ABSTRACT
BACKGROUND: It has been suggested that probiotics may improve gastrointestinal discomfort. Not all probiotics exhibit the same effects and consequently meta-analyses on probiotics should be confined to well-defined strains or strain combinations. The aim of this study was to evaluate the effectiveness of a probiotic fermented milk (PFM) that includes Bifidobacterium lactis (B. lactis) CNCM I-2494 and lactic acid bacteria on gastrointestinal discomfort in the general adult population. METHODS: Double-blind randomized controlled trials in the general adult population comparing PFM with a control dairy product for at least 4 weeks were searched from multiple literature databases (up to February 2015). Meta-analyses using random-effects models, with individual participant data were undertaken to calculate an odds ratio (OR) or standard mean difference (SMD), with a 95% confidence interval (CI). RESULTS: The search strategy identified 12,439 documents. Overall, three trials with a total of 598 adults (female = 96.5%) met the inclusion criteria. Consumption of the PFM product was associated with a significant improvement in overall gastrointestinal discomfort compared with the control product (OR = 1.48; 95% CI 1.07-2.05), with a number needed to treat (NNT) of 10.24 (95% CI 5.64-55.93). PFM was also superior to the control in reducing digestive symptoms, as measured using a composite score (SMD = -0.21; 95% CI -0.37 to -0.05). Sensitivity analyses produced similar results, and the heterogeneity between studies was minimal. CONCLUSIONS: This meta-analysis shows that the consumption of PFM with B. lactis CNCM I-2494 and lactic acid bacteria is associated with a modest but consistent and significant improvement of outcomes related to gastrointestinal discomfort in healthy adults.
ABSTRACT
Animal models are used to predict the effect of an intervention in humans. An example is the prediction of the efficacy of a vaccine when it is considered unethical or infeasible to challenge humans with the target disease to assess the effect of the vaccine on the disease in humans directly. In such cases, data from animal studies are used to develop models relating antibody level to protection probability in the animal, and then data from a study or studies in human subjects vaccinated with the proposed vaccine regimen are used in combination with the relevant animal models to predict protection in humans, and hence estimate vaccine efficacy. We explain the statistical techniques required to provide an estimate of vaccine efficacy and its precision. We present simulated examples showing that precise estimation of the relationship between antibody levels and protection in animals, at levels likely to be induced in humans by the vaccine regimen, is key to precise estimation of the vaccine efficacy. Because the confidence interval for the estimate of vaccine efficacy cannot be expressed in analytical form, but must be estimated from resampling, or bootstrapping, it is not possible to design studies with required power analytically. Therefore we propose that a simulation-based design of experiments approach using preliminary data is used to maximise the power of further studies and thus minimise the human and animal experimentation required.
