scholarly journals Using Meta-Analysis and Propensity Score Methods to Assess Treatment Effects Toward Evidence-Based Practice in Extensive Reading

2020 ◽  
Vol 11 ◽  
Author(s):  
Akira Hamada
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Evidence Based Practice ◽  
Meta Analysis ◽  
Evidence Based ◽  
Extensive Reading ◽  
Propensity Score Methods

Building the house of evaluation: Reconsidering the blueprints and bricks for knowledge accumulation in evaluation

Evaluation ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. 185-201 ◽  
Author(s):  
Sebastian Lemire ◽  
Christina A. Christie
Keyword(s):  
Evidence Based Practice ◽  
Knowledge Building ◽  
Meta Analysis ◽  
Evidence Base ◽  
Evidence Based ◽  
The Public ◽  
Body Of Knowledge ◽  
What Works ◽  
Alternative Approaches ◽  
Cumulative Knowledge

The push for evidence-based practice is persistent in the public sector—what counts is what works. One central premise for evidence-based practice is the existence of an evidence base; that is, an accumulated and generalizable body of knowledge. Informed by a recent systematic review, we examine the promises and pitfalls of meta-analysis (the statistical workhorse of systematic reviews) as the primary blueprint for cumulative knowledge building in evaluation. This analysis suggests that the statistical assumptions underlying the meta-analytic framework raise issues that, at least in regards to producing generalizable knowledge, may cut even deeper than is suggested by common criticisms. Advancing beyond meta-analysis, we consider alternative approaches for knowledge building and reflect on the implications of these for individual evaluations.

The effectiveness of leadership: meta-analysis of trial, cross-sectional, and before after studies

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
V Restivo ◽  
M Gaeta ◽  
A Odone ◽  
C Trucchi ◽  
A Battaglini ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Evidence Based Practice ◽  
Meta Analysis ◽  
Healthcare Setting ◽  
Risk Difference ◽  
Audit And Feedback ◽  
Evidence Based ◽  
Cross Sectional ◽  
Cross Sectional Studies ◽  
Private Setting

Abstract Background The clinical and surgical procedures are often based on scientifical evidence but 30-40% of patients do not receive treatment according to evidence based medicine. The main aim of this review and meta-analysis is to assess the effectiveness of leadership in healthcare setting. Methods It was conducted a literature research on MEDLINE, Pubmed and Scopus with publication year between 2015 and 2019. The inclusion criteria were studies involving healthcare workers that evaluated effectiveness of opinion leaders in improving behaviour of healthcare workers, according to clinical or patient related outcomes. The quality of studies were assesed with the NHLBI for before after studies and the NOS for other study designs. The effect of leadership was assessed as risk difference for all studies with the exception of cross sectional studies. For the last it was evaluated correlation between leadership level and outcome measurment. Results A total of 3,155 articles were screened and 284 were fully assessed including 22 of them in the final database: 1 randomized trial, 9 cross sectional and 12 before after studies. For the cross-sectional studies there was a correlation of 0.22 (95% CI 0.15-0.28) between leadership level and outcome measurment. In the metaregression analysis the only factor that increased the correlation was private setting (meta regression coefficent =0.52, p = 0.022). The pooled efficacy was 24% (95% CI 10%-17%) for before after studies. Furthermore, a higher effectiveness was revealed in studies conducted on multi professional (24%) than single professional (9%) healthcare workers. Conclusions According to results, the guidelines adherence and task performance increased in a setting with leadership implementation. The leadership effectiveness appears comparable to other strategies as audit and feedback used to implement evidence-based practice in worldwide healthcare. Key messages The translation of evidence into clinical practice is often difficult but this study suggests that leaderhip can had higher effectiveness in multiprofessional healthcare workers and private setting. The effectiveness of leadership in this review suggests that it can be of help in order to make aware healthcare professionals about effectiveness of comply with evidence-based practice.

scholarly journals What is a multiple treatments meta-analysis?

2012 ◽  
Vol 21 (2) ◽  
pp. 151-153 ◽  
Author(s):  
A. Cipriani ◽  
C. Barbui ◽  
C. Rizzo ◽  
G. Salanti
Keyword(s):  
Evidence Based Medicine ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Evidence Based ◽  
Relative Efficacy ◽  
Analytical Technique ◽  
Multiple Treatments ◽  
Meta Analyses ◽  
Based Medicine ◽  
Indirect Comparisons

Standard meta-analyses are an effective tool in evidence-based medicine, but one of their main drawbacks is that they can compare only two alternative treatments at a time. Moreover, if no trials exist which directly compare two interventions, it is not possible to estimate their relative efficacy. Multiple treatments meta-analyses use a meta-analytical technique that allows the incorporation of evidence from both direct and indirect comparisons from a network of trials of different interventions to estimate summary treatment effects as comprehensively and precisely as possible.

Joint incorporation of randomised and observational evidence in estimating treatment effects

2017 ◽  
Vol 28 (1) ◽  
pp. 235-247 ◽  
Author(s):  
John Ferguson ◽  
Alberto Alvarez-Iglesias ◽  
John Newell ◽  
John Hinde ◽  
Martin O’ Donnell
Keyword(s):  
Observational Studies ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Randomised Trial ◽  
Observational Evidence ◽  
Estimation Accuracy ◽  
Evidence Based ◽  
Randomised Trials ◽  
Potential Gain ◽  
Based Medicine

In evidence-based medicine, randomised trials are regarded as a gold standard in estimating relative treatment effects. Nevertheless, a potential gain in precision is forfeited by ignoring observational evidence. We describe a simple estimator that combines treatment estimates from randomised and observational data and investigate its properties by simulation. We show that a substantial gain in estimation accuracy, compared with the estimator based solely on the randomised trial, is possible when the observational evidence has low bias and standard error. In the contrasting scenario where the observational evidence is inaccurate, the estimator automatically discounts its contribution to the estimated treatment effect. Meta-analysis extensions, combining estimators from multiple observational studies and randomised trials, are also explored.

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