How to report E-values for meta-analyses: Recommended improvements and additions to the new GRADE approach

What are the benefits of preemptive versus non-preemptive kidney transplantation? A systematic review and meta-analysis

Opting for a preemptive kidney transplant (PKT) can help avoid costs and morbidity associated with dialysis. However, while multiple studies have shown clinical benefits of PKT, other studies have not demonstrated this, leading to controversy in the literature regarding the exact benefits of PKT. Therefore, this study aimed to determine the clinical outcomes of PKT versus non-preemptive kidney transplantation (nPKT) in adult patients. Multiple databases were searched up to May 4, 2022. Independent reviewers selected studies for inclusion and extracted relevant data. Risk of bias was assessed using the Downs and Black checklist. Eighty-seven studies including 859,715 adult kidney transplant patients were included the review. The risk of patient death (relative risk [95% confidence interval] 0.74 [0.60-0.91]) was significantly lower in PKT versus nPKT patients for living donor (LD) transplants, whereas the risk of overall graft loss was significantly lower in PKT compared to nPKT patients for both LD (0.72 [0.62-0.83]) as well as deceased donor (DD) transplants (0.80 [0.69-0.92]). The evidence suggests that LD PKT patients have a lower risk of patient death and graft loss compared to nPKT patients, and DD PKT patients have a lower risk of graft loss than nPKT patients.

The M-Value: A Simple Sensitivity Analysis for Bias Due to Missing Data in Treatment Effect Estimates

Complete-case analyses can be biased if missing data are not missing completely at random. We propose simple sensitivity analyses that apply to complete-case estimates of treatment effects; these analyses use only simple summary data and obviate specifying the precise mechanism of missingness and making distributional assumptions. Bias arises when treatment effects differ between retained and nonretained participants or, among retained participants, the estimate is biased because conditioning on retention has induced a noncausal path between the treatment and outcome. We thus bound the overall treatment effect on the difference scale by specifying: 1) the unobserved treatment effect among nonretained participants; and 2) the strengths of association that unobserved variables have with the exposure and with the outcome among retained participants ("induced confounding associations"). Working with the former sensitivity parameter subsumes certain existing methods of worst-case imputation while also accommodating less-conservative assumptions (e.g., that the treatment is not detrimental on average even among nonretained participants). As an analog to the E-value for confounding, we propose the M-value, which represents, for a specified treatment effect among nonretained participants, the strength of induced confounding associations required to reduce the treatment effect to the null or to any other value. These methods could help characterize the robustness of complete-case analyses to potential bias due to missing data.

Short-term risk of periprocedural stroke relative to radial vs. femoral access: systematic review, meta-analysis, study sequential analysis and meta-regression of 2,188,047 real-world cardiac catheterizations

OBJECTIVES

To verify whether transradial (TRA) compared to transfemoral (TFA) cardiac catheterization reduces the risk of periprocedural stroke (PS).

METHODS

We reviewed (CRD42021277918) published real-world cohorts reporting the incidence of PS within 3 days following diagnostic or interventional catheterization. Meta-analyses and meta-regressions of odds ratios (OR) performed using the DerSimonian and Laird method were checked for publication bias (Egger test) and adjusted for false-positive results (study sequential analysis SSA).

RESULTS

The pooled incidence of PS from 2,188,047 catheterizations (14 cohorts), was 193 (105 to 355) per 100,000. Meta-analyses of adjusted estimates (OR = 0.66 (0.49 to 0.89); p = 0.007; I² = 90%), unadjusted estimates (OR = 0.63 (0.51 to 0.77; I² = 74%; p = 0.000)), and a sub-group of prospective cohorts (OR = 0.67 (0.48 to 0.94; p = 0.022; I² = 16%)) had a lower risk of PS in TRA (without indication of publication bias). SSA confirmed the pooled sample size was sufficient to support these conclusions. Meta-regression decreased the unexplained heterogeneity but did not identify any independent predictor of PS nor any effect modifier.

CONCLUSION

Periprocedural stroke remains a rare and hard-to-predict adverse event associated with cardiac catheterization. TRA is associated with a 20% to 30% lower risk of PS in real-world/common practice settings. Future studies are unlikely to change our conclusion.

E-values for effect heterogeneity and approximations for causal interaction

Background

Estimates of effect heterogeneity (i.e. the extent to which the causal effect of one exposure varies across strata of a second exposure) can be biased if the exposure–outcome relationship is subject to uncontrolled confounding whose severity differs across strata of the second exposure.

Methods

We propose methods, analogous to the E-value for total effects, that help to assess the sensitivity of effect heterogeneity estimates to possible uncontrolled confounding. These E-value analogues characterize the severity of uncontrolled confounding strengths that would be required, hypothetically, to ‘explain away’ an estimate of multiplicative or additive effect heterogeneity in the sense that appropriately controlling for those confounder(s) would have shifted the effect heterogeneity estimate to the null, or alternatively would have shifted its confidence interval to include the null. One can also consider shifting the estimate or confidence interval to an arbitrary non-null value. All of these E-values can be obtained using the R package EValue.

Results

We illustrate applying the proposed E-value analogues to studies on: (i) effect heterogeneity by sex of the effect of educational attainment on dementia incidence and (ii) effect heterogeneity by age on the effect of obesity on all-cause mortality.

Conclusion

Reporting these proposed E-values could help characterize the robustness of effect heterogeneity estimates to potential uncontrolled confounding.