A Bayesian nonparametric meta-analysis model for estimating the reference interval

A reference interval represents the normative range for measurements from a healthy population. It plays an important role in laboratory testing, as well as in differentiating healthy from diseased patients. The reference interval based on a single study might not be applicable to a broader population. Meta-analysis can provide a more generalizable reference interval based on the combined population by synthesizing results from multiple studies. However, the assumptions of normally distributed underlying study-specific means and equal within-study variances, which are commonly used in existing methods, are strong and may not hold in practice. We propose a Bayesian nonparametric model with more flexible assumptions to extend random effects meta-analysis for estimating reference intervals. We illustrate through simulation studies and two real data examples the performance of our proposed approach when the assumptions of normally distributed study means and equal within-study variances do not hold.

Hierarchical models that address measurement error are needed to evaluate the correlation between treatment effect and control group event rate

OBJECTIVE

To apply a hierarchical model (HM) that addresses measurement error in regression of the treatment effect on the control group event rate (CR). We compare HM to weighted linear regression (WLR) which is subject to measurement error and mathematical coupling.

STUDY DESIGN AND SETTING

We reviewed published hierarchical models that address measurement error and implemented a Bayesian version in open-source code to facilitate adoption by meta-analysts. We compared WLR and HM across a very large convenience sample of meta-analyses published in the Cochrane Database of Systematic Reviews.

RESULTS

We applied both approaches (WLR and a HM that addresses measurement error) to 3,193 meta-analyses that included 33,071 studies (average 10.28 studies per meta-analysis). A statistically significant slope suggesting an association between the treatment effect and CR was demonstrated with both approaches in 568 (17.19%) meta-analyses, with neither approach in 2,036 (63.77%) meta-analyses, only with WLS in 229 (7.17%) and only with HM in 360 (11.28%) meta-analyses. The majority of slopes were negative (WLR 85%, HM 83%). In the majority of cases, HM had wider confidence intervals (72.53%) and slopes farther from the null (64.77%).

CONCLUSION

Approximately 28% of meta-analyses demonstrate a significant association between the treatment effect and CR when HM is used to address measurement error. User-friendly open-source code is provided to meta-analysts interested in exploring this association.

Heat Stroke Induces Pyroptosis in Spermatogonia via the cGAS-STING Signaling Pathway

To explore the mechanism whereby cGAS-STING pathway regulates the pyroptosis of cryptorchidism cells, with a view to finding a new strategy for clinically treating cryptorchidism-induced infertility. Spermatogonial GC-1 cells were heat stimulated to simulate the heat hurt microenvironment of cryptorchidism. The cell viability was assayed by CCK-8, and cellular DNA damage was detected by gamma-H2AX immunofluo-rescence assay. Flow cytometry was employed to assess pyroptosis index, while western blot, ELISA and PCR were used to examine the expressions of pyroptosis-related proteins (Caspase-1, IL-1beta, NLRP3) and cGAS-STING pathway proteins (cGAS, STING). After STING silencing by siRNA, the expressions of pyroptosis-related proteins were determined. Pyroptosis occurred after heat stimulation of cells. Morphological detection found cell swelling and karyopyknosis. According to the gamma-H2AX immunofluorescence (IFA) assay, the endonuclear green fluorescence was significantly enhanced, the gamma-H2AX content markedly increased, and the endonuclear DNA was damaged. Flow cytometry revealed a significant increase in pyroptosis index. Western blot and PCR assays showed that the expressions of intracellular pyrogenic proteins like Caspase-1, NLRP3 and GSDMD were elevated. The increased STING protein and gene expressions in cGAS-STING pathway suggested that the pathway was intracellularly activated. Silencing STING protein in cGAS-STING pathway led to significantly inhibited pyroptosis. These results indicate that cGAS-STING pathway plays an important role in heat stress-induced pyroptosis of spermatogonial cells. After heat stimulation of spermatogonial GC-1 cells, pyroptosis was induced and cGAS-STING pathway was activated. This study can further enrich and improve the molecular mechanism of cryptorchidism.

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials

BACKGROUND

Empirical evidence suggests that lack of blinding may be associated with biased estimates of treatment benefit in randomized controlled trials, but the influence on medication-related harms is not well-recognized. We aimed to investigate the association between blinding and clinical trial estimates of medication-related harms.

METHODS

We searched PubMed from January 1, 2015, till January 1, 2020, for systematic reviews with meta-analyses of medication-related harms. Eligible meta-analyses must have contained trials both with and without blinding. Potential covariates that may confound effect estimates were addressed by restricting trials within the comparison or by hierarchical analysis of harmonized groups of meta-analyses (therefore harmonizing drug type, control, dosage, and registration status) across eligible meta-analyses. The weighted hierarchical linear regression was then used to estimate the differences in harm estimates (odds ratio, OR) between trials that lacked blinding and those that were blinded. The results were reported as the ratio of OR (ROR) with its 95% confidence interval (CI).

RESULTS

We identified 629 meta-analyses of harms with 10,069 trials. We estimated a weighted average ROR of 0.68 (95% CI: 0.53 to 0.88, P < 0.01) among 82 trials in 20 meta-analyses where blinding of participants was lacking. With regard to lack of blinding of healthcare providers or outcomes assessors, the RORs were 0.68 (95% CI: 0.53 to 0.87, P < 0.01 from 81 trials in 22 meta-analyses) and 1.00 (95% CI: 0.94 to 1.07, P = 0.94 from 858 trials among 155 meta-analyses) respectively. Sensitivity analyses indicate that these findings are applicable to both objective and subjective outcomes.

CONCLUSIONS

Lack of blinding of participants and health care providers in randomized controlled trials may underestimate medication-related harms. Adequate blinding in randomized trials, when feasible, may help safeguard against potential bias in estimating the effects of harms.

Postextraction ridge preservation by using dense PTFE membranes: A systematic review and meta-analysis

STATEMENT OF PROBLEM

The use of dense polytetrafluoroethylene (dPTFE) membranes in alveolar ridge preservation may help reduce the risk of bacterial contamination and infection, maintaining the soft-tissue anatomy. However, systematic reviews on their efficacy in postextraction sites are lacking.

PURPOSE

The purpose of this systematic review and meta-analysis was to assess the efficacy of alveolar ridge preservation with dPTFE membranes when used alone or in combination with bone grafting materials in postextraction sites.

MATERIAL AND METHODS

An electronic search up to February 2021 was conducted by using PubMed, Embase, and the Cochrane library to detect studies using dPTFE membranes in postextraction sites. An additional manual search was performed in relevant journals. Clinical and radiographic dimensional changes of the alveolar ridge, histomorphometric, microcomputed tomography, implant-related findings, and rate of complications were recorded. One-dimensional meta-analysis was performed to calculate the overall means and 95% confidence intervals (α=.05).

RESULTS

A total of 23 studies, 14 randomized controlled trials, 4 retrospective cohort studies, 3 case series, and 2 prospective nonrandomized clinical trials, met the inclusion criteria. Five studies were included in the quantitative analysis. The meta-analysis revealed that the use of dPTFE membranes resulted in a statistically significant (P=.042) increase in clinical keratinized tissue of 3.49 mm (95% confidence interval [CI]: 0.16, 6.83) when compared with extraction alone. Metaregression showed that the difference of 1.10 mm (95% CI: -0.14, 2.35) in the radiographic horizontal measurements was not significant (P=.082), but the difference of 1.06 mm (95% CI: 0.51, 1.62) in the radiographic vertical dimensional change between dPTFE membranes+allograft and extraction alone was statistically significant (P<.001).

CONCLUSIONS

The use of dPTFE membranes was better than extraction alone in terms of keratinized tissue width and radiographic vertical bone loss.

Network meta analysis to predict the efficacy of an approved treatment in a new indication

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

A comprehensive review and shiny application on the matching-adjusted indirect comparison

Population-adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head-to-head trials are unavailable. Three commonly used PAIC methods include matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta-regression (ML-NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user-friendly R Shiny application Shiny-MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny-MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny-MAIC application, enabling a user-friendly approach conducting MAIC for healthcare decision-making. The Shiny-MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/.

Double-Negative Results Matter: A Reevaluation of Sensitivities for Detecting SARS-CoV-2 Infection Using Saliva Versus Nasopharyngeal Swabs

In a recent systematic review, Bastos et al. (Ann Intern Med. 2021;174(4):501-510) compared the sensitivities of saliva sampling and nasopharyngeal swabs in the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by assuming a composite reference standard defined as positive if either test is positive and negative if both tests are negative (double negative). Even under a perfect specificity assumption, this approach ignores the double-negative results and risks overestimating the sensitivities due to residual misclassification. In this article, we first illustrate the impact of double-negative results in the estimation of the sensitivities in a single study, and then propose a 2-step latent class meta-analysis method for reevaluating both sensitivities using the same published data set as that used in Bastos et al. by properly including the observed double-negative results. We also conduct extensive simulation studies to compare the performance of the proposed method with Bastos et al.'s method for varied levels of prevalence and between-study heterogeneity. The results demonstrate that the sensitivities are overestimated noticeably using Bastos et al.'s method, and the proposed method provides a more accurate evaluation with nearly no bias and close-to-nominal coverage probability. In conclusion, double-negative results can significantly impact the estimated sensitivities when a gold standard is absent, and thus they should be properly incorporated.

A matching-adjusted indirect comparison of the efficacy of elranatamab versus physician's choice of treatment in patients with triple-class exposed/refractory multiple myeloma

INTRODUCTION

For patients with triple-class exposed/refractory multiple myeloma (TCE/R MM), prognosis is poor and effective treatment options are limited. Elranatamab is a novel B-cell maturation antigen (BCMA)- and CD3-directed bispecific antibody which was approved by the US Food and Drug Administration in August 2023 and demonstrated safety and efficacy in patients with TCE/R MM in the phase 2, single-arm MagnetisMM-3 trial (NCT04649359). To compare the effectiveness of elranatamab vs physician's choice of treatment (PCT) in the absence of head-to-head comparative data, a matching-adjusted indirect comparison (MAIC) was conducted.

METHODS

Individual patient data from MagnetisMM-3 (Cohort A [BCMA-naïve] N = 123, 14.7 months of follow-up) were reweighted to match published summary data from two real-world studies of PCT in patients with TCE/R MM (LocoMMotion and MAMMOTH) using a propensity score-type logistic regression. Unanchored MAIC analyses were conducted according to National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) 18 guidance.

RESULTS

Compared with PCT in LocoMMotion, elranatamab was associated with a significantly higher objective response rate (ORR rate difference: 37.52; 95% CI 26.20-48.83; odds ratio: 4.85; 95% CI 2.85-8.23) and complete or stringent complete response rate (≥CR rate difference: 42.29; 95% CI 31.84-52.74; odds ratio: 184.01; 95% CI 24.66-1372.86), longer progression-free survival (PFS HR 0.32; 95% CI 0.20-0.49), and overall survival (OS HR 0.62; 95% CI 0.40-0.94). Compared with PCT in MAMMOTH, elranatamab was associated with significantly higher ORR (rate difference: 28.14; 95% CI 16.77-39.52; odds ratio: 3.24; 95% CI 1.98-5.32) and ≥ CR (rate difference: 26.22; 95% CI 16.40-36.05; odds ratio: 5.48; 95% CI 2.88-10.44), as well as longer PFS (HR 0.25; 95% CI 0.17-0.37) and OS (HR 0.49; 95% CI 0.33-0.71). Sensitivity analysis results were consistent with the base case.

CONCLUSION

In the MAIC, elranatamab was consistently associated with improved rates and depth of response and significantly longer PFS and OS versus PCT in LocoMMotion and MAMMOTH.

Association Between Maternal Depression and Lower Urinary Tract Symptoms in Their Primary School-Age Daughters: A Birth Cohort Study

PURPOSE

Although maternal depression is associated with adverse outcomes in women and children, its relationship with lower urinary tract symptoms (LUTS) in offspring is less well-characterized. We examined the association between prenatal and postpartum maternal depression and LUTS in primary school-age daughters.

DESIGN

Observational cohort study.

SUBJECTS AND SETTING

The sample comprised 7148 mother-daughter dyads from the Avon Longitudinal Study of Parents and Children.

METHOD

Mothers completed questionnaires about depressive symptoms at 18 and 32 weeks' gestation and 21 months postpartum and their children's LUTS (urinary urgency, nocturia, and daytime and nighttime wetting) at 6, 7, and 9 years of age. Multivariable logistic regression models were used to estimate the association between maternal depression and LUTS in daughters.

RESULTS

Compared to daughters of mothers without depression, those born to mothers with prenatal and postpartum depression had higher odds of LUTS, including urinary urgency (adjusted odds ratio [aOR] range = 1.99-2.50) and nocturia (aOR range = 1.67-1.97) at 6, 7, and 9 years of age. Additionally, daughters born to mothers with prenatal and postpartum depression had higher odds of daytime wetting (aOR range = 1.81-1.99) and nighttime wetting (aOR range = 1.63-1.95) at 6 and 7 years of age. Less consistent associations were observed for depression limited to the prenatal or postpartum periods only.

CONCLUSIONS

Exposure to maternal depression in the prenatal and postpartum periods was associated with an increased likelihood of LUTS in daughters. This association may be an important opportunity for childhood LUTS prevention. Prevention strategies should reflect an understanding of potential biological and environmental mechanisms through which maternal depression may influence childhood LUTS.

Causally interpretable meta-analysis: Clearly defined causal effects and two case studies

Meta-analysis is commonly used to combine results from multiple clinical trials, but traditional meta-analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently-introduced causally interpretable meta-analysis methods and apply their treatment effect estimators to two individual-participant data sets. These estimators transport estimated treatment effects from studies in the meta-analysis to a specified target population using the individuals' potentially effect-modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated-data meta-analysis methods. In our applications, certain versions of the causally interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally interpretable approach gives meta-analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.

The prognostic value of Eastern Cooperative Oncology Group performance status on overall survival among patients with metastatic prostate cancer: a systematic review and meta-analysis

Background

There is heterogeneity in the literature regarding the strength of association between Eastern Cooperative Oncology Group performance status (ECOG PS) and mortality. We conducted a systematic review and meta-analysis of studies reporting the prognostic value of ECOG PS on overall survival (OS) in metastatic prostate cancer (mPC).

Methods

PubMed was searched from inception to March 21, 2022. A meta-analysis pooling the effect of ECOG PS categories (≥2 vs. <2, 2 vs. <2, and ≥1 vs. <1) on OS was performed separately for studies including patients with metastatic castration-resistant prostate cancer (mCRPC) and metastatic castration-sensitive prostate cancer (mCSPC) using a random-effects model. Analyses were stratified by prior chemotherapy and study type.

Results

Overall, 75 studies, comprising 32,298 patients, were included. Most studies (72/75) included patients with mCRPC. Higher ECOG PS was associated with a significant increase in mortality risk, with the highest estimate observed among patients with mCRPC with an ECOG PS of ≥2 versus <2 (hazard ratio [HR]: 2.10, 95% confidence interval [CI]: 1.87-2.37). When stratifying by study type, there was a higher risk estimate of mortality among patients with mCRPC with an ECOG PS of ≥1 versus <1 in real-world data studies (HR: 1.98, 95% CI: 1.72-2.26) compared with clinical trials (HR: 1.32, 95% CI: 1.13-1.54; p < 0.001). There were no significant differences in the HR of OS stratified by previous chemotherapy.

Conclusion

ECOG PS was a significant predictor of OS regardless of category, previous chemotherapy, and mPC population. Additional studies are needed to better characterize the effect of ECOG PS on OS in mCSPC.

Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19

When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS-CoV-2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS-CoV-2 may still have double-negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study-level covariates. We evaluate the model performance using an example based on a recently published meta-analysis on the diagnosis of SARS-CoV-2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta-analytic framework.

Regulatory agilities impacting review timelines for Pfizer/BioNTech's BNT162b2 mRNA COVID-19 vaccine: a retrospective study

The appropriate use of regulatory agilities has the potential to accelerate regulatory review, utilize resources more efficiently and deliver medicines and vaccines more rapidly, all without compromising quality, safety and efficacy. This was clearly demonstrated during the COVID-19 pandemic where regulators and industry rapidly adapted to ensure continued supply of existing critical medicines and review and approve new innovative medicines. In this retrospective study, we analyze the impact of regulatory agilities on the review and approval of Pfizer/BioNTech's BNT162b2 mRNA COVID-19 Vaccine globally using regulatory approval data from 73 country/regional approvals. We report on the critical role of reliance and provide evidence that demonstrates reliance approaches and certain regulatory agilities reduced review times for the COVID-19 vaccine. These findings support the case for more widespread implementation of regulatory agilities and demonstrate the important role of such approaches to improve public health outcomes.

Proposed triggers for retiring a living systematic review

Living systematic reviews (LSRs) are systematic reviews that are continually updated, incorporating relevant new evidence as it becomes available. LSRs are critical for decision-making in topics where the evidence continues to evolve. It is not feasible to continue to update LSRs indefinitely; however, guidance on when to retire LSRs from the living mode is not clear. We propose triggers for making such a decision. The first trigger is to retire LSRs when the evidence becomes conclusive for the outcomes that are required for decision-making. Conclusiveness of evidence is best determined based on the GRADE certainty of evidence construct, which is more comprehensive than solely relying on statistical considerations. The second trigger to retire LSRs is when the question becomes less pertinent for decision-making as determined by relevant stakeholders, including people affected by the problem, healthcare professionals, policymakers and researchers. LSRs can also be retired from a living mode when new studies are not anticipated to be published on the topic and when resources become unavailable to continue updating. We describe examples of retired LSRs and apply the proposed approach using one LSR about adjuvant tyrosine kinase inhibitors in high-risk renal cell carcinoma that we retired from a living mode and published its last update.

Unintended placement of a double-J stent in the contralateral renal pelvis during laparoscopic pyeloplasty for pediatric hydronephrosis: a case report

BACKGROUND

The double-J stent (DJS) is a commonly used ureteral stent in urological surgeries, which provides support and drainage. However, the DJS may result in various complications such as infection, hematuria, stone formation, stent occlusion, and migration. Normally, one end of the DJS is located in the renal pelvis, and the other end in the bladder. In this case report, we describe the rare occurrence of a misplaced DJS during laparoscopic pyeloplasty, which was unintentionally placed in the contralateral renal pelvis.

CASE REPORT

A 4-month-old male infant was diagnosed with left hydronephrosis. After confirmation of the diagnosis, laparoscopic left pyeloplasty was performed with the placement of a DJS. The patient did not experience any discomfort, such as nausea, vomiting, refusal to feed, crying and restlessness, or fever, after the operation, and was discharged on postoperative day 4. The patient returned to the hospital for DJS removal 6 weeks after the operation. However, the kidneys, ureters, and bladder (KUB) X-ray examination showed that the DJS was unintentionally placed in the contralateral ureter and renal pelvis. The stent was confirmed and removed under cystoscopy. Postoperative examination of the DJS showed that there was a hole in the side of the middle of the stent for urine drainage, with no obstruction or contralateral hydronephrosis.

CONCLUSIONS

Misplacement of a DJS in the contralateral renal pelvis during laparoscopic pyeloplasty is a rare but potentially serious complication. Surgeons should be cautious when placing the stent and confirm its placement with imaging studies. Patients should be closely monitored for postoperative complications and prompt intervention should be taken if necessary.

The association of sensitivity and specificity with disease prevalence: analysis of 6909 studies of diagnostic test accuracy

BACKGROUND

Sensitivity and specificity are characteristics of a diagnostic test and are not expected to change as the prevalence of the target condition changes. We sought to evaluate the association between prevalence and changes in sensitivity and specificity.

METHODS

We retrieved data from meta-analyses of diagnostic test accuracy published in the Cochrane Database of Systematic Reviews (2003-2020). We used mixed-effects random-intercept linear regression models to evaluate the association between prevalence and logit-transformed sensitivity and specificity. The model evaluated all meta-analyses as nested within each systematic review.

RESULTS

We analyzed 6909 diagnostic test accuracy studies from 552 meta-analyses that were included in 92 systematic reviews. For sensitivity, compared with the lowest quartile of prevalence, the second, third and fourth quartiles were associated with significantly higher odds of identifying a true positive case (odds ratio [OR] 1.17, 95% confidence interval [CI] 1.09-1.26; OR 1.32, 95% CI 1.23-1.41; OR 1.47, 95% CI 1.37-1.58; respectively). For specificity, compared with the lowest quartile of prevalence, the second, third and fourth quartiles were associated with significantly lower odds of identifying a true negative case (OR 0.74, 95% CI 0.69-0.80; OR 0.65, 95% CI 0.60-0.70; OR 0.47, 95% CI 0.44-0.51; respectively). Pooled regression coefficients from bivariate models conducted within each meta-analysis showed that prevalence was positively associated with sensitivity and negatively associated with specificity. Findings were consistent across subgroups.

INTERPRETATION

In this large sample of diagnostic studies, higher prevalence was associated with higher estimated sensitivity and lower estimated specificity. Clinicians should consider the implications of disease prevalence and spectrum when interpreting the results from studies of diagnostic test accuracy.

An improved Bayesian approach to estimating the reference interval from a meta-analysis: Directly monitoring the marginal quantiles and characterizing their uncertainty

Reference intervals, or reference ranges, aid medical decision-making by containing a pre-specified proportion (e.g., 95%) of the measurements in a representative healthy population. We recently proposed three approaches for estimating a reference interval from a meta-analysis based on a random effects model: a frequentist approach, a Bayesian posterior predictive interval, and an empirical approach. Because the Bayesian posterior predictive interval becomes wider to incorporate estimation uncertainty, it may systematically contain greater than 95% of measurements when the number of studies is small or the between study heterogeneity is large. The frequentist and empirical approaches also captured a median of less than 95% of measurements in this setting, and 95% confidence or credible intervals for the reference interval limits were not developed. In this update, we describe how one can instead use Bayesian methods to summarize the appropriate quantiles (e.g., 2.5th and 97.5th) of the marginal distribution of individuals across studies and construct a credible interval describing the estimation uncertainty in the lower and upper limits of the reference interval. We demonstrate through simulations that this method performs well in capturing 95% of values from the marginal distribution and maintains a median coverage of near 95% of the marginal distribution even when the number of studies is small, or the between-study heterogeneity is large. We also compare the results of this method to those obtained from the three previously proposed methods in the original case study of the meta-analysis of frontal subjective postural vertical measurements.

Influencing factors of laparoscopic pelvic urethroplasty in the treatment of children with hydronephrosis

OBJECTIVE

The purpose of this study was to evaluate the clinical efficacy of laparoscopic pyeloureteroplasty in the treatment of children suffering from hydronephrosis.

PATIENTS AND METHODS

Our pediatric department received 160 children with hydronephrosis from January 2019 through December 2021. These children were randomly assigned to either the control group or the study group with 80 cases in each group. The control group underwent traditional open pyeloureteroplasty, while the study group underwent laparoscopic pyeloureteroplasty. After assessing the results of both groups, the clinical outcomes were compared.

RESULTS

The study group had a significantly shorter operating time, lower intraoperative bleeding rate, and shorter hospital stay than the control group. On the first day after the operation, there was no significant difference between the control and study groups, and on the seventh day after the operation, the study group's OPS was significantly lower than that of the control group. A significant difference was observed after treatment between the study group and the control group in terms of the anteroposterior diameter of the renal pelvis. Both groups' GFR increased significantly with time, and the GFR of the study group was significantly greater than that of the control group at 3 months after the operation, but there was no significant difference at 6 months after the operation. Postoperative adverse effects did not differ significantly between the two groups.

CONCLUSIONS

Pediatric laparoscopic pyeloureteroplasty can reduce intraoperative bleeding, shorten operation time and hospital stay, alleviate postoperative pain, and promote the recovery of postoperative renal morphology and function in children with hydronephrosis, which merits further discussion.

Sexual Health Behaviors by Age 17 and Lower Urinary Tract Symptoms at Age 19: PLUS Research Consortium Analysis of ALSPAC Data

PURPOSE

We examined how antecedent sexual health factors affect lower urinary tract symptoms (LUTS) in adolescent women.

METHODS

We analyzed 1,941 adolescent women from the Avon Longitudinal Study of Parents and Children at age 19. At ages 15 and 17, participants reported use of oral contraceptives (OCs), history of sexual intercourse, number of sexual partners, and condom use. At age 19, The Bristol Female Lower Urinary Tract Symptoms questionnaire quantified the frequency over the past month: stress incontinence, any incontinence, urgency, sensation of incomplete emptying, bladder pain, and urinary tract infection. Multivariable regression models examined associations between sexual health behaviors reported at ages 15 and 17 and six LUTS reported at age 19, after controlling for covariates.

RESULTS

Commonly reported LUTS at age 19 were past-month stress incontinence (26.8%), bladder pain (26.3%), any urine leakage (22.1%), and urinary tract infection (15.4%). OC use by age 17 was associated with urgency (odds ratio [OR] = 1.62, 95% confidence interval [CI] 1.19-2.20), incomplete emptying (OR = 1.62, 95% CI = 1.17-2.26), bladder pain (OR = 1.45, 95% CI = 1.15-1.83), and urinary tract infections (OR = 1.68, 95% CI = 1.28-2.21) at age 19 after adjustment for covariates. However, associations were attenuated after adjustment for condom use and number of sexual partners. Sexual intercourse by age 17 was associated with 1.53-2.65 increased odds of LUTs categories except incontinence, with lower confidence interval boundaries > 1.0. Associations were stronger among women with ≥ 3 sexual partners (vs. 0) by age 17.

DISCUSSION

We found longitudinally assessed associations between OC use, sexual intercourse, and number of sexual partners during adolescence and LUTS at age 19.

Efficacy of the modified Brisson+Devine procedure for the treatment of concealed penis

OBJECTIVE

This study was performed to evaluate the clinical efficacy of the modified Brisson+Devine procedure in the management of concealed penis.

PATIENTS AND METHODS

In this retrospective study, the medical data of 45 children diagnosed with concealed penis who underwent modified Brisson+Devine procedure in the Department of Urology of Anhui Provincial Children's Hospital between January 2019 and December 2021 were analyzed. Follow-up visits were performed at one, three, and six months postoperatively, and outcome measures included postoperative complications and parental satisfaction.

RESULTS

All 45 children completed the surgery uneventfully. At 3-4 days after surgery, the penile dressing and the urinary catheter were removed. The patients were discharged 4-5 days postoperatively without ischemic necrosis of metastatic flaps. The follow-up visits spanned from 7 to 33 months, with a mean of 14.6 months. A statistically significant increase in the penile length after surgery was observed (p<0.05). The postoperative penile appearance was good, and the parents of the children had high treatment satisfaction (p<0.05). 38 children developed postoperative transferred flap edema, and the edema disappeared at 3 months postoperatively.

CONCLUSIONS

The modified Brisson+ Devine procedure for concealed penis allows maximum use of the foreskin to improve the appearance of the penis and has a high safety profile by reducing postoperative complications, and provides high treatment satisfaction.

Origami plot: a novel multivariate data visualization tool that improves radar chart

OBJECTIVES

We propose the origami plot, which maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multicriteria decision-making.

STUDY DESIGN AND SETTING

Built upon a radar chart, the origami plot adds additional auxiliary axes and points such that the area of the connected region of all dots is invariant to the ordering of axes. As such, it enables ranking different individuals by the overall performance for multicriteria decision-making while maintaining the intuitive visual appeal of the radar chart. We develop extensions of the origami plot, including the weighted origami plot, which allows reweighting of each attribute to define the overall performance, and the pairwise origami plot, which highlights comparisons between two individuals.

RESULTS

We illustrate the different versions of origami plots using the hospital compare database developed by the Centers for Medicare & Medicaid Services (CMS). The plot shows individual hospital's performance on mortality, readmission, complication, and infection, as well as patient experience and timely and effective care, as well as their overall performance across these metrics. The weighted origami plot allows weighing the attributes differently when some are more important than others. We illustrate the potential use of the pairwise origami plot in electronic health records (EHR) system to monitor five clinical measures (body mass index [BMI]), fasting glucose level, blood pressure, triglycerides, and low-density lipoprotein ([LDL] cholesterol) of a patient across multiple hospital visits.

CONCLUSION

The origami plot is a useful visualization tool to assist multicriteria decision making. It improves radar charts by avoiding potential misuse of the connected regions. It has several new features and allows flexible customization.

Borrowing Concurrent Information from Non-Concurrent Control to Enhance Statistical Efficiency in Platform Trials

A platform trial is a trial involving an innovative adaptive design with a single master protocol to efficiently evaluate multiple interventions. It offers flexible features such as dropping interventions for futility and adding new interventions to be evaluated during the course of a trial. Although there is a consensus that platform trials can identify beneficial interventions with fewer patients, less time, and a higher probability of success than traditional trials, there remains debate on certain issues, one of which is whether (and how) the non-concurrent control (NCC) (i.e., patients in the control group recruited prior to the new interventions) can be combined with the current control (CC) in the analysis, especially if there is a change of standard of care during the trial. Methods: In this paper, considering time-to-event endpoints under the proportional hazard model assumption, we introduce a new concept of NCC concurrent observation time (NCC COT), and propose to borrow NCC COT through left truncation. This assumes that the NCC COT and CC are comparable. If the protocol does not prohibit NCC patients to change the standard of care while on study, NCC COT and CC likely will share the same standard of care. A simulated example is provided to demonstrate the approach. Results: Using exponential distributions, the simulated example assumes that NCC COT and CC have the same hazard, and the treatment group has a lower hazard. The estimated HR comparing treatment to the pooled control group is 0.744 (95% CI 0.575, 0.962), whereas the comparison to the CC group alone is 0.755 (95% CI 0.566, 1.008), with corresponding p-values of 0.024 versus 0.057, respectively. This suggests that borrowing NCC COT can improve statistical efficiency when the exchangeability assumption holds. Conclusion: This article proposes an innovative approach of borrowing NCC COT to enhance statistical inference in platform trials under appropriate scenarios.

Accounting for post-randomization variables in meta-analysis: A joint meta-regression approach

Meta-regression is widely used in systematic reviews to investigate sources of heterogeneity and the association of study-level covariates with treatment effectiveness. Existing meta-regression approaches are successful in adjusting for baseline covariates, which include real study-level covariates (e.g., publication year) that are invariant within a study and aggregated baseline covariates (e.g., mean age) that differ for each participant but are measured before randomization within a study. However, these methods have several limitations in adjusting for post-randomization variables. Although post-randomization variables share a handful of similarities with baseline covariates, they differ in several aspects. First, baseline covariates can be aggregated at the study level presumably because they are assumed to be balanced by the randomization, while post-randomization variables are not balanced across arms within a study and are commonly aggregated at the arm level. Second, post-randomization variables may interact dynamically with the primary outcome. Third, unlike baseline covariates, post-randomization variables are themselves often important outcomes under investigation. In light of these differences, we propose a Bayesian joint meta-regression approach adjusting for post-randomization variables. The proposed method simultaneously estimates the treatment effect on the primary outcome and on the post-randomization variables. It takes into consideration both between- and within-study variability in post-randomization variables. Studies with missing data in either the primary outcome or the post-randomization variables are included in the joint model to improve estimation. Our method is evaluated by simulations and a real meta-analysis of major depression disorder treatments.

Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity

Aim: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. Materials & methods: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. Results: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively, with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). Conclusion: BH-WMC-NMA impacts incremental mean LYs and cost-effectiveness ratios, potentially affecting reimbursement decisions.

Assessing the robustness of results from clinical trials and meta-analyses with the fragility index

The fragility index has been increasingly used to assess the robustness of the results of clinical trials since 2014. It aims at finding the smallest number of event changes that could alter originally statistically significant results. Despite its popularity, some researchers have expressed several concerns about the validity and usefulness of the fragility index. It offers a comprehensive review of the fragility index's rationale, calculation, software, and interpretation, with emphasis on application to studies in obstetrics and gynecology. This article presents the fragility index in the settings of individual clinical trials, standard pairwise meta-analyses, and network meta-analyses. Moreover, this article provides worked examples to demonstrate how the fragility index can be appropriately calculated and interpreted. In addition, the limitations of the traditional fragility index and some solutions proposed in the literature to address these limitations were reviewed. In summary, the fragility index is recommended to be used as a supplemental measure in the reporting of clinical trials and a tool to communicate the robustness of trial results to clinicians. Other considerations that can aid in the fragility index's interpretation include the loss to follow-up and the likelihood of data modifications that achieve the loss of statistical significance.

The prognostic value of ECOG performance status on overall survival among patients with metastatic prostate cancer: A systematic review of the literature and meta-analysis

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Background: Eastern Cooperative Oncology Group Performance Status (ECOG PS) has been shown to be a strong predictor of mortality among patients with advanced cancer. However, in metastatic prostate cancer, there has been heterogeneity in the literature regarding the strength of association between different ECOG categories and overall survival (OS). Therefore, we conducted a systematic search and meta-analysis of studies reporting on the prognostic value of ECOG PS on OS within the metastatic prostate cancer population. Methods: We performed a systematic search of the literature within the Medline (PubMed) database from inception until March 21st 2022. Both trials and real-world data (RWD) studies were included. Using a random-effects model, a meta-analysis pooling ECOG categories on OS was performed separately for metastatic castrate resistant prostate cancer (mCRPC) and metastatic castrate sensitive prostate cancer (mCSPC) studies. We also stratified the analyses by prior use of chemotherapy and study type (RWD vs. Trial). Between-study heterogeneity was analyzed using the I2 statistic. Results: A total of 75 studies met the eligibility criteria, comprising of 32,298 patients. Nearly all studies (72 out of 75) were among patients with mCRPC, while the remaining three studies were among mCSPC patients. Overall, all ECOG PS categories were associated with OS, with the highest estimate observed among mCRPC patients with ECOG PS of ≥2 vs. <2 ([HR] = 2.10; 95% confidence interval [CI]: 1.87 to 2.37). In secondary analyses, among mCRPC patients with ECOG PS of ≥1 vs. <1, there was a significant difference according to study type (RWD: HR=1.98, 95% CI: 1.73 to 2.26 vs. trials HR=1.32, 95% CI: 1.13 to 1.54). There were no significant differences in the pooled HR of OS stratified by previous chemotherapy across all three ECOG PS categories. Conclusions: Overall, ECOG PS was a significant predictor of OS regardless of category, previous chemotherapy, and type of metastatic population, although differences were observed in RWD vs. trial populations. Additional studies are needed to quantify the association between ECOG PS and OS in the mCSPC population, as well as to better understand the role of ECOG PS on outcomes in RWD vs. trials of cancer treatments.

RIMeta: An R shiny tool for estimating the reference interval from a meta-analysis

A reference interval, or an interval in which a prespecified proportion of measurements from a healthy population are expected to fall, is used to determine whether a person's measurement is typical of a healthy individual. For a specific biomarker, multiple published studies may provide data collected from healthy participants. A reference interval estimated by combining the data across these studies is typically more generalizable than a reference interval based on a single study. Methods for estimating reference intervals from random effects meta-analysis and fixed-effects meta-analysis have been recently proposed and implemented using R software. We present an R Shiny tool, RIMeta, implementing these methods, which allows users not proficient in R to estimate a reference interval from a meta-analysis using aggregate data (mean, standard deviation, and sample size) from each study. RIMeta (https://cers.shinyapps.io/RIMeta/) provides users a convenient way to estimate a reference interval from a meta-analysis and to generate the reference interval plot to visualize the results. The use of this web-based R Shiny tool does not require the installation of R or any background knowledge of programming. We explain all functions of the R Shiny tool and illustrate how to use it with a real data example.

Meta-analysis methods for risk difference: A comparison of different models

Risk difference is a frequently-used effect measure for binary outcomes. In a meta-analysis, commonly-used methods to synthesize risk differences include: (1) the two-step methods that estimate study-specific risk differences first, then followed by the univariate common-effect model, fixed-effects model, or random-effects models; and (2) the one-step methods using bivariate random-effects models to estimate the summary risk difference from study-specific risks. These methods are expected to have similar performance when the number of studies is large and the event rate is not rare. However, studies with zero events are common in meta-analyses, and bias may occur with the conventional two-step methods from excluding zero-event studies or using an artificial continuity correction to zero events. In contrast, zero-event studies can be included and modeled by bivariate random-effects models in a single step. This article compares various methods to estimate risk differences in meta-analyses. Specifically, we present two case studies and three simulation studies to compare the performance of conventional two-step methods and bivariate random-effects models in the presence or absence of zero-event studies. In conclusion, we recommend researchers using bivariate random-effects models to estimate risk differences in meta-analyses, particularly in the presence of zero events.

Longitudinal transition of body mass index status and its associated factors among Chinese middle-aged and older adults in Markov model

Introduction

Body mass index (BMI) has a strong correlation with chronic diseases and all-cause mortality. However, few studies have previously reported the longitudinal transition of BMI status and its influential factors, especially among Chinese middle-aged and older adults.

Methods

This population-based cohort study involved 6,507 participants derived from the China Health and Retirement Longitudinal Study from 2011 to 2015, including objectively measured BMI recorded in 26,028 person-year of all observations followed up. Multistate Markov model was performed to estimate the BMI state transition intensity and hazard ratios of each potential exposure risk.

Results

The mean intensity of the population that shifted from normal to overweight was more than twice than shifted to underweight. Besides, a predicted probability was up to 16.16% that the population with overweight would suffer from obesity and more than half of the population with underweight would return to normal weight over a 6-year interval. The study also implied significant effects of baseline age, gender, marital status, education level, alcohol consumption, smoking, depression symptoms, and activities of daily living impairment on BMI status transition to varying degrees.

Conclusions

Findings of this study indicated that the mean transition probability between different BMI statuses varied, specific exposure factors serving as barriers or motivators to future transitions based on current BMI status was clarified for the health promotion strategies.

Assessing and visualizing fragility of clinical results with binary outcomes in R using the fragility package

With the growing concerns about research reproducibility and replicability, the assessment of scientific results' fragility (or robustness) has been of increasing interest. The fragility index was proposed to quantify the robustness of statistical significance of clinical studies with binary outcomes. It is defined as the minimal event status modifications that can alter statistical significance. It helps clinicians evaluate the reliability of the conclusions. Many factors may affect the fragility index, including the treatment groups in which event status is modified, the statistical methods used for testing for the association between treatments and outcomes, and the pre-specified significance level. In addition to assessing the fragility of individual studies, the fragility index was recently extended to both conventional pairwise meta-analyses and network meta-analyses of multiple treatment comparisons. It is not straightforward for clinicians to calculate these measures and visualize the results. We have developed an R package called "fragility" to offer user-friendly functions for such purposes. This article provides an overview of methods for assessing and visualizing the fragility of individual studies as well as pairwise and network meta-analyses, introduces the usage of the "fragility" package, and illustrates the implementations with several worked examples.

Splenectomy versus Imaging-Guided Percutaneous Drainage for Splenic Abscess: A Systematic Review and Meta-Analysis

Background: Splenic abscess (SA) is a rare, life-threatening illness that is generally treated with splenectomy. However, this is associated with high mortality and morbidity. Recently, percutaneous drainage (PD) has emerged as an alternative therapy in select patients. In this study, we compare mortality and complications in patients with SA treated with splenectomy versus PD. Patients and Methods: A systematic literature search of 13 databases and online search engines was conducted from 2019 to 2020. A bivariate generalized linear mixed model (BGLMM) was used to conduct a separate meta-analysis for both mortality and complications. We used the risk of bias in non-randomized studies of interventions (ROBINS-I) tool to evaluate risk of bias in non-randomized studies, and the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach for assessing quality of evidence and strength of recommendations. Results were presented according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Results: The review included 46 retrospective studies from 21 countries. For mortality rate, 27 studies compared splenectomy and PD whereas 10 used PD only and nine used splenectomy only. Data for major complications were available in 18 two-arm studies, seven single-arm studies with PD, and seven single-arm studies with splenectomy. Of a total of 589 patients, 288 were treated with splenectomy and 301 underwent PD. Mortality rate was 12% (95% confidence interval [CI], 8%-17%) in patients undergoing splenectomy compared with 8% (95% CI, 4%-13%) with PD. Complication rates were 26% (95% CI, 16%-37%) in the splenectomy group compared with 10% (95% CI, 4%-17%) in the PD group. Conclusions: Percutaneous drainage s associated with a trend toward lower complications and mortality rates compared with splenectomy in the treatment of SA, however, these findings were not statistically significant. Because of the heterogeneity of the data, further prospective studies are needed to draw definitive conclusions.

Empirical comparisons of meta-analysis methods for diagnostic studies: a meta-epidemiological study

OBJECTIVES

Several methods are commonly used for meta-analyses of diagnostic studies, such as the bivariate linear mixed model (LMM). It estimates the overall sensitivity, specificity, their correlation, diagnostic OR (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (ROC) estimates. Nevertheless, the bivariate LMM makes potentially unrealistic assumptions (ie, normality of within-study estimates), which could be avoided by the bivariate generalised linear mixed model (GLMM). This article aims at investigating the real-world performance of the bivariate LMM and GLMM using meta-analyses of diagnostic studies from the Cochrane Library.

METHODS

We compared the bivariate LMM and GLMM using the relative differences in the overall sensitivity and specificity, their 95% CI widths, between-study variances, and the correlation between the (logit) sensitivity and specificity. We also explored their relationships with the number of studies, number of subjects, overall sensitivity and overall specificity.

RESULTS

Among the extracted 1379 meta-analyses, point estimates of overall sensitivities and specificities by the bivariate LMM and GLMM were generally similar, but their CI widths could be noticeably different. The bivariate GLMM generally produced narrower CIs than the bivariate LMM when meta-analyses contained 2-5 studies. For meta-analyses with <100 subjects or the overall sensitivities or specificities close to 0% or 100%, the bivariate LMM could produce substantially different AUCs, DORs and DOR CI widths from the bivariate GLMM.

CONCLUSIONS

The variation of estimates calls into question the appropriateness of the normality assumption within individual studies required by the bivariate LMM. In cases of notable differences presented in these methods' results, the bivariate GLMM may be preferred.

A Guide to Estimating the Reference Range From a Meta-Analysis Using Aggregate or Individual Participant Data

Clinicians frequently must decide whether a patient's measurement reflects that of a healthy "normal" individual. Thus, the reference range is defined as the interval in which some proportion (frequently 95%) of measurements from a healthy population is expected to fall. One can estimate it from a single study or preferably from a meta-analysis of multiple studies to increase generalizability. This range differs from the confidence interval for the pooled mean and the prediction interval for a new study mean in a meta-analysis, which do not capture natural variation across healthy individuals. Methods for estimating the reference range from a meta-analysis of aggregate data that incorporates both within- and between-study variations were recently proposed. In this guide, we present 3 approaches for estimating the reference range: one frequentist, one Bayesian, and one empirical. Each method can be applied to either aggregate or individual-participant data meta-analysis, with the latter being the gold standard when available. We illustrate the application of these approaches to data from a previously published individual-participant data meta-analysis of studies measuring liver stiffness by transient elastography in healthy individuals between 2006 and 2016.

A Bayesian hierarchical model for individual participant data meta-analysis of demand curves

Individual participant data meta-analysis is a frequently used method to combine and contrast data from multiple independent studies. Bayesian hierarchical models are increasingly used to appropriately take into account potential heterogeneity between studies. In this paper, we propose a Bayesian hierarchical model for individual participant data generated from the Cigarette Purchase Task (CPT). Data from the CPT details how demand for cigarettes varies as a function of price, which is usually described as an exponential demand curve. As opposed to the conventional random-effects meta-analysis methods, Bayesian hierarchical models are able to estimate both the study-specific and population-level parameters simultaneously without relying on the normality assumptions. We applied the proposed model to a meta-analysis with baseline CPT data from six studies and compared the results from the proposed model and a two-step conventional random-effects meta-analysis approach. We conducted extensive simulation studies to investigate the performance of the proposed approach and discussed the benefits of using the Bayesian hierarchical model for individual participant data meta-analysis of demand curves.

A penalization approach to random-effects meta-analysis

Systematic reviews and meta-analyses are principal tools to synthesize evidence from multiple independent sources in many research fields. The assessment of heterogeneity among collected studies is a critical step when performing a meta-analysis, given its influence on model selection and conclusions about treatment effects. A common-effect (CE) model is conventionally used when the studies are deemed homogeneous, while a random-effects (RE) model is used for heterogeneous studies. However, both models have limitations. For example, the CE model produces excessively conservative confidence intervals with low coverage probabilities when the collected studies have heterogeneous treatment effects. The RE model, on the other hand, assigns higher weights to small studies compared to the CE model. In the presence of small-study effects or publication bias, the over-weighted small studies from a RE model can lead to substantially biased overall treatment effect estimates. In addition, outlying studies may exaggerate between-study heterogeneity. This article introduces penalization methods as a compromise between the CE and RE models. The proposed methods are motivated by the penalized likelihood approach, which is widely used in the current literature to control model complexity and reduce variances of parameter estimates. We compare the existing and proposed methods with simulated data and several case studies to illustrate the benefits of the penalization methods.

Controversy and Debate : Questionable utility of the relative risk in clinical research: Paper 4 :Odds Ratios are far from "portable" - A call to use realistic models for effect variation in meta-analysis

OBJECTIVE

Recently Doi et al. argued that risk ratios should be replaced with odds ratios in clinical research. We disagreed, and empirically documented the lack of portability of odds ratios, while Doi et al. defended their position. In this response we highlight important errors in their position.

STUDY DESIGN AND SETTING

We counter Doi et al.'s arguments by further examining the correlations of odds ratios, and risk ratios, with baseline risks in 20,198 meta-analyses from the Cochrane Database of Systematic Reviews.

RESULTS

Doi et al.'s claim that odds ratios are portable is invalid because 1) their reasoning is circular: they assume a model under which the odds ratio is constant and show that under such a model the odds ratio is portable; 2) the method they advocate to convert odds ratios to risk ratios is biased; 3) their empirical example is readily-refuted by counter-examples of meta-analyses in which the risk ratio is portable but the odds ratio isn't; and 4) they fail to consider the causal determinants of meta-analytic inclusion criteria: Doi et al. mistakenly claim that variation in odds ratios with different baseline risks in meta-analyses is due to collider bias. Empirical comparison between the correlations of odds ratios, and risk ratios, with baseline risks show that the portability of odds ratios and risk ratios varies across settings.

CONCLUSION

The suggestion to replace risk ratios with odds ratios is based on circular reasoning and a confusion of mathematical and empirical results. It is especially misleading for meta-analyses and clinical guidance. Neither the odds ratio nor the risk ratio is universally portable. To address this lack of portability, we reinforce our suggestion to report variation in effect measures conditioning on modifying factors such as baseline risk; understanding such variation is essential to patient-centered practice.

Estimating the Complier Average Causal Effect in a Meta-Analysis of Randomized Clinical Trials With Binary Outcomes Accounting for Noncompliance: A Generalized Linear Latent and Mixed Model Approach

Noncompliance, a common problem in randomized clinical trials (RCTs), can bias estimation of the effect of treatment receipt using a standard intention-to-treat analysis. The complier average causal effect (CACE) measures the effect of an intervention in the latent subpopulation that would comply with their assigned treatment. Although several methods have been developed to estimate the CACE in analyzing a single RCT, methods for estimating the CACE in a meta-analysis of RCTs with noncompliance await further development. This article reviews the assumptions needed to estimate the CACE in a single RCT and proposes a frequentist alternative for estimating the CACE in a meta-analysis, using a generalized linear latent and mixed model with SAS software (SAS Institute, Inc.). The method accounts for between-study heterogeneity using random effects. We implement the methods and describe an illustrative example of a meta-analysis of 10 RCTs evaluating the effect of receiving epidural analgesia in labor on cesarean delivery, where noncompliance varies dramatically between studies. Simulation studies are used to evaluate the performance of the proposed method.

BRIDGING RANDOMIZED CONTROLLED TRIALS AND SINGLE-ARM TRIALS USING COMMENSURATE PRIORS IN ARM-BASED NETWORK META-ANALYSIS

Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and indirectly by combining and contrasting multiple independent clinical trials. Because many NMAs collect only a few eligible randomized controlled trials (RCTs), there is an urgent need to synthesize different sources of information, e.g., from both RCTs and single-arm trials. However, single-arm trials and RCTs may have different populations and quality, so that assuming they are exchangeable may be inappropriate. This article presents a novel method using a commensurate prior on variance (CPV) to borrow variance (rather than mean) information from single-arm trials in an arm-based (AB) Bayesian NMA. We illustrate the advantages of this CPV method by reanalyzing an NMA of immune checkpoint inhibitors in cancer patients. Comprehensive simulations investigate the impact on statistical inference of including single-arm trials. The simulation results show that the CPV method provides efficient and robust estimation even when the two sources of information are moderately inconsistent.

Giant modulation of optical nonlinearity by Floquet engineering

Strong periodic driving with light offers the potential to coherently manipulate the properties of quantum materials on ultrafast timescales. Recently, strategies have emerged to drastically alter electronic and magnetic properties by optically inducing non-trivial band topologies^1-6, emergent spin interactions^7-11 and even superconductivity¹². However, the prospects and methods of coherently engineering optical properties on demand are far less understood¹³. Here we demonstrate coherent control and giant modulation of optical nonlinearity in a van der Waals layered magnetic insulator, manganese phosphorus trisulfide (MnPS₃). By driving far off-resonance from the lowest on-site manganese d-d transition, we observe a coherent on-off switching of its optical second harmonic generation efficiency on the timescale of 100 femtoseconds with no measurable dissipation. At driving electric fields of the order of 10⁹ volts per metre, the on-off ratio exceeds 10, which is limited only by the sample damage threshold. Floquet theory calculations¹⁴ based on a single-ion model of MnPS₃ are able to reproduce the measured driving field amplitude and polarization dependence of the effect. Our approach can be applied to a broad range of insulating materials and could lead to dynamically designed nonlinear optical elements.

Characterizing Long COVID: Deep Phenotype of a Complex Condition

BACKGROUND

Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies.

METHODS

The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19.

FUNDING

We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies.

INTERPRETATION

Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

FUNDING

U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.