OBJECTIVES/SPECIFIC AIMS:Chronic peripheral neuropathy (CPN) is frequent and diffi cult to treat. Smoked cannabis may provide relief, but design and outcomes of available randomized controlled trials (RCT) were too heterogeneous for a traditional meta-analysis. Our Bayesian model converted continuous pain outcomes to dichotomous data, quantifying the number of patients needed to treat (NNT) for a clinically meaningful response. We hypothesized that dose diff erences may partly explain the between study variance and improve eff ect estimates and credible intervals.
METHODS/STUDY POPULATION: We performed a systematic review and Bayesian random eff ects meta-analysis of smoked cannabis for CPN. We used a Gibbs sampling scheme in OpenBugs to generate a Monte Carlo sample from the posterior distribution of the model parameters. RESULTS/ANTICIPATED RESULTS: We pooled four RCTs with 132 patients, but encountered variance in design and outcomes. We estimated the odds ratio for benefi t as 1.8 with a 95% Bayesian credible interval CI 95% [0.9 to 4.5], NNT 4.3, CI 95% [2 to ∞]. Dose diff erences partly explained the observed between study heterogeneity in our Bayesian meta-regression (R^2 0.57); the adjusted NNT is 3.42 with CI 95% (1.64, 1520), estimated for a dose of 96 mg. Th e probability of a positive eff ect is 98%. Th e Bayes Factor is 57, signifying strong evidence.
DISCUSSION/SIGNIFICANCE OF IMPACT: Smoked cannabis is eff ective for CPN, improving pain in one patient for every 3.4 patients treated. Our dose-eff ect meta-regression improved eff ect estimates and shrank the credible interval. Our innovation is the development of a novel Bayesian meta-regression to partly explain the between study variance in heterogeneous RCTs reporting diverse long term pain outcomes.
UNLABELLED: Chronic neuropathic pain, the most frequent condition affecting the peripheral nervous system, remains underdiagnosed and difficult to treat. Inhaled cannabis may alleviate chronic neuropathic pain. Our objective was to synthesize the evidence on the use of inhaled cannabis for chronic neuropathic pain. We performed a systematic review and a meta-analysis of individual patient data. We registered our protocol with PROSPERO CRD42011001182. We searched in Cochrane Central, PubMed, EMBASE, and AMED. We considered all randomized controlled trials investigating chronic painful neuropathy and comparing inhaled cannabis with placebo. We pooled treatment effects following a hierarchical random-effects Bayesian responder model for the population-averaged subject-specific effect. Our evidence synthesis of individual patient data from 178 participants with 405 observed responses in 5 randomized controlled trials following patients for days to weeks provides evidence that inhaled cannabis results in short-term reductions in chronic neuropathic pain for 1 in every 5 to 6 patients treated (number needed to treat = 5.6 with a Bayesian 95% credible interval ranging between 3.4 and 14). Our inferences were insensitive to model assumptions, priors, and parameter choices. We caution that the small number of studies and participants, the short follow-up, shortcomings in allocation concealment, and considerable attrition limit the conclusions that can be drawn from the review. The Bayes factor is 332, corresponding to a posterior probability of effect of 99.7%.
PERSPECTIVE: This novel Bayesian meta-analysis of individual patient data from 5 randomized trials suggests that inhaled cannabis may provide short-term relief for 1 in 5 to 6 patients with neuropathic pain. Pragmatic trials are needed to evaluate the long-term benefits and risks of this treatment.
Chronic peripheral neuropathy (CPN) is frequent and diffi cult to treat. Smoked cannabis may provide relief, but design and outcomes of available randomized controlled trials (RCT) were too heterogeneous for a traditional meta-analysis. Our Bayesian model converted continuous pain outcomes to dichotomous data, quantifying the number of patients needed to treat (NNT) for a clinically meaningful response. We hypothesized that dose diff erences may partly explain the between study variance and improve eff ect estimates and credible intervals.
METHODS/STUDY POPULATION:
We performed a systematic review and Bayesian random eff ects meta-analysis of smoked cannabis for CPN. We used a Gibbs sampling scheme in OpenBugs to generate a Monte Carlo sample from the posterior distribution of the model parameters.
RESULTS/ANTICIPATED RESULTS:
We pooled four RCTs with 132 patients, but encountered variance in design and outcomes. We estimated the odds ratio for benefi t as 1.8 with a 95% Bayesian credible interval CI 95% [0.9 to 4.5], NNT 4.3, CI 95% [2 to ∞]. Dose diff erences partly explained the observed between study heterogeneity in our Bayesian meta-regression (R^2 0.57); the adjusted NNT is 3.42 with CI 95% (1.64, 1520), estimated for a dose of 96 mg. Th e probability of a positive eff ect is 98%. Th e Bayes Factor is 57, signifying strong evidence.
DISCUSSION/SIGNIFICANCE OF IMPACT:
Smoked cannabis is eff ective for CPN, improving pain in one patient for every 3.4 patients treated. Our dose-eff ect meta-regression improved eff ect estimates and shrank the credible interval. Our innovation is the development of a novel Bayesian meta-regression to partly explain the between study variance in heterogeneous RCTs reporting diverse long term pain outcomes.
