Assessing the effect of an influenza vaccine in an encouragement design

Many randomized experiments suffer from noncompliance. Some of these experiments, so-called encouragement designs, can be expected to have especially large amounts of noncompliance, because encouragement to take the treatment rather than the treatment itself is randomly assigned to individuals. We present an extended framework for the analysis of data from such experiments with a binary treatment, binary encouragement, and background covariates. There are two key features of this framework: we use an instrumental variables approach to link intention-to-treat effects to treatment effects and we adopt a Bayesian approach for inference and sensitivity analysis. This framework is illustrated in a medical example concerning the effects of inoculation for influenza. In this example, the analyses suggest that positive estimates of the intention-to-treat effect need not be due to the treatment itself, but rather to the encouragement to take the treatment: the intention-to-treat effect for the subpopulation who would be inoculated whether or not encouraged is estimated to be approximately as large as the intention-to-treat effect for the subpopulation whose inoculation status would agree with their (randomized) encouragement status whether or not encouraged. Thus, our methods suggest that global intention-to-treat estimates, although often regarded as conservative, can be too coarse and even misleading when taken as summarizing the evidence in the data for the effects of treatments.     

Efficient nonparametric estimation of causal effects in randomized trials with noncompliance

Causal approaches based on the potential outcome framework providea useful tool for addressing noncompliance problems in randomizedtrials. We propose a new estimator of causal treatment effectsin randomized clinical trials with noncompliance. We use theempirical likelihood approach to construct a profile randomsieve likelihood and take into account the mixture structurein outcome distributions, so that our estimator is robust toparametric distribution assumptions and provides substantialfinite-sample efficiency gains over the standard instrumentalvariable estimator. Our estimator is asymptotically equivalentto the standard instrumental variable estimator, and it canbe applied to outcome variables with a continuous, ordinal orbinary scale. We apply our method to data from a randomizedtrial of an intervention to improve the treatment of depressionamong depressed elderly patients in primary care practices.     

Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates

Mendelian randomization methods, which use genetic variants as instrumental variables for exposures of interest to overcome problems of confounding and reverse causality, are becoming widespread for assessing causal relationships in epidemiological studies. The main purpose of this paper is to demonstrate how results can be biased if researchers select genetic variants on the basis of their association with the exposure in their own dataset, as often happens in candidate gene analyses. This can lead to estimates that indicate apparent “causal” relationships, despite there being no true effect of the exposure. In addition, we discuss the potential bias in estimates of magnitudes of effect from Mendelian randomization analyses when the measured exposure is a poor proxy for the true underlying exposure. We illustrate these points with specific reference to tobacco research.     

The effectiveness and efficacy of Rhodiola rosea L.: A systematic review of randomized clinical trials

Objective

To critically assess the current evidence from randomized clinical trials (RCTs) for or against the effectiveness or efficacy of Rhodiola rosea.

Data sources

Systematic literature searches were performed in six electronic databases: AMED (1985-July 2009), CINAHL (1982-July 2009), The Cochrane Library (search in July 2009), EMBASE (1974-July 2009), MEDLINE (1950-July 2009) and Web of Science (searched in July 2009). No language restrictions were imposed. Reference lists of all retrieved articles were searched, and experts and manufacturers were contacted for unpublished RCT.

Review methods

RCTs testing the efficacy or effectiveness of mono-preparations of R. rosea as sole treatment administered orally against a control intervention in any human individual suffering from any condition or healthy human volunteers were included. Studies were selected, data extracted, and quality assessed by two independent reviewers.

Results

Eleven RCTs met the inclusion criteria; all were placebo-controlled. Six trials investigated the effects of R. rosea on physical performance, four on mental performance, and two in patients diagnosed with mental health condition. The methodological quality of most trials was moderate or good. Only few mild adverse events were reported.

Conclusion

R. rosea may have beneficial effects on physical performance, mental performance, and certain mental health conditions. There is, however, a lack of independent replications of the single different studies. Five of the 10 RCTs reached more than three points on the Jadad score (i.e., good quality). More research seems warranted.     

Inadequacy of blocking in cultivar yield trials

Summary Based on the literature, theoretical considerations and a numerical example on triticale, Complete Randomized Blocks design is shown to be inadequate for cultivar yield trial purposes. Assumptions required for validity and convenience are shown not to be verified throughout most of the published experiments as well as in the present numerical example. It has been referred to the difficulty in forecasting homogeneity within blocks together with heterogeneity between blocks. This is difficult to achieve even in wellknown experimental fields, let alone local fields chosen at random, which leads to unacceptably low correlation between plots from block to block in each trial. Heteroscedasticity, as supported by different regression coefficients in Joint Regression Analysis, does not allow for ANOVA, unless the overall variation of soil fertility level is reduced to an amount comparable with that expected for the unknown errors. In this instance, the loss of degrees of freedom in the two-way ANOVA is known not to be compensated for by block effect deduction. The need to generalize trial results calls attention to the emphasis that should be given to cultivar performance pattern within the area they are to be released. Thus, we advocate the need for precise point evaluations in order to give accurate estimation of that pattern. Genotype-environment interaction, within situations where ecological diversity does not include stress mechanisms of different natures, can be reasonably described through its genotype-fertility level component, since specific instability, related to climatic features, is supposed to be strongly reduced by the screening process of both cultivar production and introduction. Sensitivity of the regression techniques (even through robust methods) requires a broad range of trial fertility levels and, besides an adequate number of degrees of freedom and detection of eventual remaining specific instabilities, demands an experienced evaluation of particular ecological situations; however, randomization is not required except within trials, which should be designed as completely randomized. To carry on trials beyond one year is not an a priori demand and should only be considered when very abnormal seasonal conditions occur.This research was partially supported by the Calouste Gulbenkian Foundation, Lisboa     

Elicitation of a beta prior for Bayesian inference in clinical trials

When making Bayesian inferences we need to elicit an expert's opinion to set up the prior distribution. For applications in clinical trials, we study this problem with binary variables. A critical and often ignored issue in the process of eliciting priors in clinical trials is that medical investigators can seldom specify the prior quantities with precision. In this paper, we discuss several methods of eliciting beta priors from clinical information, and we use simulations to conduct sensitivity analyses of the effect of imprecise assessment of the prior information. These results provide useful guidance for choosing methods of eliciting the prior information in practice.