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1.
Cytokine ; 141: 155444, 2021 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-33529888

RESUMO

BACKGROUND: Rosacea is a chronic inflammatory skin disease whose psychological consequences severely affect patient's quality of life. OBJECTIVE: To identify candidate genes of rosacea for potential development of new target therapies. METHODS: Gene Expression Omnibus datasets were retrieved to obtain differentially expressed genes (DEGs) between rosacea patients and healthy controls. Gene ontology (GO) analyses were used to identify functions of candidate genes. Related signaling pathways of DEGs were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis. Protein-protein interaction (PPI) networks were applied using search tools for the retrieval of interacting genes/proteins and modulations involving PPI networks were evaluated with use of the MCODE app. RESULTS: Samples from 19 rosacea patients and 10 healthy controls of dataset GSE65914 were enrolled. A total of 215 DEGs, 115 GO terms and 6 KEGG pathways were identified. A total of 182 nodes and 456 edges were enriched in PPI networks. Maximal clusters showed 15 central nodes and 96 edges. The toll-like receptor (TLR) signaling pathway was the most significant pathway detected and 5 DEGs were identified as candidate genes which included TLR2, C-C motif chemokine (CCL) 5, C-X-C motif chemokine ligand (CXCL) 9, CXCL10 and CXCL11. The results were verified in rosacea patients with use of real-time polymerase chain reaction and immunohistochemistry. Cell-type enrichment analysis revealed 8 lymphocytes that were enriched in rosacea patients. CONCLUSIONS: The results suggest that both innate and adaptive immune responses were involved in the etiology of rosacea. Five DEGs in the TLR signaling pathway may serve as potential therapeutic target genes.

2.
J Gen Intern Med ; 2021 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-33403620

RESUMO

BACKGROUND: Network meta-analysis (NMA) is a popular tool to compare multiple treatments in medical research. It is frequently implemented via Bayesian methods. The prior choice of between-study heterogeneity is critical in Bayesian NMAs. This study evaluates the impact of different priors for heterogeneity on NMA results. METHODS: We identified all NMAs with binary outcomes published in The BMJ, JAMA, and The Lancet during 2010-2018, and extracted information about their prior choices for heterogeneity. Our primary analyses focused on those with publicly available full data. We re-analyzed the NMAs using 3 commonly-used non-informative priors and empirical informative log-normal priors. We obtained the posterior median odds ratios and 95% credible intervals of all comparisons, assessed the correlation among different priors, and used Bland-Altman plots to evaluate their agreement. The kappa statistic was also used to evaluate the agreement among these priors regarding statistical significance. RESULTS: Among the selected Bayesian NMAs, 52.3% did not specify the prior choice for heterogeneity, and 84.1% did not provide rationales. We re-analyzed 19 NMAs with full data available, involving 894 studies, 173 treatments, and 395,429 patients. The correlation among posterior median (log) odds ratios using different priors were generally very strong for NMAs with over 20 studies. The informative priors produced substantially narrower credible intervals than non-informative priors, especially for NMAs with few studies. Bland-Altman plots and kappa statistics indicated strong overall agreement, but this was not always the case for a specific NMA. CONCLUSIONS: Priors should be routinely reported in Bayesian NMAs. Sensitivity analyses are recommended to examine the impact of priors, especially for NMAs with relatively small sample sizes. Informative priors may produce substantially narrower credible intervals for such NMAs.

3.
medRxiv ; 2020 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-33173884

RESUMO

OBJECTIVES: A recent paper by Doi et al. advocated completely replacing the relative risk (RR) with the odds ratio (OR) as the effect measure used to report the association between a treatment and a binary outcome in clinical trials and meta-analyses. Besides some practical advantages of RR over OR and the well-known issue of the OR being non-collapsible, Doi et al.'s key assumption that the OR is "portable" in the meta-analysis, i.e., study-specific ORs are likely not correlated with baseline risks, was not well justified. Study designs and settings: We summarized the Spearman's rank correlation coefficient between study-specific OR and the baseline risk in 40,243 meta-analyses from the Cochrane Database of Systematic Reviews (CDSR). RESULTS: Study-specific ORs are negatively correlated with baseline risk of disease (i.e., higher ORs tend to be observed in studies with lower baseline risks of disease) for most meta-analyses in CDSR. Using a meta-analysis comparing the effect of oral sumatriptan (100 mg) versus placebo on mitigating the acute headache at 2 hours after drug administration, we demonstrate that there is a strong negative correlation between OR (RR or RD) with the baseline risk and the conditional effects notably vary with baseline risks. CONCLUSIONS: Replacing RR or RD with OR is currently unadvisable in clinical trials and meta-analyses. It is possible that no effect measure is "portable" in a meta-analysis. In cases where portability of the effect measure is challenging to satisfy, we suggest presenting the conditional effect based on the baseline risk using a bivariate generalized linear mixed model. The bivariate generalized linear mixed model can be used to account for correlation between the effect measure and baseline disease risk. Furthermore, in addition to the overall (or marginal) effect, we recommend that investigators also report the effects conditioning on the baseline risk.

4.
Am J Epidemiol ; 2020 Sep 25.
Artigo em Inglês | MEDLINE | ID: mdl-32975277

RESUMO

Meta-analyses are undertaken to combine information from a set of studies, often in settings where some of the individual study-specific estimates are based on relatively small study samples. Finite sample bias may occur when maximum likelihood estimates of associations are obtained by fitting logistic regression models to sparse data sets. We show that combining information from small studies by undertaking a meta-analytic summary of logistic regression estimates can propagate such sparse data bias. In simulations, we illustrate two challenges encountered by meta-analyses of logistic regression results in settings of sparse data: bias in the summary meta-analytic result; and, confidence interval coverage that can worsen, rather than improve, in terms of being less than nominal, as the number of studies in the meta-analysis increases.

5.
J Am Med Dir Assoc ; 21(11): 1712-1717, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32739282

RESUMO

OBJECTIVES: The Preferences Assessment Tool (PAT) in the Minimum Data Set (MDS) 3.0 assesses 16 resident preferences for daily routines and activities. Although integrating important preferences into care planning is essential to provide person-centered care in nursing homes (NHs), preferences rated as important but unmet or unimportant may not receive much attention. This study aims to (1) identify the prevalence of unmet preferences and unimportant preferences, and (2) examine their associations with resident and facility-level characteristics. DESIGN: This is a longitudinal study of residents in NHs. SETTINGS AND PARTICIPANTS: We used data from 2012-2017 MDS assessments of long-stay residents aged 65 or older in 295 Minnesota NHs. In total, 51,859 assessments from 25,668 residents were included. METHODS: Generalized linear mixed models were used to analyze resident and facility-level characteristics associated with having any unmet preferences, and with the number of unimportant preferences. RESULTS: Across all years for both daily routine preferences and activity preferences, 3.3% to 5.1% of residents reported that at least 1 or more preference was important but unmet, and 10.0% to 16.6% reported that 4 or more out of the 8 preferences were unimportant. Residents with higher depressive symptoms, and poorer physical and sensory function were more likely to report unmet preferences. Residents with poorer physical and sensory function, and living in rural facilities and facilities having fewer activity staff hours per resident day were more likely to report unimportant preferences. CONCLUSIONS AND IMPLICATIONS: Residents with functional and sensory limitations and living in underresourced NHs are more likely to report that preferences are unimportant, or that they are important but unmet. It is important for staff to elicit preferences that truly matter for residents, and to enable residents to meet their preferences.

6.
Stat Interface ; 13(4): 425-436, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32742550

RESUMO

As an extension of pairwise meta-analysis of two treatments, network meta-analysis has recently attracted many researchers in evidence-based medicine because it simultaneously synthesizes both direct and indirect evidence from multiple treatments and thus facilitates better decision making. The Bayesian hierarchical model is a popular method to implement network meta-analysis, and it is generally considered more powerful than conventional pairwise meta-analysis, leading to more precise effect estimates with narrower credible intervals. However, the improvement of effect estimates produced by Bayesian network meta-analysis has never been studied theoretically. This article shows that such improvement depends highly on evidence cycles in the treatment network. When all treatment comparisons are assumed to have different heterogeneity variances, a network meta-analysis produces posterior distributions identical to separate pairwise meta-analyses for treatment comparisons that are not contained in any evidence cycles. However, this equivalence does not hold under the commonly-used assumption of a common heterogeneity variance for all comparisons. Simulations and a case study are used to illustrate the equivalence of the Bayesian network and pairwise meta-analyses in certain networks.

7.
J Sport Health Sci ; 2020 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-32745617

RESUMO

Continued advancement in the field of physical activity and health promotion relies heavily on the synthesis of rigorous scientific evidence. As such, systematic reviews and meta-analyses of randomized controlled trials have led to a better understanding of which intervention strategies are superior (i.e., produce the greatest effects) in physical activity-based health behavior change interventions. Indeed, standard meta-analytic approaches have allowed researchers in the field to synthesize relevant experimental evidence using pairwise procedures that produce reliable estimates of the homogeneity, magnitude, and potential biases in the observed effects. However, pairwise meta-analytic procedures are only capable to discerning differences in effects between a select intervention strategy and a select comparison or control condition. In order to maximize the impact of physical activity interventions on health-related outcomes, it is necessary to establish evidence concerning the comparative efficacy of all relevant physical activity intervention strategies. The development of network meta-analysis (NMA)-most commonly used in medical-based clinical trials-has allowed for the quantification of indirect comparisons, even in the absence of direct, head-to-head trials. Thus, it stands to reason that NMA can be applied in physical activity and health promotion research to identify the best intervention strategies. Given that this analysis technique is novel and largely unexplored in the field of physical activity and health promotion, care must be taken in its application to ensure reliable estimates and discernment of the effect sizes among interventions. Therefore, the purpose of this review is to comment on the potential application and importance of NMA in the field of physical activity and health promotion, describe how to properly and effectively apply this technique, and suggest important considerations for its appropriate application in this field. In this paper, overviews of the foundations of NMA and commonly used approaches for conducting NMA are provided, followed by assumptions related to NMA, opportunities and challenges in NMA, and a step-by-step example of developing and conducting an NMA.

8.
Stat Methods Med Res ; : 962280220945731, 2020 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-32757707

RESUMO

Network meta-analysis is a commonly used tool to combine direct and indirect evidence in systematic reviews of multiple treatments to improve estimation compared to traditional pairwise meta-analysis. Unlike the contrast-based network meta-analysis approach, which focuses on estimating relative effects such as odds ratios, the arm-based network meta-analysis approach can estimate absolute risks and other effects, which are arguably more informative in medicine and public health. However, the number of clinical studies involving each treatment is often small in a network meta-analysis, leading to unstable treatment-specific variance estimates in the arm-based network meta-analysis approach when using non- or weakly informative priors under an unequal variance assumption. Additional assumptions, such as equal (i.e. homogeneous) variances for all treatments, may be used to remedy this problem, but such assumptions may be inappropriately strong. This article introduces a variance shrinkage method for an arm-based network meta-analysis. Specifically, we assume different treatment variances share a common prior with unknown hyperparameters. This assumption is weaker than the homogeneous variance assumption and improves estimation by shrinking the variances in a data-dependent way. We illustrate the advantages of the variance shrinkage method by reanalyzing a network meta-analysis of organized inpatient care interventions for stroke. Finally, comprehensive simulations investigate the impact of different variance assumptions on statistical inference, and simulation results show that the variance shrinkage method provides better estimation for log odds ratios and absolute risks.

9.
Res Synth Methods ; 2020 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-32790064

RESUMO

Often clinicians are interested in determining whether a subject's measurement falls within a normal range, defined as a range of values of a continuous outcome which contains some proportion (eg, 95%) of measurements from a healthy population. Several studies in the biomedical field have estimated reference ranges based on a meta-analysis of multiple studies with healthy individuals. However, the literature currently gives no guidance about how to estimate the reference range of a new subject in such settings. Instead, meta-analyses of such normative range studies typically report the pooled mean as a reference value, which does not incorporate natural variation across healthy individuals in different studies. We present three approaches to calculating the normal reference range of a subject from a meta-analysis of normally or lognormally distributed outcomes: a frequentist random effects model, a Bayesian random effects model, and an empirical approach. We present the results of a simulation study demonstrating that the methods perform well under a variety of scenarios, though users should be cautious when the number of studies is small and between-study heterogeneity is large. Finally, we apply these methods to two examples: pediatric time spent awake after sleep onset and frontal subjective postural vertical measurements.

10.
medRxiv ; 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32817983

RESUMO

OBJECTIVES: High-quality meta-analyses on COVID-19 are in urgent demand for evidence-based decision making. However, conventional approaches exclude double-zero-event studies (DZS) from meta-analyses. We assessed whether including such studies impacts the conclusions in a recent systematic urgent review on prevention measures for preventing person-to-person transmission of COVID-19. Study designs and settings: We extracted data for meta-analyses containing DZS from a recent review that assessed the effects of physical distancing, face masks, and eye protection for preventing person-to-person transmission. A bivariate generalized linear mixed model was used to re-do the meta-analyses with DZS included. We compared the synthesized relative risks (RRs) of the three prevention measures, their 95% confidence intervals (CI), and significance tests (at the level of 0.05) including and excluding DZS. RESULTS: The re-analyzed COVID-19 data containing DZS involved a total of 1,784 participants who were not considered in the original review. Including DZS noticeably changed the synthesized RRs and 95% CIs of several interventions. For the meta-analysis of the effect of physical distancing, the RR of COVID-19 decreased from 0.15 (95% CI, 0.03 to 0.73) to 0.07 (95% CI, 0.01 to 0.98). For several meta-analyses, the statistical significance of the synthesized RR was changed. The RR of eye protection with a physical distance of 2 m and the RR of physical distancing when using N95 respirators were no longer statistically significant after including DZS. CONCLUSIONS: DZS may contain useful information. Sensitivity analyses that include DZS in meta-analysis are recommended.

11.
Biometrics ; 76(4): 1240-1250, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32720712

RESUMO

Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects include publication bias, outcome reporting bias, and clinical heterogeneity. Methods to account for small study effects in univariate meta-analysis have been extensively studied. However, detecting small study effects in a multivariate meta-analysis setting remains an untouched research area. One of the complications is that different types of selection processes can be involved in the reporting of multivariate outcomes. For example, some studies may be completely unpublished while others may selectively report multiple outcomes. In this paper, we propose a score test as an overall test of small study effects in multivariate meta-analysis. Two detailed case studies are given to demonstrate the advantage of the proposed test over various naive applications of univariate tests in practice. Through simulation studies, the proposed test is found to retain nominal Type I error rates with considerable power in moderate sample size settings. Finally, we also evaluate the concordance between the proposed tests with the naive application of univariate tests by evaluating 44 systematic reviews with multiple outcomes from the Cochrane Database.

12.
Epidemiology ; 31(5): 713-717, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32657954

RESUMO

Epidemiologic research often involves meta-analyses of proportions. Conventional two-step methods first transform each study's proportion and subsequently perform a meta-analysis on the transformed scale. They suffer from several important limitations: the log and logit transformations impractically treat within-study variances as fixed, known values and require ad hoc corrections for zero counts; the results from arcsine-based transformations may lack interpretability. Generalized linear mixed models (GLMMs) have been recommended in meta-analyses as a one-step approach to fully accounting for within-study uncertainties. However, they are seldom used in current practice to synthesize proportions. This article summarizes various methods for meta-analyses of proportions, illustrates their implementations, and explores their performance using real and simulated datasets. In general, GLMMs led to smaller biases and mean squared errors and higher coverage probabilities than two-step methods. Many software programs are readily available to implement these methods.

13.
J Clin Epidemiol ; 127: 29-39, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32659361

RESUMO

BACKGROUND AND OBJECTIVES: The network meta-analysis (NMA) is frequently used to synthesize evidence for multiple treatment comparisons, but its complexity may affect the robustness (or fragility) of the results. The fragility index (FI) is recently proposed to assess the fragility of the results from clinical studies and from pairwise meta-analyses. We extend the FI to NMAs with binary outcomes. METHODS: We define the FI for each treatment comparison in NMAs. It quantifies the minimal number of events necessary to be modified for altering the comparison's statistical significance. We introduce an algorithm to derive the FI and visualizations of the process. A worked example of smoking cessation data is used to illustrate the proposed methods. RESULTS: Some treatment comparisons had small FIs; their significance (or nonsignificance) could be altered by modifying a few events' status. They were related to various factors, such as P-values, event counts, and sample sizes, in the original NMA. After modifying event status, treatment ranking measures were also changed to different extents. CONCLUSION: Many NMAs include insufficiently compared treatments, small event counts, or small sample sizes; their results are potentially fragile. The FI offers a useful tool to evaluate treatment comparisons' robustness and reliability.

14.
J Aging Health ; : 898264320939006, 2020 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-32648793

RESUMO

Objectives: To investigate trends in racial/ethnic differences in nursing home (NH) residents' quality of life (QoL) and assess these patterns within and between facilities. Method: Data include resident-reported QoL surveys (n = 60,093), the Minimum Data Set, and facility-level characteristics (n = 376 facilities) for Minnesota. Hierarchical linear models were estimated to identify differences in QoL by resident race/ethnicity and facility racial/ethnic minority composition for 2011-2015. Results: White residents in low-proportion racial/ethnic minority facilities reported higher QoL than both minority and white residents in high-proportion minority facilities. While the year-to-year differences were not statistically significant, the point estimates for white-minority disparity widened over time. Discussion: Racial/ethnic differences in QoL are persistent and may be widening over time. The QoL disparity reported by minority residents and all residents in high-proportion minority facilities underscores the importance of examining NH structural characteristics and practices to ultimately achieve the goal of optimal, person-centered care in NHs.

15.
J Diabetes Complications ; 34(9): 107605, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32600893

RESUMO

AIMS: The association of hyperglycemia and duration of diabetes with intracranial atherosclerotic stenosis (ICAS) in the general population is not well documented. We examined whether elevated glucose and longer diabetes duration is independently associated with ICAS in a community-based sample. METHODS: We cross-sectionally analyzed 1644 participants (age 67-90 years) of the Atherosclerosis Risk in Communities Study who underwent cerebrovascular magnetic resonance angiography in 2011-13. We applied multivariable ordinal logistic regression to evaluate the association of ICAS category ("no stenosis", "stenosis <50%", or "stenosis ≥50%") with glucose or diabetes duration (<10, 10 to 20, and ≥20 years). We also obtained the corresponding odds ratios applying inverse-probability weighting to account for potential selection bias due to attrition. RESULTS: Compared to non-diabetic participants in the lowest glucose quartile, the weighted odds ratios (95% confidence interval) of higher ICAS category were 1.88 (1.18, 3.00) and 2.01 (1.08, 3.72) for non-diabetic and diabetic participants in the corresponding highest glucose quartile, respectively. We observed significant positive trends of ICAS across diabetes duration categories in unweighted, but not in weighted, analyses. CONCLUSIONS: Hyperglycemia and longer duration of diabetes were independently associated with ICAS, suggesting the importance of maintaining glycemic control to prevent stroke.

16.
Urology ; 2020 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-32679271

RESUMO

OBJECTIVES: To examine whether the school toilet environment at age 13, including bullying at toilets, is associated with female lower urinary tract symptoms (LUTS) at ages 13 and 19, as little is known about the association among school toilet environment, voiding behaviors, and LUTS in adolescent girls. METHODS: The sample comprised 3962 female participants from the Avon Longitudinal Study of Parents and Children. At age 13, participants reported on 7 school toilet environment characteristics and a range of LUTS items. At age 19, participants completed the Bristol Female Lower Urinary Tract Symptoms questionnaire. RESULTS: All toilet environmental factors were associated with at least 1 LUTS outcome at age 13. Holding behavior was associated with all school toilet environmental factors, with odds ratios ranging from 1.36 (95% confidence interval [CI]: 1.05, 1.76) for dirty toilets to 2.38 (95% CI: 1.60, 3.52) for feeling bullied at toilets. Bullying was associated with all daytime LUTS symptoms and nocturia; odds ratios ranged from 1.60 (95% CI: 1.04, 2.07) for nocturia to 2.90 (95% CI: 1.77, 4.75) for urgency. Associations between age 13 school toilets and age 19 LUTS were in the same direction as age 13 LUTS. CONCLUSION: This is the first examination of associations between school toilets and LUTS. Toileting environments were cross-sectionally associated with LUTS in adolescent girls. While further work is needed to determine whether these associations are causal, school toilet environments are modifiable and thus a promising target for LUTS prevention.

17.
Stat Med ; 39(23): 3105-3119, 2020 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-32510638

RESUMO

When conducting a meta-analysis involving prevalence data for an outcome with several subtypes, each of them is typically analyzed separately using a univariate meta-analysis model. Recently, multivariate meta-analysis models have been shown to correspond to a decrease in bias and variance for multiple correlated outcomes compared with univariate meta-analysis, when some studies only report a subset of the outcomes. In this article, we propose a novel Bayesian multivariate random effects model to account for the natural constraint that the prevalence of any given subtype cannot be larger than that of the overall prevalence. Extensive simulation studies show that this new model can reduce bias and variance when estimating subtype prevalences in the presence of missing data, compared with standard univariate and multivariate random effects models. The data from a rapid review on occupation and lower urinary tract symptoms by the Prevention of Lower Urinary Tract Symptoms Research Consortium are analyzed as a case study to estimate the prevalence of urinary incontinence and several incontinence subtypes among women in suspected high risk work environments.

18.
Stat Med ; 39(22): 2883-2900, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32495349

RESUMO

Bayesian analyses with the arm-based (AB) network meta-analysis (NMA) model require researchers to specify a prior distribution for the covariance matrix of the treatment-specific event rates in a transformed scale, for example, the treatment-specific log-odds when a logit transformation is used. The commonly used conjugate prior for the covariance matrix, the inverse-Wishart (IW) distribution, has several limitations. For example, although the IW distribution is often described as noninformative or weakly informative, it may in fact provide strong information when some variance components are small (eg, when the standard deviation of study-specific log-odds of a treatment is smaller than 1/2), as is common in NMAs with binary outcomes. In addition, the IW prior generally leads to underestimation of correlations between treatment-specific log-odds, which are critical for borrowing strength across treatment arms to estimate treatment effects efficiently and to reduce potential bias. Alternatively, several separation strategies (ie, separate priors on variances and correlations) can be considered. To study the IW prior's impact on NMA results and compare it with separation strategies, we did simulation studies under different missing-treatment mechanisms. A separation strategy with appropriate priors for the correlation matrix and variances performs better than the IW prior, and should be recommended as the default vague prior in the AB NMA approach. Finally, we reanalyzed three case studies and illustrated the importance, when performing AB-NMA, of sensitivity analyses with different prior specifications on variances.

19.
Res Synth Methods ; 11(5): 641-654, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32562361

RESUMO

When reporting the results of clinical studies, some researchers may choose the five-number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation (SD), particularly for skewed data. For these studies, when included in a meta-analysis, it is often desired to convert the five-number summary back to the sample mean and SD. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this article, we propose to further advance the literature by developing a smoothly weighted estimator for the sample SD that fully utilizes the sample size information. For ease of implementation, we also derive an approximation formula for the optimal weight, as well as a shortcut formula for the sample SD. Numerical results show that our new estimator provides a more accurate estimate for normal data and also performs favorably for non-normal data. Together with the optimal sample mean estimator in Luo et al., our new methods have dramatically improved the existing methods for data transformation, and they are capable to serve as "rules of thumb" in meta-analysis for studies reported with the five-number summary. Finally for practical use, an Excel spreadsheet and an online calculator are also provided for implementing our optimal estimators.

20.
BMC Med Res Methodol ; 20(1): 152, 2020 06 11.
Artigo em Inglês | MEDLINE | ID: mdl-32539721

RESUMO

BACKGROUND: In meta-analyses of a binary outcome, double zero events in some studies cause a critical methodology problem. The generalized linear mixed model (GLMM) has been proposed as a valid statistical tool for pooling such data. Three parameter estimation methods, including the Laplace approximation (LA), penalized quasi-likelihood (PQL) and adaptive Gauss-Hermite quadrature (AGHQ) were frequently used in the GLMM. However, the performance of GLMM via these estimation methods is unclear in meta-analysis with zero events. METHODS: A simulation study was conducted to compare the performance. We fitted five random-effects GLMMs and estimated the results through the LA, PQL and AGHQ methods, respectively. Each scenario conducted 20,000 simulation iterations. The data from Cochrane Database of Systematic Reviews were collected to form the simulation settings. The estimation methods were compared in terms of the convergence rate, bias, mean square error, and coverage probability. RESULTS: Our results suggested that when the total events were insufficient in either of the arms, the GLMMs did not show good point estimation to pool studies of rare events. The AGHQ method did not show better properties than the LA estimation in terms of convergence rate, bias, coverage, and possibility to produce very large odds ratios. In addition, although the PQL had some advantages, it was not the preferred option due to its low convergence rate in some situations, and the suboptimal point and variance estimation compared to the LA. CONCLUSION: The GLMM is an alternative for meta-analysis of rare events and is especially useful in the presence of zero-events studies, while at least 10 total events in both arms is recommended when employing GLMM for meta-analysis. The penalized quasi-likelihood and adaptive Gauss-Hermite quadrature are not superior to the Laplace approximation for rare events and thus they are not recommended.

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