log scale, λ. We obtained additional empirical evidence from two examples.

The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring.

The win ratio method has received much attention in methodological research, ad hoc analyses, and designs of prospective studies. As the primary analysis, it supported the approval of tafamidis for the treatment of cardiomyopathy to reduce cardiovascular mortality and cardiovascular-related hospitalization. However, its dependence on censoring is a potential shortcoming.

Methods for estimating between-study variance and overall effect in meta-analysis of odds ratios.

In random-effects meta-analysis the between-study variance ( τ ) has a key role in assessing heterogeneity of study-level estimates and combining them to estimate an overall effect. For odds ratios the most common methods suffer from bias in estimating τ and the overall effect and produce confidence intervals with below-nominal coverage. An improved approximation to the moments of Cochran's Q statistic, suggested by Kulinskaya and Dollinger (KD), yields new point and interval estimators of τ and of the overall log-odds-ratio.

Estimation in meta-analyses of mean difference and standardized mean difference.

Methods for random-effects meta-analysis require an estimate of the between-study variance, τ . The performance of estimators of τ (measured by bias and coverage) affects their usefulness in assessing heterogeneity of study-level effects and also the performance of related estimators of the overall effect. However, as we show, the performance of the methods varies widely among effect measures.

The win ratio: Impact of censoring and follow-up time and use with nonproportional hazards.

The win ratio has been studied methodologically and applied in data analysis and in designing clinical trials. Researchers have pointed out that the results depend on follow-up time and censoring time, which are sometimes used interchangeably. In this article, we distinguish between follow-up time and censoring time, show theoretically the impact of censoring on the win ratio, and illustrate the impact of follow-up time.

Pitfalls of using the risk ratio in meta-analysis.

For meta-analysis of studies that report outcomes as binomial proportions, the most popular measure of effect is the odds ratio (OR), usually analyzed as log(OR). Many meta-analyses use the risk ratio (RR) and its logarithm because of its simpler interpretation. Although log(OR) and log(RR) are both unbounded, use of log(RR) must ensure that estimates are compatible with study-level event rates in the interval (0, 1).

Automatic extraction of quantitative data from ClinicalTrials.gov to conduct meta-analyses.

Objectives: Systematic reviews and meta-analyses are labor-intensive and time-consuming. Automated extraction of quantitative data from primary studies can accelerate this process. ClinicalTrials.

Efficacy and safety of direct oral anticoagulants approved for cardiovascular indications: Systematic review and meta-analysis.

Background: Direct oral anticoagulants (DOACs) have emerged as promising alternatives to vitamin K antagonists (VKAs) for patients with non-valvular atrial fibrillation (NVAF) or venous thromboembolism (VTE). Few meta-analyses have included all DOACs that have received FDA approval for these cardiovascular indications, and their overall comparisons against VKAs have shortcomings in data and methods. We provide an updated overall assessment of the efficacy and safety of those DOACs at dosages currently approved for NVAF or VTE, in comparison with VKAs.