Universidade Federal de Pelotas

About: Universidade Federal de Pelotas is a education organization based out in Pelotas, Brazil. It is known for research contribution in the topics: Population & Cohort study. The organization has 10999 authors who have published 19061 publications receiving 314862 citations.

Evidence-Based Public Health: Moving Beyond Randomized Trials

Abstract: Randomized controlled trials (RCTs) are essential for evaluating the efficacy of clinical interventions, where the causal chain between the agent and the outcome is relatively short and simple and where results may be safely extrapolated to other settings. However, causal chains in public health interventions are complex, making RCT results subject to effect modification in different populations. Both the internal and external validity of RCT findings can be greatly enhanced by observational studies using adequacy or plausibility designs. For evaluating large-scale interventions, studies with plausibility designs are often the only feasible option and may provide valid evidence of impact. There is an urgent need to develop evaluation standards and protocols for use in circumstances where RCTs are not appropriate.

Physical activity change during adolescence: a systematic review and a pooled analysis

Abstract: Conclusions The decline of PA during adolescence is a consistent finding in the literature. Differences between boys and girls were observed and should be explored in future studies. Interventions that attempt to attenuate the PA decline, even without an increase in PA levels, could be considered as effective.

Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption.

Abstract: Background Mendelian randomization (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions. Methods Here, a new method - the mode-based estimate (MBE) - is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk. Results The MBE presented less bias and lower type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared with the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia. Conclusions The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in sensitivity analyses.