ZIBGLMM: Zero-Inflated Bivariate Generalized Linear Mixed Model for Meta-Analysis with Double-Zero-Event Studies.

Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis. Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies. In this paper, we propose a zero-inflated bivariate generalized linear mixed model (ZIBGLMM) to tackle this issue. This two-component finite mixture model includes zero-inflation for a subpopulation with negligible or extremely low risk. We develop both frequentist and Bayesian versions of ZIBGLMM and examine its performance in estimating risk ratios (RRs) against the bivariate generalized linear mixed model and conventional two-stage meta-analysis that excludes DZS. Through extensive simulation studies and real-world meta-analysis case studies, we demonstrate that ZIBGLMM outperforms the bivariate generalized linear mixed model and conventional two-stage metaanalysis that excludes DZS in estimating the true effect size with substantially less bias and comparable coverage probability.

Long-term associations of PM1 versus PM2.5 and PM10 with asthma and asthma-related respiratory symptoms in the middle-aged and elderly population.

Background: Few studies have compared the associations between long-term exposures to particulate matters (aerodynamic diameter ≤1, ≤2.5 and ≤10 µm: PM1, PM2.5 and PM10, respectively) and asthma and asthma-related respiratory symptoms. The objective of the present study was to compare the strength of the aforementioned associations in middle-aged and elderly adults. Methods: We calculated the mean 722-day personal exposure estimates of PM1, PM2.5 and PM10 at 1 km×1 km spatial resolution between 2013 and 2019 at individual levels from China High Air Pollutants (CHAP) datasets. Using logistic regression models, we presented the associations as odds ratios and 95% confidence intervals, for each interquartile range (IQR) increase in PM1/PM2.5/PM10 concentration. Asthma denoted a self-reported history of physician-diagnosed asthma or wheezing in the preceding 12 months. Results: We included 7371 participants in COPD surveillance from Guangdong, China. Each IQR increase in PM1, PM2.5 and PM10 was associated with a greater odds (OR (95% CI)) of asthma (PM1: 1.22 (1.02-1.45); PM2.5: 1.24 (1.04-1.48); PM10: 1.30 (1.07-1.57)), wheeze (PM1: 1.27 (1.11-1.44); PM2.5: 1.30 (1.14-1.48); PM10: 1.34 (1.17-1.55)), persistent cough (PM1: 1.33 (1.06-1.66); PM2.5: 1.36 (1.09-1.71); PM10: 1.31 (1.02-1.68)) and dyspnoea (PM1: 2.10 (1.84-2.41); PM2.5: 2.17 (1.90-2.48); PM10: 2.29 (1.96-2.66)). Sensitivity analysis results were robust after excluding individuals with a family history of allergy. Associations of PM1, PM2.5 and PM10 with asthma and asthma-related respiratory symptoms were slightly stronger in males. Conclusion: Long-term exposure to PM is associated with increased risks of asthma and asthma-related respiratory symptoms.

Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.

Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions from different meta-analyses on the same topic can be discrepant, which has raised concerns about their reliability. One reason is that the result of a meta-analysis is sensitive to factors such as study inclusion/exclusion criteria and model assumptions. The arm-based meta-analysis model is growing in importance due to its advantage of including single-arm studies and historical controls with estimation efficiency and its flexibility in drawing conclusions with both marginal and conditional effect measures. Despite its benefits, the inference may heavily depend on the heterogeneity parameters that reflect design and model assumptions. This article aims to evaluate the robustness of meta-analyses using the arm-based model within a Bayesian framework. Specifically, we develop a tipping point analysis of the between-arm correlation parameter to assess the robustness of meta-analysis results. Additionally, we introduce some visualization tools to intuitively display its impact on meta-analysis results. We demonstrate the application of these tools in three real-world meta-analyses, one of which includes single-arm studies.

Accelerating evidence synthesis for safety assessment through ClinicalTrials.gov platform: a feasibility study.

BACKGROUND: Standard systematic review can be labor-intensive and time-consuming meaning that it can be difficult to provide timely evidence when there is an urgent public health emergency such as a pandemic. The ClinicalTrials.gov provides a promising way to accelerate evidence production. METHODS: We conducted a search on PubMed to gather systematic reviews containing a minimum of 5 studies focused on safety aspects derived from randomized controlled trials (RCTs) of pharmacological interventions, aiming to establish a real-world dataset. The registration information of each trial from eligible reviews was further collected and verified. The meta-analytic data were then re-analyzed by using 1) the full meta-analytic data with all trials and 2) emulated rapid data with trials that had been registered and posted results on ClinicalTrials.gov, under the same synthesis methods. The effect estimates of the full meta-analysis and rapid meta-analysis were then compared. RESULTS: The real-world dataset comprises 558 meta-analyses. Among them, 56 (10.0%) meta-analyses included RCTs that were not registered in ClinicalTrials.gov. For the remaining 502 meta-analyses, the median percentage of RCTs registered within each meta-analysis is 70.1% (interquartile range: 33.3% to 88.9%). Under a 20% bias threshold, rapid meta-analyses conducted through ClinicalTrials.gov achieved accurate point estimates ranging from 77.4% (using the MH model) to 83.1% (using the GLMM model); 91.0% to 95.3% of these analyses accurately predicted the direction of effects. CONCLUSIONS: Utilizing the ClinicalTrials.gov platform for safety assessment with a minimum of 5 RCTs holds significant potential for accelerating evidence synthesis to support urgent decision-making.

Exploiting multivariate network meta-analysis: A calibrated Bayesian composite likelihood inference.

Multivariate network meta-analysis has emerged as a powerful tool in evidence synthesis by incorporating multiple outcomes and treatments. Despite its advantages, this method comes with methodological challenges, such as the issue of unreported within-study correlations among treatments and outcomes, which potentially lead to misleading conclusions. In this paper, we proposed a calibrated Bayesian composite likelihood approach to overcome this limitation. The proposed method eliminated the need to specify a full likelihood function while allowing for the unavailability of within-study correlations among treatments and outcomes. Additionally, we developed a hybrid Gibbs sampler algorithm along with the Open-Faced Sandwich post-sampling adjustment to enable robust posterior inference. Through comprehensive simulation studies, we demonstrated that the proposed approach yielded unbiased estimates while maintaining coverage probabilities close to the nominal level. Furthermore, we implemented the proposed method on two real-world network meta-analysis datasets; one comparing treatment procedures for the root coverage and another comparing treatments for anaemia in chronic kidney disease patients.

Methods for assessing inverse publication bias of adverse events.

In medical research, publication bias (PB) poses great challenges to the conclusions from systematic reviews and meta-analyses. The majority of efforts in methodological research related to classic PB have focused on examining the potential suppression of studies reporting effects close to the null or statistically non-significant results. Such suppression is common, particularly when the study outcome concerns the effectiveness of a new intervention. On the other hand, attention has recently been drawn to the so-called inverse publication bias (IPB) within the evidence synthesis community. It can occur when assessing adverse events because researchers may favor evidence showing a similar safety profile regarding an adverse event between a new intervention and a control group. In comparison to the classic PB, IPB is much less recognized in the current literature; methods designed for classic PB may be inaccurately applied to address IPB, potentially leading to entirely incorrect conclusions. This article aims to provide a collection of accessible methods to assess IPB for adverse events. Specifically, we discuss the relevance and differences between classic PB and IPB. We also demonstrate visual assessment through contour-enhanced funnel plots tailored to adverse events and popular quantitative methods, including Egger's regression test, Peters' regression test, and the trim-and-fill method for such cases. Three real-world examples are presented to illustrate the bias in various scenarios, and the implementations are illustrated with statistical code. We hope this article offers valuable insights for evaluating IPB in future systematic reviews of adverse events.

A review and comparison of arm-based versus contrast-based network meta-analysis for binary outcomes-Understanding their differences and limitations.

Network meta-analysis (NMA) is a statistical procedure to simultaneously compare multiple interventions. Despite the added complexity of performing an NMA compared with the traditional pairwise meta-analysis, under proper assumptions the NMA can lead to more efficient estimates on the comparisons of interventions by combining and contrasting the direct and indirect evidence into a form of evidence that can be used to underpin treatment guidelines. Two broad classes of NMA methods are commonly used in practice: the contrast-based (CB-NMA) and the arm-based (AB-NMA) models. While CB-NMA only focuses on the relative effects by assuming fixed intercepts, the AB-NMA offers greater flexibility on the estimands, including both the absolute and relative effects by assuming random intercepts. A major criticism of the AB-NMA, on which we aim to elaborate in this paper, is that it does not retain randomization within trials, which may introduce bias in the estimated relative effects in some scenarios. This criticism was drawn under the implicit assumption that a given relative effect is transportable, in which case the data generating mechanism favors the inference based on CB-NMA, which models the relative effect. In this article, we aim to review, summarize, and elaborate on the underlying assumptions, similarities and differences, and also the advantages and disadvantages, between CB-NMA and AB-NMA methods. As indirect treatment comparison is susceptible to risk of bias no matter which approach is taken, it is important to consider both approaches in practice as complementary sensitivity analyses and to provide the totality of evidence from the data.

Exploring the Limits of Artificial Intelligence for Referencing Scientific Articles.

OBJECTIVE: To evaluate the reliability of three artificial intelligence (AI) chatbots (ChatGPT, Google Bard, and Chatsonic) in generating accurate references from existing obstetric literature. STUDY DESIGN: Between mid-March and late April 2023, ChatGPT, Google Bard, and Chatsonic were prompted to provide references for specific obstetrical randomized controlled trials (RCTs) published in 2020. RCTs were considered for inclusion if they were mentioned in a previous article that primarily evaluated RCTs published by the top medical and obstetrics and gynecology journals with the highest impact factors in 2020 as well as RCTs published in a new journal focused on publishing obstetric RCTs. The selection of the three AI models was based on their popularity, performance in natural language processing, and public availability. Data collection involved prompting the AI chatbots to provide references according to a standardized protocol. The primary evaluation metric was the accuracy of each AI model in correctly citing references, including authors, publication title, journal name, and digital object identifier (DOI). Statistical analysis was performed using a permutation test to compare the performance of the AI models. RESULTS: Among the 44 RCTs analyzed, Google Bard demonstrated the highest accuracy, correctly citing 13.6% of the requested RCTs, whereas ChatGPT and Chatsonic exhibited lower accuracy rates of 2.4 and 0%, respectively. Google Bard often substantially outperformed Chatsonic and ChatGPT in correctly citing the studied reference components. The majority of references from all AI models studied were noted to provide DOIs for unrelated studies or DOIs that do not exist. CONCLUSION: To ensure the reliability of scientific information being disseminated, authors must exercise caution when utilizing AI for scientific writing and literature search. However, despite their limitations, collaborative partnerships between AI systems and researchers have the potential to drive synergistic advancements, leading to improved patient care and outcomes. KEY POINTS: · AI chatbots often cite scientific articles incorrectly.. · AI chatbots can create false references.. · Responsible AI use in research is vital..

Influence of social vulnerability index on Medicare beneficiaries' expenditures upon discharge.

Medicare beneficiaries' healthcare spending varies across geographical regions, influenced by availability of medical resources and institutional efficiency. We aimed to evaluate whether social vulnerability influences healthcare costs among Medicare beneficiaries. Multivariable regression analyses were conducted to determine whether the social vulnerability index (SVI), released by the Centers for Disease Control and Prevention (CDC), was associated with average submitted covered charges, total payment amounts, or total covered days upon hospital discharge among Medicare beneficiaries. We used information from discharged Medicare beneficiaries from hospitals participating in the Inpatient Prospective Payment System. Covariate adjustment included demographic information consisting of age groups, race/ethnicity, and Hierarchical Condition Category risk score. The regressions were performed with weights proportioned to the number of discharges. Average submitted covered charges significantly correlated with SVI (ß = 0.50, p < 0.001) in the unadjusted model and remained significant in the covariates-adjusted model (ß = 0.25, p = 0.039). The SVI was not significantly associated with the total payment amounts (ß = -0.07, p = 0.238) or the total covered days (ß = 0.00, p = 0.953) in the adjusted model. Regional variations in Medicare beneficiaries' healthcare spending exist and are influenced by levels of social vulnerability. Further research is warranted to fully comprehend the impact of social determinants on healthcare costs.

Assessing the Seasonality of Lab Tests Among Patients with Alzheimer's Disease and Related Dementias in OneFlorida Data Trust.

About 1 in 9 older adults over 65 has Alzheimer's disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations-higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.

Comparisons of various estimates of the I2 statistic for quantifying between-study heterogeneity in meta-analysis.

Assessing heterogeneity between studies is a critical step in determining whether studies can be combined and whether the synthesized results are reliable. The I2 statistic has been a popular measure for quantifying heterogeneity, but its usage has been challenged from various perspectives in recent years. In particular, it should not be considered an absolute measure of heterogeneity, and it could be subject to large uncertainties. As such, when using I2 to interpret the extent of heterogeneity, it is essential to account for its interval estimate. Various point and interval estimators exist for I2. This article summarizes these estimators. In addition, we performed a simulation study under different scenarios to investigate preferable point and interval estimates of I2. We found that the Sidik-Jonkman method gave precise point estimates for I2 when the between-study variance was large, while in other cases, the DerSimonian-Laird method was suggested to estimate I2. When the effect measure was the mean difference or the standardized mean difference, the Q-profile method, the Biggerstaff-Jackson method, or the Jackson method was suggested to calculate the interval estimate for I2 due to reasonable interval length and more reliable coverage probabilities than various alternatives. For the same reason, the Kulinskaya-Dollinger method was recommended to calculate the interval estimate for I2 when the effect measure was the log odds ratio.

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

OBJECTIVES: 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 HMs 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 an HM that addresses measurement error) to 3193 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 2036 (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 was 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, which can suggest frequent lack of portability of the relative effect across baseline risks. User-friendly open-source code is provided to meta-analysts interested in exploring this association.

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.

Single-balloon catheter with concomitant vaginal misoprostol is the most effective strategy for labor induction: a meta-review with network meta-analysis.

OBJECTIVE: Several systematic reviews and meta-analyses have been conducted to summarize the evidence for the efficacy of various labor induction agents. However, the most effective agents or strategies have not been conclusively determined. We aimed to perform a meta-review and network meta-analysis of published systematic reviews to determine the efficacy and safety of currently employed pharmacologic, mechanical, and combined methods of labor induction. DATA SOURCES: With the assistance of an experienced medical librarian, we performed a systematic search of the literature using PubMed, EMBASE, and the Cochrane Central Register of Control Trials. We systematically searched electronic databases from inception to May 31, 2021. STUDY ELIGIBILITY CRITERIA: We considered systematic reviews and meta-analyses of randomized controlled trials comparing different agents or methods for inpatient labor induction. METHODS: We conducted a frequentist random-effects network meta-analysis employing data from randomized controlled trials of published systematic reviews. We performed direct pairwise meta-analyses to compare the efficacy of the various labor induction agents and placebo or no treatment. We performed ranking to determine the best treatment using the surface under the cumulative ranking curve. The main outcomes assessed were cesarean delivery, vaginal delivery within 24 hours, operative vaginal delivery, hyperstimulation, neonatal intensive care unit admissions, and Apgar scores of <7 at 5 minutes of birth. RESULTS: We included 11 systematic reviews and extracted data from 207 randomized controlled trials with a total of 40,854 participants. When assessing the efficacy of all agents and methods, the combination of a single-balloon catheter with misoprostol was the most effective in reducing the odds of cesarean delivery and vaginal birth >24 hours (surface under the cumulative ranking curve of 0.9 for each). Among the pharmacologic agents, low-dose vaginal misoprostol was the most effective in reducing the odds of cesarean delivery, whereas high-dose vaginal misoprostol was the most effective in achieving vaginal delivery within 24 hours (surface under the cumulative ranking curve of 0.9 for each). Single-balloon catheter (surface under the cumulative ranking curve of 0.8) and double-balloon catheter (surface under the cumulative ranking curve of 0.9) were the most effective in reducing the odds of operative vaginal delivery and hyperstimulation. Buccal or sublingual misoprostol (surface under the cumulative ranking curve of 0.9) and the combination of single-balloon catheter and misoprostol (surface under the cumulative ranking curve of 0.9) most effectively reduced the odds of abnormal Apgar scores and neonatal intensive care unit admissions. CONCLUSION: The combination of a single-balloon catheter with misoprostol was the most effective method in reducing the odds for cesarean delivery and prolonged time to vaginal delivery. This method was associated with a reduction in admissions to the neonatal intensive care unit.

Outpatient cervical ripening and labor induction with low-dose vaginal misoprostol reduces the interval to delivery: a systematic review and network meta-analysis.

OBJECTIVE: Several systematic reviews and meta-analyses have summarized the evidence on the efficacy and safety of various outpatient cervical ripening methods. However, the method with the highest efficacy and safety profile has not been determined conclusively. We performed a systematic review and network meta-analysis of published randomized controlled trials to assess the efficacy and safety of cervical ripening methods currently employed in the outpatient setting. DATA SOURCES: With the assistance of an experienced medical librarian, we performed a systematic search of the literature using MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, and ClinicalTrials.gov. We systematically searched electronic databases from inception to January 14, 2020. STUDY ELIGIBILITY CRITERIA: We considered randomized controlled trials comparing a variety of methods for outpatient cervical ripening. METHODS: We conducted a frequentist random effects network meta-analysis employing data from randomized controlled trials. We performed a direct, pairwise meta-analysis to compare the efficacy of various outpatient cervical ripening methods, including placebo. We employed ranking strategies to determine the most efficacious method using the surface under the cumulative ranking curve; a higher surface under the cumulative ranking curve value implied a more efficacious method. We assessed the following outcomes: time from intervention to delivery, cesarean delivery rates, changes in the Bishop score, need for additional ripening methods, incidence of Apgar scores <7 at 5 minutes, and uterine hyperstimulation. RESULTS: We included data from 42 randomized controlled trials including 6093 participants. When assessing the efficacy of all methods, 25 µg vaginal misoprostol was the most efficacious in reducing the time from intervention to delivery (surface under the cumulative ranking curve of 1.0) without increasing the odds of cesarean delivery, the need for additional ripening methods, the incidence of a low Apgar score, or uterine hyperstimulation. Acupressure (surface under the cumulative ranking curve of 0.3) and primrose oil (surface under the cumulative ranking curve of 0.2) were the least effective methods in reducing the time to delivery interval. Among effective methods, 50 mg oral mifepristone was associated with the lowest odds of cesarean delivery (surface under the cumulative ranking curve of 0.9). CONCLUSION: When balancing efficacy and safety, vaginal misoprostol 25 µg represents the best method for outpatient cervical ripening.

Water birth: a systematic review and meta-analysis of maternal and neonatal outcomes.

OBJECTIVE: This systematic review and meta-analysis aimed to conduct a thorough and contemporary assessment of maternal and neonatal outcomes associated with water birth in comparison with land-based birth. DATA SOURCES: We conducted a comprehensive search of PubMed, EMBASE, CINAHL, and gray literature sources, from inception to February 28, 2023. STUDY ELIGIBILITY CRITERIA: We included randomized and nonrandomized studies that assessed maternal and neonatal outcomes in patients who delivered either conventionally or while submerged in water. METHODS: Pooled unadjusted odds ratios with 95% confidence intervals were calculated using a random-effects model (restricted maximum likelihood method). We assessed the 95% prediction intervals to estimate the likely range of future study results. To evaluate the robustness of the results, we calculated fragility indices. Maternal infection was designated as the primary outcome, whereas postpartum hemorrhage, perineal lacerations, obstetrical anal sphincter injury, umbilical cord avulsion, low Apgar scores, neonatal aspiration requiring resuscitation, neonatal infection, neonatal mortality within 30 days of birth, and neonatal intensive care unit admission were considered secondary outcomes. RESULTS: Of the 20,642 articles identified, 52 were included in the meta-analyses. Based on data from observational studies, water birth was not associated with increased probability of maternal infection compared with land birth (10 articles, 113,395 pregnancies; odds ratio, 0.93; 95% confidence interval, 0.76-1.14). Patients undergoing water birth had decreased odds of postpartum hemorrhage (21 articles, 149,732 pregnancies; odds ratio, 0.80; 95% confidence interval, 0.68-0.94). Neonates delivered while submerged in water had increased odds of cord avulsion (10 articles, 91,504 pregnancies; odds ratio, 1.75; 95% confidence interval, 1.38-2.24) and decreased odds of low Apgar scores (21 articles, 165,917 pregnancies; odds ratio, 0.69; 95% confidence interval, 0.58-0.82), neonatal infection (15 articles, 53,635 pregnancies; odds ratio, 0.64; 95% confidence interval, 0.42-0.97), neonatal aspiration requiring resuscitation (19 articles, 181,001 pregnancies; odds ratio, 0.60; 95% confidence interval, 0.43-0.84), and neonatal intensive care unit admission (30 articles, 287,698 pregnancies; odds ratio, 0.56; 95% confidence interval, 0.45-0.70). CONCLUSION: When compared with land birth, water birth does not appear to increase the risk of most maternal and neonatal complications. Like any other delivery method, water birth has its unique considerations and potential risks, which health care providers and expectant parents should evaluate thoroughly. However, with proper precautions in place, water birth can be a reasonable choice for mothers and newborns, in facilities equipped to conduct water births safely.

Comparisons of the mean differences and standardized mean differences for continuous outcome measures on the same scale.

When conducting systematic reviews and meta-analyses of continuous outcomes, the mean differences (MDs) and standardized mean differences (SMDs) are 2 commonly used choices for effect measures. The SMDs are motivated by scenarios where studies collected in a systematic review do not report the continuous measures on the same scale. The standardization process transfers the MDs to be unit-free measures that can be synthesized across studies. As such, some evidence synthesis researchers tend to prefer the SMD over the MD. However, other researchers have concerns about the interpretability of the SMD. The standardization process could also yield additional heterogeneity between studies. In this paper, we use simulation studies to illustrate that, in a scenario where the continuous measures are on the same scale, the SMD could have considerably poorer performance compared with the MD in some cases. The simulations compare the MD and SMD in various settings, including cases where the normality assumption of continuous measures does not hold. We conclude that although the SMD remains useful for evidence synthesis of continuous measures on different scales, the SMD could have substantially greater biases, greater mean squared errors, and lower coverage probabilities of CIs than the MD. The MD is generally more robust to the violation of the normality assumption for continuous measures. In scenarios where continuous measures are inherently comparable or can be transformed to a common scale, the MD is the preferred choice for an effect measure.