scholarly journals A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jeong-Hwa Yoon ◽  
Sofia Dias ◽  
Seokyung Hahn
Keyword(s):  
Meta Analysis ◽  
Indirect Evidence ◽  
Mixed Treatment Comparison ◽  
Data Imputation ◽  
Reliability Of Results ◽  
Treatment Comparison ◽  
Randomized Controlled ◽  
Complete Network ◽  
Mixed Treatment ◽  
Different Levels

Abstract Background In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency. Methods The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets. Results Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated. Conclusions Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.

A Network Meta-Analysis of Randomized Controlled Trials Directed at Treating Lateral Epicondylalgia

2021 ◽  
Vol 31 (4) ◽  
pp. 17-33
Author(s):  
James William Price
Keyword(s):  
Corticosteroid Injection ◽  
Meta Analysis ◽  
Low Level Laser Therapy ◽  
Mixed Treatment Comparison ◽  
Effective Measure ◽  
Treatment Comparison ◽  
Randomized Controlled ◽  
Mixed Treatment ◽  
Lateral Epicondylalgia ◽  
Lateral Epicondylosis

Abstract Background: Lateral epicondylosis is the most prevalent cause of lateral elbow pain, occurring in 4 per 1000 patients. The aim of most treatments is to reduce inflammation even with histological evidence demonstrating that lateral epicondylosis is a non-inflammatory condition. Objective: To determine the relative merits of the different regimens used to diminish lateral epicondylosis pain using a mixed treatment comparison/network meta-analysis (NMA). Methods: A thorough literature search was performed. The eligibility criteria for this mixed treatment comparison were: randomized controlled clinical trials; human subjects; working age population (16 to 70 years); the outcome measure was an objective pain assessment; measured at a 1- to 3-month follow-up. The NMA were performed using the GeMTC user interface for automated NMA utilizing a Bayesian Hierarchical Model of random effects. The evaluation of confidence in the findings from NMA was performed using a semi-automated platform called CINeMA (Confidence in Network Meta-Analysis). Results: The model suggests that articulation technique is the most effective measure for decreasing lateral epicondylalgia followed by topical nitrates, acupuncture, kinesiology taping and low-level laser therapy, respectively. Muscle energy technique, local corticosteroid injection, prolotherapy and counterforce bracing displayed a trend toward being less effective than placebo. Conclusions: The results suggest that the most effective modalities for improving lateral epicondylalgia are those that decrease muscle tone and those that improve circulation, while measures meant to decrease inflammation appear to be of no or limited benefit.

scholarly journals A mixed treatment comparison of selected osteopathic techniques used to treat acute nonspecific low back pain: a proof of concept and plan for further research

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
James W. Price
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Spinal Manipulation ◽  
Conventional Care ◽  
Mixed Treatment Comparison ◽  
Low Back ◽  
Acute Low Back Pain ◽  
Treatment Comparison ◽  
Randomized Controlled ◽  
Mixed Treatment

Abstract Context Back injuries have a high prevalence in the United States and can be costly for both patients and the healthcare system at large. While previous guidelines from the American College of Physicians for the management of acute nonspecific low back pain (ANLBP) have encouraged nonpharmacologic management, those treatment recommendations involved only superficial heat, massage, acupuncture, and spinal manipulation. Investigation about the efficacy of spinal manipulation in the management of ANLBP is warranted. Objectives To compare the results in previously-published literature documenting the outcomes of osteopathic manipulative treatment (OMT) techniques used to treat ANLBP. The secondary objective of this study was to demonstrate the utility of using Bayesian network meta-analysis (NMA) to perform a mixed treatment comparison (MTC) of a variety of osteopathic techniques. Methods A literature search for randomized controlled trials (RCTs) of ANLBP treatments was performed in April 2020 according to PRISMA guidelines by searching MEDLINE/PubMed, OVID, Cochrane Central, PEDro, and OSTMED.Dr databases; scanning the reference lists of articles; and using the Canadian Agency for Drugs and Technologies in Health grey literature checklist. Each database was searched from inception to April 1, 2020. The following search terms were used: acute low back pain, acute low back pain plus physical therapy, acute low back pain plus spinal manipulation, and acute low back pain plus osteopathic manipulation. The validity of eligible trials was assessed by the single author using an adapted National Institute for Health and Care Excellence methodology checklist for randomized, controlled trials and an extraction form based on that checklist. The outcome measure chosen for this NMA was the Visual Analogue Scale of pain. The NMA were performed using the GeMTC user interface for automated NMA utilizing a Bayesian hierarchical model of random effects. Results The literature search initially found 483 unduplicated records. After screening and full text assessment, five RCTs were eligible for the MTC, yielding a total of 430 participants. Results of the MTC model suggested that there was no statistically significant decrease in reported pain when exercise, high-velocity low-amplitude (HVLA), counterstrain, muscle energy technique, or a mix of techniques were added to conventional treatment to treat ANLBP. However, the rank probabilities assessment determined that HVLA and the OMT mixed treatment protocol plus conventional care were ranked superior to conventional care alone for improving ANLBP. Conclusions While this study failed to provide definitive evidence upon which clinical recommendations can be based, it does demonstrate the utility of performing NMA for MTCs of osteopathic modalities used to treat ANLBP. However, to take full advantage of this statistical technique, future studies should be designed with consideration for the methodological shortcomings found in past osteopathic research.

Sign in / Sign up

Export Citation Format

Share Document