One-stage laproendoscopic procedure versus two-stage procedure in the management for gallstone disease and biliary duct calculi: a systemic review and meta-analysis

Abstract Background The 2018 World Health Organization HIV guidelines were based on the results of a network meta-analysis (NMA) of published trials. This study employed individual patient-level data (IPD) and aggregate data (AgD) and meta-regression methods to assess the evidence supporting the WHO recommendations and whether they needed any refinements. Methods Access to IPD from three trials was granted through ClinicalStudyDataRequest.com (CSDR). Seven modelling approaches were applied and compared: 1) Unadjusted AgD network meta-analysis (NMA) – the original analysis; 2) AgD-NMA with meta-regression; 3) Two-stage IPD-AgD NMA; 4) Unadjusted one-stage IPD-AgD NMA; 5) One-stage IPD-AgD NMA with meta-regression (one-stage approach); 6) Two-stage IPD-AgD NMA with empirical-priors (empirical-priors approach); 7) Hierarchical meta-regression IPD-AgD NMA (HMR approach). The first two were the models used previously. Models were compared with respect to effect estimates, changes in the effect estimates, coefficient estimates, DIC and model fit, rankings and between-study heterogeneity. Results IPD were available for 2160 patients, representing 6.5% of the evidence base and 3 of 24 edges. The aspect of the model affected by the choice of modeling appeared to differ across outcomes. HMR consistently generated larger intervals, often with credible intervals (CrI) containing the null value. Discontinuations due to adverse events and viral suppression at 96 weeks were the only two outcomes for which the unadjusted AgD NMA would not be selected. For the first, the selected model shifted the principal comparison of interest from an odds ratio of 0.28 (95% CrI: 10.17, 0.44) to 0.37 (95% CrI: 0.23, 0.58). Throughout all outcomes, the regression estimates differed substantially between AgD and IPD methods, with the latter being more often larger in magnitude and statistically significant. Conclusions Overall, the use of IPD often impacted the coefficient estimates, but not sufficiently as to necessitate altering the final recommendations of the 2018 WHO Guidelines. Future work should examine the features of a network where adjustments will have an impact, such as how much IPD is required in a given size of network.

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Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

BMJ Open ◽

10.1136/bmjopen-2020-037405 ◽

2020 ◽

Vol 10 (10) ◽

pp. e037405

Author(s):

Daniel Dedman ◽

Melissa Cabecinha ◽

Rachael Williams ◽

Stephen J W Evans ◽

Krishnan Bhaskaran ◽

...

Keyword(s):

Systematic Review ◽

Primary Care ◽

Electronic Health Record ◽

Observational Studies ◽

Meta Analysis ◽

Data Sources ◽

Health Record ◽

Two Stage ◽

One Stage ◽

Electronic Health

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

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Chronic infections in hip arthroplasties: comparing risk of reinfection following one-stage and two-stage revision: a systematic review and meta-analysis

Clinical Epidemiology ◽

10.2147/clep.s29025 ◽

2012 ◽

pp. 57 ◽

Cited By ~ 94

Author(s):

Jeppe Lange ◽

Troelsen ◽

Reimar Thomsen ◽

Soballe

Keyword(s):

Systematic Review ◽

Meta Analysis ◽

Chronic Infections ◽

Two Stage ◽

One Stage ◽

Hip Arthroplasties

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Flexible piecewise linear model for investigating dose-response relationship in meta-analysis: methodology, examples, and comparison

10.7287/peerj.preprints.27277 ◽

2018 ◽

Cited By ~ 1

Author(s):

Chang Xu ◽

Lehana Thabane ◽

Tong-Zu Liu ◽

Ling Li ◽

Sayem Borhan ◽

...

Keyword(s):

Linear Model ◽

Dose Response ◽

Linear Trend ◽

Piecewise Linear ◽

Meta Analysis ◽

Continuous Exposure ◽

Response Relationship ◽

Dose Response Relationship ◽

Two Stage ◽

One Stage

Objectives: Dose-response meta-analysis (DRMA) is widely employed to establishing the potential dose-response relationship between continuous exposures and disease outcomes. However, no method is readily available for exploring the relation between a discrete exposure and a binary or continuous outcome. We proposed a piecewise linear (PL) DRMA model as a solution to this issue. Methods: We illustrated the methodology of PL model in both one-stage DRMA approach and two-stage DRMA approach. The method by testing the equality of slopes of each piecewise was employed to judge if there is “piecewise effect” against simple linear trend. We then used sleep (continuous exposure) and parity (discrete exposure) data as examples to illustrate how to apply PL model in DRMA using the Stata code attached. We also empirically compared the slopes of PL model with simple linear as well as restricted cubic spline (RCS) model. Results: Both one-stage and two-stage PL DRMA model fitted well in our examples, and the results were similar. Obvious “piecewise effects” were detected in both the two examples by the method we used. In our example, the PL model showed better fitting effect and practical reliable results compared to simple linear model, while similar results for to RCS model. Conclusion: Piecewise linear function is a simple and valid method for DRMA and can be used for discrete exposures. It also represents a superior model to linear model in DRMA and may be an alternative model to non-linear model.

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Multilevel Meta-Analysis of Individual Participant Data of Single-Case Experimental Designs: One-Stage versus Two-Stage Methods

Multivariate Behavioral Research ◽

10.1080/00273171.2020.1822148 ◽

2020 ◽

pp. 1-20

Author(s):

Lies Declercq ◽

Laleh Jamshidi ◽

Belén Fernández Castilla ◽

Mariola Moeyaert ◽

S. Natasha Beretvas ◽

...

Keyword(s):

Single Case ◽

Meta Analysis ◽

Experimental Designs ◽

Individual Participant Data ◽

Stage Versus ◽

Two Stage ◽

One Stage ◽

Individual Participant

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Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

BMC Medical Research Methodology ◽

10.1186/s12874-020-01205-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Loukia M. Spineli ◽

Katerina Papadimitropoulou ◽

Chrysostomos Kalyvas

Keyword(s):

Mixture Model ◽

Simulation Study ◽

Meta Analysis ◽

Credible Interval ◽

Outcome Data ◽

Systematic Bias ◽

Two Stage ◽

Pattern Mixture Model ◽

One Stage ◽

The One

Abstract Background Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The performance of the one-stage and the two-stage approaches has been documented extensively in the literature. However, little is known about how these approaches behave in the presence of missing outcome data (MOD), which are ubiquitous in clinical trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis using Bayesian methods to handle MOD appropriately. Methods We used 29 published networks to empirically compare the two approaches concerning the relative treatment effects of several competing interventions and the between-trial variance (τ2), while considering the extent and level of balance of MOD in the included trials. We additionally conducted a simulation study to compare the competing approaches regarding the bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and τ2 in the presence of moderate and large MOD. Results The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. For these networks, the empirical results revealed slightly higher τ2 estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and τ2 when compared with the two-stage approach for large MOD. Conclusions Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

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