scholarly journals Estimating the contribution of studies in network meta-analysis: paths, flows and streams

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 610
Author(s):  
Theodoros Papakonstantinou ◽  
Adriani Nikolakopoulou ◽  
Gerta Rücker ◽  
Anna Chaimani ◽  
Guido Schwarzer ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Direct Evidence ◽  
Meta Analysis ◽  
Effect Estimate ◽  
Analysis Model ◽  
Direct Treatment ◽  
Analysis Methodology ◽  
Treatment Effect Estimate ◽  
Quinolone Antibiotics ◽  
Hat Matrix

In network meta-analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the network. The percentage contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from network meta-analysis, is crucial in this context. We use ideas from graph theory to derive the percentage that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step network meta-analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to percentage contributions based on the observation that the rows of H can be interpreted as flow networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the percentage contributions of direct evidence from individual studies to network treatment effects.

scholarly journals Estimating the contribution of studies in network meta-analysis: paths, flows and streams

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 610 ◽  
Author(s):  
Theodoros Papakonstantinou ◽  
Adriani Nikolakopoulou ◽  
Gerta Rücker ◽  
Anna Chaimani ◽  
Guido Schwarzer ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Direct Evidence ◽  
Meta Analysis ◽  
Effect Estimate ◽  
Analysis Model ◽  
Direct Treatment ◽  
Analysis Methodology ◽  
Treatment Effect Estimate ◽  
Quinolone Antibiotics ◽  
Hat Matrix

In network meta-analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the network. The proportion contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from network meta-analysis, is crucial in this context. We use ideas from graph theory to derive the proportion that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step network meta-analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to proportion contributions based on the observation that the rows of H can be interpreted as flow networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the proportion contributions of direct evidence from individual studies to network treatment effects.

scholarly journals Estimating the contribution of studies in network meta-analysis: paths, flows and streams

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 610 ◽  
Author(s):  
Theodoros Papakonstantinou ◽  
Adriani Nikolakopoulou ◽  
Gerta Rücker ◽  
Anna Chaimani ◽  
Guido Schwarzer ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Direct Evidence ◽  
Meta Analysis ◽  
Effect Estimate ◽  
Analysis Model ◽  
Direct Treatment ◽  
Analysis Methodology ◽  
Treatment Effect Estimate ◽  
Quinolone Antibiotics ◽  
Hat Matrix

In network meta-analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the network. The percentage contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from network meta-analysis, is crucial in this context. We use ideas from graph theory to derive the percentage that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step network meta-analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to percentage contributions based on the observation that the rows of H can be interpreted as flow networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the percentage contributions of direct evidence from individual studies to network treatment effects.

scholarly journals Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Nicole Bohme Carnegie ◽  
Rui Wang ◽  
Victor De Gruttola
Keyword(s):  
Infectious Disease ◽  
Disease Prevention ◽  
Social Interactions ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Effect Estimate ◽  
Treatment Effect Estimate ◽  
Partial Interference ◽  
The Difference ◽  
Cluster Randomized

AbstractAn issue that remains challenging in the field of causal inference is how to relax the assumption of no interference between units. Interference occurs when the treatment of one unit can affect the outcome of another, a situation which is likely to arise with outcomes that may depend on social interactions, such as occurrence of infectious disease. Existing methods to accommodate interference largely depend upon an assumption of “partial interference” – interference only within identifiable groups but not among them. There remains a considerable need for development of methods that allow further relaxation of the no-interference assumption. This paper focuses on an estimand that is the difference in the outcome that one would observe if the treatment were provided to all clusters compared to that outcome if treatment were provided to none – referred as the overall treatment effect. In trials of infectious disease prevention, the randomized treatment effect estimate will be attenuated relative to this overall treatment effect if a fraction of the exposures in the treatment clusters come from individuals who are outside these clusters. This source of interference – contacts sufficient for transmission that are with treated clusters – is potentially measurable. In this manuscript, we leverage epidemic models to infer the way in which a given level of interference affects the incidence of infection in clusters. This leads naturally to an estimator of the overall treatment effect that is easily implemented using existing software.

scholarly journals Impact of informative censoring on the treatment effect estimate of disability worsening in multiple sclerosis clinical trials

2020 ◽  
Vol 39 ◽  
pp. 101865
Author(s):  
Katherine Riester ◽  
Ludwig Kappos ◽  
Krzysztof Selmaj ◽  
Stacy Lindborg ◽  
Ilya Lipkovich ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Treatment Effect ◽  
Informative Censoring ◽  
Effect Estimate ◽  
Treatment Effect Estimate

The Mechanics of Treatment-effect Estimate Bias for Nonexperimental Data

2019 ◽  
pp. 004912411985237
Author(s):  
Roberto V. Penaloza ◽  
Mark Berends
Keyword(s):  
Treatment Effect ◽  
Latent Variable ◽  
Value Added ◽  
Effect Estimate ◽  
Education Production Function ◽  
Education Production ◽  
Group Assignment ◽  
Treatment Effect Estimate ◽  
Flexible Form ◽  
Variable Errors

To measure “treatment” effects, social science researchers typically rely on nonexperimental data. In education, school and teacher effects on students are often measured through value-added models (VAMs) that are not fully understood. We propose a framework that relates to the education production function in its most flexible form and connects with the basic VAMs without using untenable assumptions. We illustrate how, due to measurement error (ME), cross-group imbalances created by nonrandom group assignment cause correlations that drive the models’ treatment-effect estimate bias. We derive formulas to calculate bias and rank the models and show that no model is better in all situations. The framework and formulas’ workings are verified and illustrated via simulation. We also evaluate the performance of latent variable/errors-in-variables models that handle ME and study the role of extra covariates including lags of the outcome.

Safety and Efficacy of Rifaximin in Prophylaxis of Spontaneous Bacterial Peritonitis: A Systematic Review and Meta-analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 380-387 ◽  
Author(s):  
Amr Menshawy ◽  
Omar Mattar ◽  
Kirolos Barssoum ◽  
Ammar M. AboEl-Naga ◽  
Haitham Mohamed Salim ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Success Rate ◽  
Spontaneous Bacterial Peritonitis ◽  
Meta Analysis ◽  
Final Analysis ◽  
Effect Estimate ◽  
Analysis Model ◽  
Bacterial Peritonitis ◽  
Significant Difference

Aim: The role of rifaximin in the prevention of Spontaneous Bacterial Peritonitis (SBP) is not well studied. The aim of this meta-analysis was to evaluate the role of rifaximin in the prevention of SBP. Methods: A computerized literature search for relevant clinical trials was conducted during August 2017. Data on Frequency of SBP, the success rate of prevention of SBP, mortality rate, hepatorenal syndrome, septic shock, hepatic encephalopathy, and GIT bleeding were extracted and pooled as Risk Ratio (RR) with their 95% Confidence Interval (CI) in a meta-analysis model. Heterogeneity was assessed by Chi-square test. Results: Six studies involving 973 patients were included in the final analysis. The pooled effect estimate showed that the rifaximin plus norfloxacin group had less incidence of SBP (RR 0.58, 95% CI[0.37, 0.92], P=0.02) and hepatic encephalopathy (RR 0.38, 95% CI[0.17, 0.84], P=0.02) than the norfloxacin-based regimen group. No significant difference between rifaximin and norfloxacin in terms of frequency of SBP and success rate of primary prevention of SBP (RR 0.49, 95% CI [0.24, 1.01], P=0.05; RR1.21, 95% CI [0.95, 1.55], P=0.13, respectively). Conclusion: Based on our analysis, Rifaximin is a promising drug and appears to be a good alternative to norfloxacin in the prevention of SBP.

scholarly journals How should systematic reviewers handle conference abstracts? A view from the trenches

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Roberta W. Scherer ◽  
Ian J. Saldanha
Keyword(s):  
Systematic Reviews ◽  
Treatment Effect ◽  
Meta Analysis ◽  
Good Practice ◽  
Effect Estimate ◽  
Main Body ◽  
Unpublished Studies ◽  
Study Results ◽  
Meta Analyses ◽  
Positive Results

Abstract Background While identifying and cataloging unpublished studies from conference proceedings is generally recognized as a good practice during systematic reviews, controversy remains whether to include study results that are reported in conference abstracts. Existing guidelines provide conflicting recommendations. Main body The main argument for including conference abstracts in systematic reviews is that abstracts with positive results are preferentially published, and published sooner, as full-length articles compared with other abstracts. Arguments against including conference abstracts are that (1) searching for abstracts is resource-intensive, (2) abstracts may not contain adequate information, and (3) the information in abstracts may not be dependable. However, studies comparing conference abstracts and fully published articles of the same study find only minor differences, usually with conference abstracts presenting preliminary results. Other studies that have examined differences in treatment estimates of meta-analyses with and without conference abstracts report changes in precision, but usually not in the treatment effect estimate. However, in some cases, including conference abstracts has made a difference in the estimate of the treatment effect, not just its precision. Instead of arbitrarily deciding to include or exclude conference abstracts in systematic reviews, we suggest that systematic reviewers should consider the availability of evidence informing the review. If available evidence is sparse or conflicting, it may be worthwhile to search for conference abstracts. Further, attempts to contact authors of abstracts or search for protocols or trial registers to supplement the information presented in conference abstracts is prudent. If unique information from conference abstracts is included in a meta-analysis, a sensitivity analysis with and without the unique results should be conducted. Conclusions Under given circumstances, it is worthwhile to search for and include results from conference abstracts in systematic reviews.

scholarly journals Efficacy and safety of polyethylene glycol dural sealant system in cranial and spinal neurosurgical procedures: Meta-analysis

2021 ◽  
Vol 12 ◽  
pp. 182
Author(s):  
Ahmed Diab ◽  
Hieder Al-Shami ◽  
Ahmed Negida ◽  
Ahmed Gadallah ◽  
Hossam Farag ◽  
...  
Keyword(s):  
Polyethylene Glycol ◽  
Meta Analysis ◽  
Neurological Deficits ◽  
Csf Leak ◽  
Cochrane Library ◽  
Effect Estimate ◽  
Neurosurgical Procedures ◽  
Analysis Model ◽  
Significant Difference ◽  
Watertight Closure

Background: We aimed to assess the efficacy of polyethylene glycol (PEG) dura sealant to achieve watertight closure, prevention of cerebrospinal fluid (CSF) leak and to investigate its possible side effects. Methods: We searched Medline (through PubMed), Scopus, and the Cochrane Library through December 2019. We included articles demonstrating cranial or spinal procedures with the use of PEG material as a dural sealant. Data on intraoperative watertight closure, CSF leak, and surgical complications were extracted and pooled in a meta-analysis model using RevMan version 5.3 and OpenMeta (Analyst). Results: Pooling the controlled trials showed that PEG resulted in significantly more intraoperative watertight closures than standard care (risk ratio [RR] = 1.44, 95% confidence interval [CI] [1.24, 1.66]). However, the combined effect estimate did not reveal any significant difference between both groups in terms of CSF leaks, the incidence of surgical site infections, and neurological deficits (P = 0.7, 0.45, and 0.92, respectively). On the other hand, pooling both controlled and noncontrolled trials showed significance in terms of leak and neurological complications (RR = 0.0238, 95% CI [0.0102, 0.0373] and RR = 0.035, 95% CI [0.018, 0.052]). Regarding intraoperative watertight closure, the overall effect estimate showed no significant results (RR=0.994, 95% CI [0.986, 1.002]). Conclusion: Dura seal material is an acceptable adjuvant for dural closure when the integrity of the dura is questionable. However, marketing it as a factor for the prevention of surgical site infection is not scientifically proved. We suggest that, for neurosurgeons, using the dural sealants are highly recommended for duraplasty, skull base approaches, and in keyhole approaches.

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