Effectiveness of biomaterial-based combination strategies for spinal cord repair - a systematic review and meta-analysis of preclinical literature.

STUDY DESIGN: Systematic review and meta-analysis of preclinical literature. OBJECTIVES: To assess the effects of biomaterial-based combination (BMC) strategies for the treatment of Spinal Cord Injury (SCI), the effects of individual biomaterials in the context of BMC strategies, and the factors influencing their efficacy. To assess the effects of different preclinical testing paradigms in BMC strategies. METHODS: We performed a systematic literature search of Embase, Web of Science and PubMed. All controlled preclinical studies describing an in vivo or in vitro model of SCI that tested a biomaterial in combination with at least one other regenerative strategy (cells, drugs, or both) were included. Two review authors conducted the study selection independently, extracted study characteristics independently and assessed study quality using a modified CAMARADES checklist. Effect size measures were combined using random-effects models and heterogeneity was explored using meta-regression with tau2, I2 and R2 statistics. We tested for small-study effects using funnel plot-based methods. RESULTS: 134 publications were included, testing over 100 different BMC strategies. Overall, treatment with BMC therapies improved locomotor recovery by 25.3% (95% CI, 20.3-30.3; n = 102) and in vivo axonal regeneration by 1.6 SD (95% CI 1.2-2 SD; n = 117) in comparison with injury only controls. CONCLUSION: BMC strategies improve locomotor outcomes after experimental SCI. Our comprehensive study highlights gaps in current knowledge and provides a foundation for the design of future experiments.

Comparison of commonly used methods in random effects meta-analysis: application to preclinical data in drug discovery research.

BACKGROUND: Meta-analysis of preclinical data is used to evaluate the consistency of findings and to inform the design and conduct of future studies. Unlike clinical meta-analysis, preclinical data often involve many heterogeneous studies reporting outcomes from a small number of animals. Here, we review the methodological challenges in preclinical meta-analysis in estimating and explaining heterogeneity in treatment effects. METHODS: Assuming aggregate-level data, we focus on two topics: (1) estimation of heterogeneity using commonly used methods in preclinical meta-analysis: method of moments (DerSimonian and Laird; DL), maximum likelihood (restricted maximum likelihood; REML) and Bayesian approach; (2) comparison of univariate versus multivariable meta-regression for adjusting estimated treatment effects for heterogeneity. Using data from a systematic review on the efficacy of interleukin-1 receptor antagonist in animals with stroke, we compare these methods, and explore the impact of multiple covariates on the treatment effects. RESULTS: We observed that the three methods for estimating heterogeneity yielded similar estimates for the overall effect, but different estimates for between-study variability. The proportion of heterogeneity explained by a covariate is estimated larger using REML and the Bayesian method as compared with DL. Multivariable meta-regression explains more heterogeneity than univariate meta-regression. CONCLUSIONS: Our findings highlight the importance of careful selection of the estimation method and the use of multivariable meta-regression to explain heterogeneity. There was no difference between REML and the Bayesian method and both methods are recommended over DL. Multiple meta-regression is worthwhile to explain heterogeneity by more than one variable, reducing more variability than any univariate models and increasing the explained proportion of heterogeneity.

Design of Meta-Analysis Studies.

Any given research claim can be made with a degree of confidence that a phenomenon is present, with an estimate of the precision of the observed effects and a prediction of the extent to which the findings might hold true under different experimental or real-world conditions. In some situations, the certainty and precision obtained from a single study are sufficient reliably to inform future research decisions. However, in other situations greater certainty is required. This might be the case where a substantial research investment is planned, a pivotal claim is to be made or the launch of a clinical trial programme is being considered. Under these circumstances, some form of summary of findings across studies may be helpful.Summary estimates can describe findings from exploratory (observational) or hypothesis testing experiments, but importantly, the creation of such summaries is, in itself, observational rather than experimental research. The process is therefore particularly at risk from selective identification of literature to be included, and this can be addressed using systematic search strategies and pre-specified criteria for inclusion and exclusion against which possible contributing data will be assessed. This characterises a systematic review (in contrast to nonsystematic or narrative reviews). In meta-analysis, there is an attempt to provide a quantitative summary of such research findings.

Animal models of chemotherapy-induced peripheral neuropathy: A machine-assisted systematic review and meta-analysis.

We report a systematic review and meta-analysis of research using animal models of chemotherapy-induced peripheral neuropathy (CIPN). We systematically searched 5 online databases in September 2012 and updated the search in November 2015 using machine learning and text mining to reduce the screening for inclusion workload and improve accuracy. For each comparison, we calculated a standardised mean difference (SMD) effect size, and then combined effects in a random-effects meta-analysis. We assessed the impact of study design factors and reporting of measures to reduce risks of bias. We present power analyses for the most frequently reported behavioural tests; 337 publications were included. Most studies (84%) used male animals only. The most frequently reported outcome measure was evoked limb withdrawal in response to mechanical monofilaments. There was modest reporting of measures to reduce risks of bias. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. In this comprehensive summary of the use of animal models of CIPN, we have identified areas in which the value of preclinical CIPN studies might be increased. Using both sexes of animals in the modelling of CIPN, ensuring that outcome measures align with those most relevant in the clinic, and the animal's pain contextualised ethology will likely improve external validity. Measures to reduce risk of bias should be employed to increase the internal validity of studies. Different outcome measures have different statistical power, and this can refine our approaches in the modelling of CIPN.