Appropriateness of conducting and reporting random-effects meta-analysis in oncology

A random-effects model is often applied in meta-analysis when considerable heterogeneity among studies is observed due to the differences in patient characteristics, timeframe, treatment regimens, and other study characteristics. Since 2014, the journals Research Synthesis Methods and the Annals of Internal Medicine have published a few noteworthy papers that explained why the most widely used method for pooling heterogeneous studies-the DerSimonian-Laird (DL) estimator-can produce biased estimates with falsely high precision and recommended to use other several alternative methods. Nevertheless, more than half of studies (55.7%) published in top oncology-specific journals during 2015-2022 did not report any detailed method in the random-effects meta-analysis. Of the studies that did report the methodology used, the DL method was still the dominant one reported. Thus, while the authors recommend that Research Synthesis Methods and the Annals of Internal Medicine continue to increase the publication of its articles that report on specific methods for handling heterogeneity and use random-effects estimates that provide more accurate confidence limits than the DL estimator, other journals that publish meta-analyses in oncology (and presumably in other disease areas) are urged to do the same on a much larger scale than currently documented.

Prevalence of neuropsychiatric disorders in patients with systemic lupus erythematosus in Pakistan: A systematic review and meta-analysis

INTRODUCTION

By conducting a systematic review and meta-analysis, we investigated the prevalence of neuropsychiatric (NP) symptoms among systemic lupus erythematosus (SLE) patients in Pakistan.

METHODS

In this review work, three electronic databases (Web of Science, MEDLINE, and Google Scholar) and local databases were screened for 20 years from 1 January 2002 to 30 September 2022, to identify the articles evaluating the prevalence of NP symptoms in SLE patients in Pakistan. We performed a random-effects meta-analysis to estimate the prevalence of NPSLE. Statistical heterogeneity was measured by the I2 index, and subgroup meta-analyses were used to access the statistical heterogeneity. Furthermore, meta-regression models were used to examine the associations between prevalence estimates and study characteristics of interest. Three independent authors reviewed existing studies, extracted data, and rated the qualities of selected studies. This review was registered on PROSPERO (Registration no. CRD42022361798).

RESULTS

Thirteen studies met the inclusion criteria out of the 322 studies with a total of 2,003 SLE patients for this systematic review and meta-analysis. The prevalence of NP disorders in SLE patients was estimated to be 30.42% (95% CI:18.26-44.11%), with cognitive dysfunction being the most common (31.51%; 95% CI:1.28-76.27%), followed by headache (10.22%; 95% CI: 0.00-33.43%), seizures (5.96%; 95% CI: 3.80-8.53%), psychosis (3.64%; 95% CI: 2.38-5.13%), and neuropathy is the least common (0.86%; 95% CI: 0.00-2.74%). The heterogeneity between studies was significant (p < 0.01). The pooled prevalence of NP disorders among SLE patients was found highest in Punjab (41.21%) and lowest in Sindh (17.60%).

CONCLUSION

Findings from this study revealed that SLE patients have a high prevalence of NP disorders. The most common symptoms were cognitive dysfunctions, headaches, seizures, psychosis, and neuropathy. Clinicians can manage these potentially deadly and disabling diseases more effectively if they understand the incidence of each NP symptom in SLE patients. NP symptoms among SLE patients are at their peak in Pakistan; policymakers should devise preventive strategies to curb the disease.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero/display_record. php?RecordID=361798, identifier CRD42022361798.

A practical guide to random-effects Bayesian meta-analyses with application to the psychological trauma and suicide literature

OBJECTIVE

Bayesian meta-analyses offer several advantages over traditional approaches, including improved accuracy when using a small number of studies and enhanced estimation of heterogeneity. However, psychological trauma research has yet to see widespread adoption of these statistical methods, potentially due to researchers' unfamiliarity with the processes involved. The purpose of this article is to provide a practical tutorial for conducting random-effects Bayesian meta-analyses.

METHOD

Explanations and recommendations are provided for completing the primary steps of a Bayesian meta-analysis, ranging from model specification to interpretation of results. Furthermore, an illustrative example is used to demonstrate the application of each step. In the example, results are synthesized from six studies included in a previously published systematic review (Holliday et al., 2020), with a combined sample size of 21,244,109, examining the association between posttraumatic stress disorder and risk of suicide in veterans and military personnel.

RESULTS

The posterior distributions for each model estimate, such as the pooled effect size and the heterogeneity parameter, are discussed and interpreted with regard to the probability of increased suicide risk.

CONCLUSIONS

Our hope is that this tutorial, along with the provided data and code, facilitate the use of Bayesian meta-analyses in the study of psychological trauma. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Compliance Indicators of COVID-19 Prevention and Vaccines Hesitancy in Kenya: A Random-Effects Endogenous Probit Model

Vaccine hesitancy remains a major public health concern in the effort towards addressing the COVID-19 pandemic. This study analyzed the effects of indicators of compliance with preventive practices on the willingness to take COVID-19 vaccines in Kenya. The data were from the COVID-19 Rapid Response Phone Surveys conducted between January and June 2021 during the fourth and fifth waves. The data were analyzed with the random-effects endogenous Probit regression model, with estimated parameters tested for robustness and stability. The results showed that willingness to take vaccines increased between the fourth and fifth waves. Compliance with many of the preventive practices also improved, although the utilizations of immune system-promoting practices were very low. The panel Probit regression results showed that compliance indicators were truly endogenous and there was existence of random effects. Immune system-boosting and contact-prevention indicators significantly increased and decreased the willingness to take vaccines, respectively (p < 0.01). The experience of mental health disorders in the form of nervousness and hopelessness also significantly influenced vaccine hesitancy (p < 0.10). Willingness to take vaccines also significantly increased among older people and those with a formal education (p < 0.01). Different forms of association exist between vaccine hesitancy and the prevention compliance indicators. There is a need to properly sensitize the people to the need to complement compliance with COVID-19 contact-prevention indicators with vaccination. Addressing mental health disorders in the form of loneliness, nervousness, depression, hopelessness and anxiety should also become the focus of public health, while efforts to reduce vaccine hesitancy should focus on individuals without formal education, males and youths.

A permutation-based approach for heterogeneous meta-analyses of rare events

The increasingly widespread use of meta-analysis has led to growing interest in meta-analytic methods for rare events and sparse data. Conventional approaches tend to perform very poorly in such settings. Recent work in this area has provided options for sparse data, but these are still often hampered when heterogeneity across the available studies differs based on treatment group. We propose a permutation-based approach based on conditional logistic regression that accommodates this common contingency, providing more reliable statistical tests when such patterns of heterogeneity are observed. We find that commonly used methods can yield highly inflated Type I error rates, low confidence interval coverage, and bias when events are rare and non-negligible heterogeneity is present. Our method often produces much lower Type I error rates and higher confidence interval coverage than traditional methods in these circumstances. We illustrate the utility of our method by comparing it to several other methods via a simulation study and analyzing an example data set, which assess the use of antibiotics to prevent acute rheumatic fever.

A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses

Studies combined in a meta-analysis often have differences in their design and conduct that can lead to heterogeneous results. A random-effects model accounts for these differences in the underlying study effects, which includes a heterogeneity variance parameter. The DerSimonian-Laird method is often used to estimate the heterogeneity variance, but simulation studies have found the method can be biased and other methods are available. This paper compares the properties of nine different heterogeneity variance estimators using simulated meta-analysis data. Simulated scenarios include studies of equal size and of moderate and large differences in size. Results confirm that the DerSimonian-Laird estimator is negatively biased in scenarios with small studies and in scenarios with a rare binary outcome. Results also show the Paule-Mandel method has considerable positive bias in meta-analyses with large differences in study size. We recommend the method of restricted maximum likelihood (REML) to estimate the heterogeneity variance over other methods. However, considering that meta-analyses of health studies typically contain few studies, the heterogeneity variance estimate should not be used as a reliable gauge for the extent of heterogeneity in a meta-analysis. The estimated summary effect of the meta-analysis and its confidence interval derived from the Hartung-Knapp-Sidik-Jonkman method are more robust to changes in the heterogeneity variance estimate and show minimal deviation from the nominal coverage of 95% under most of our simulated scenarios.

One-stage dose-response meta-analysis for aggregated data

The standard two-stage approach for estimating non-linear dose-response curves based on aggregated data typically excludes those studies with less than three exposure groups. We develop the one-stage method as a linear mixed model and present the main aspects of the methodology, including model specification, estimation, testing, prediction, goodness-of-fit, model comparison, and quantification of between-studies heterogeneity. Using both fictitious and real data from a published meta-analysis, we illustrated the main features of the proposed methodology and compared it to a traditional two-stage analysis. In a one-stage approach, the pooled curve and estimates of the between-studies heterogeneity are based on the whole set of studies without any exclusion. Thus, even complex curves (splines, spike at zero exposure) defined by several parameters can be estimated. We showed how the one-stage method may facilitate several applications, in particular quantification of heterogeneity over the exposure range, prediction of marginal and conditional curves, and comparison of alternative models. The one-stage method for meta-analysis of non-linear curves is implemented in the dosresmeta R package. It is particularly suited for dose-response meta-analyses of aggregated where the complexity of the research question is better addressed by including all the studies.

Finding the power to reduce publication bias

The central purpose of this study is to document how a sharper focus upon statistical power may reduce the impact of selective reporting bias in meta-analyses. We introduce the weighted average of the adequately powered (WAAP) as an alternative to the conventional random-effects (RE) estimator. When the results of some of the studies have been selected to be positive and statistically significant (i.e. selective reporting), our simulations show that WAAP will have smaller bias than RE at no loss to its other statistical properties. When there is no selective reporting, the difference between RE's and WAAP's statistical properties is practically negligible. Nonetheless, when selective reporting is especially severe or heterogeneity is very large, notable bias can remain in all weighted averages. The main limitation of this approach is that the majority of meta-analyses of medical research do not contain any studies with adequate power (i.e. >80%). For such areas of medical research, it remains important to document their low power, and, as we demonstrate, an alternative unrestricted weighted least squares weighted average can be used instead of WAAP. Copyright © 2017 John Wiley & Sons, Ltd.

An empirical comparison of heterogeneity variance estimators in 12 894 meta-analyses

Heterogeneity in meta-analysis is most commonly estimated using a moment-based approach described by DerSimonian and Laird. However, this method has been shown to produce biased estimates. Alternative methods to estimate heterogeneity include the restricted maximum likelihood approach and those proposed by Paule and Mandel, Sidik and Jonkman, and Hartung and Makambi. We compared the impact of these five methods on the results of 12,894 meta-analyses extracted from the Cochrane Database of Systematic Reviews. We compared the methods in terms of the following: (1) the extent of heterogeneity, expressed as an I(2) statistic; (2) the overall effect estimate; (3) the precision of the overall effect estimate; and (4) p-values testing the no effect hypothesis. Results suggest that, in some meta-analyses, I(2) estimates differ by more than 50% when different heterogeneity estimators are used. Conclusions naively based on statistical significance (at a 5% level) were discordant for at least one pair of estimators in 7.5% of meta-analyses, indicating that the choice of heterogeneity estimator could affect the conclusions of a meta-analysis. These findings imply that using a single estimate of heterogeneity may lead to non-robust results in some meta-analyses, and researchers should consider using alternatives to the DerSimonian and Laird method.