Conditional Estimation in Two-stage Adaptive Designs

We consider conditional estimation in two-stage sample size adjustable designs and the following bias. More specifically, we consider a design which permits raising the sample size when interim results look rather promising, and, which keeps the originally planned sample size when results look very promising. The estimation procedures reported comprise the unconditional maximum likelihood, the conditionally unbiased Rao-Blackwell estimator, the conditional median unbiased estimator, and the conditional maximum likelihood with and without bias correction. We compare these estimators based on analytical results and by a simulation study. We show in a real clinical trial setting how they can be applied

A comparative review of estimates of the proportion unchanged genes and the false discovery rate

BACKGROUND: In the analysis of microarray data one generally produces a vector of p-values that for each gene give the likelihood of obtaining equally strong evidence of change by pure chance. The distribution of these p-values is a mixture of two components corresponding to the changed genes and the unchanged ones. The focus of this article is how to estimate the proportion unchanged and the false discovery rate (FDR) and how to make inferences based on these concepts. Six published methods for estimating the proportion unchanged genes are reviewed, two alternatives are presented, and all are tested on both simulated and real data. All estimates but one make do without any parametric assumptions concerning the distributions of the p-values. Furthermore, the estimation and use of the FDR and the closely related q-value is illustrated with examples. Five published estimates of the FDR and one new are presented and tested. Implementations in R code are available. RESULTS: A simulation model based on the distribution of real microarray data plus two real data sets were used to assess the methods. The proposed alternative methods for estimating the proportion unchanged fared very well, and gave evidence of low bias and very low variance. Different methods perform well depending upon whether there are few or many regulated genes. Furthermore, the methods for estimating FDR showed a varying performance, and were sometimes misleading. The new method had a very low error. CONCLUSION: The concept of the q-value or false discovery rate is useful in practical research, despite some theoretical and practical shortcomings. However, it seems possible to challenge the performance of the published methods, and there is likely scope for further developing the estimates of the FDR. The new methods provide the scientist with more options to choose a suitable method for any particular experiment. The article advocates the use of the conjoint information regarding false positive and negative rates as well as the proportion unchanged when identifying changed genes

Statistical methods for ranking differentially expressed genes

In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that give few false positives and few false negatives. Estimation of the false-negative as well as the false-positive rate lies at the heart of the method

Does environmental leadership pay off for Swed-ish industry? - Analyzing the effects of environ-mental investments on efficiency

Swedish environmental policy often emphasizes the importance of “taking the lead”. For example, Sweden has chosen a more ambitious climate policy target than required by the European Union (EU), namely a reduction of Swedish emissions of greenhouse gases by 40 percent by 2020 compared to the 1990 level. Government Bill 2008/09:162 emphasizes Sweden’s role as a good example in making an effort to re-duce climate change by showing that an offensive climate policy can indeed be com-bined with high economic growth. This view of environmental policy is, however, the subject of constant debate. A common argument is that environmental requirements induce private costs by forc-ing firms to make investments that crowd out other more productive investments, which hampers productivity growth and therefore competitiveness. Professor Mi-chael E. Porter of Harvard questioned this argument, and his view has become known as the Porter hypothesis (Porter, 1991). This hypothesis implies that levying stringent environmental regulations on firms enhances their productivity compared to competi-tors not subject to, or subject to lax, environmental regulations. A central message is that the connection between environmental regulation and competitiveness should be scrutinized within a dynamic framework (Porter and van der Linde, 1995). The main objective of this paper is to test the Porter hypothesis by assessing static and dynamic effects of environmental policy on productivity within the Swedish manufac-turing industry, specifically on the component total efficiency. The paper adds mainly to previous literature by using unique data on environmental protection investments, divided into investments in pollution control and pollution prevention, as a proxy for envi-ronmental regulation. The distinction between these types of investments is crucial to the understanding of the outcomes anticipated by the Porter hypothesis. The international literature studying the Porter hypothesis is extensive. A comprehen-sive review reveals that neither theoretical nor empirical literature gives general sup-port for the hypothesis (Brännlund and Lundgren, 2009). We argue that, to some ex-tent, the Porter hypothesis has not yet been given a fair chance in the empirical litera-ture, as dynamic effects are often neglected in empirical tests. Two exceptions are Managi et al. (2005) and Lanoie et al. (2008), who first estimate Total Factor Produc-tivity (TFP) scores that then are used as dependent variables in regression analyses where explanatory lagged environmental stringency measures model dynamic effects. A disadvantage with these studies is, however, that environmental stringency is ap-proximated by the cost of complying with environmental command- and-control regulations, such regulations are not emphasized by the Porter hypothesis. The empirical test of the Porter hypothesis is performed as a two-step procedure, where total efficiency scores are first estimated by adopting a stochastic production frontier function approach. In the second step, the efficiency scores are used as the dependent variable in random effects regression analyses, where the independent vari-ables are, e.g., investment in pollution control and pollution prevention. In order to assess whether these investments have dynamic effects on total efficiency these vari-ables are also lagged. If positive effects are established we cannot reject the claim that environmental leadership will benefit the Swedish industry. The estimations are based on firm level data from five Swedish industries for the period 1999-2004, and carried out for the pooled data as well as for the industries separately.

Serological evaluation of possible exposure to Ljungan virus and related parechovirus in autoimmune (type 1) diabetes in children.

Exposure to Ljungan virus (LV) is implicated in the risk of autoimmune (type 1) diabetes but possible contribution by other parechoviruses is not ruled out. The aim was to compare children diagnosed with type 1 diabetes in 2005-2011 (n = 69) with healthy controls (n = 294), all from the Jämtland County in Sweden, using an exploratory suspension multiplex immunoassay for IgM and IgG against 26 peptides of LV, human parechoviruses (HPeV), Aichi virus and poliovirus in relation to a radiobinding assay (RBA) for antibodies against LV and InfluenzaA/H1N1pdm09. Islet autoantibodies and HLA-DQ genotypes were also determined. 1) All five LV-peptide antibodies correlated to each other (P < 0.001) in the suspension multiplex IgM- and IgG-antibody assay; 2) The LV-VP1_31-60-IgG correlated with insulin autoantibodies alone (P = 0.007) and in combination with HLA-DQ8 overall (P = 0.022) as well as with HLA-DQ 8/8 and 8/X subjects (P = 0.013); 3) RBA detected LV antibodies correlated with young age at diagnosis (P < 0.001) and with insulin autoantibodies (P < 0.001) especially in young HLA-DQ8 subjects (P = 0.004); 4) LV-peptide-VP1_31-60-IgG correlated to RBA LV antibodies (P = 0.009); 5) HPeV3-peptide-IgM and -IgG showed inter-peptide correlations (P < 0.001) but only HPeV3-VP1_1-30-IgG (P < 0.001) and VP1_95-124-IgG (P = 0.009) were related to RBA LV antibodies without relation to insulin autoantibody positivity (P = 0.072 and P = 0.486, respectively). Both exploratory suspension multiplex IgG to LV-peptide VP1_31-60 and RBA detected LV antibodies correlated with insulin autoantibodies and HLA-DQ8 suggesting possible role in type 1 diabetes. It remains to be determined if cross-reactivity or concomitant exposure to LV and HPeV3 contributes to the seroprevalence. J. Med. Virol. © 2015 Wiley Periodicals, Inc

DiMoPEx-project is designed to determine the impacts of environmental exposure on human health

The WHO has ranked environmental hazardous exposures in the living and working environment among the top risk factors for chronic disease mortality. Worldwide, about 40 million people die each year from noncommunicable diseases (NCDs) including cancer, diabetes, and chronic cardiovascular, neurological and lung diseases. The exposure to ambient pollution in the living and working environment is exacerbated by individual susceptibilities and lifestyle-driven factors to produce complex and complicated NCD etiologies. Research addressing the links between environmental exposure and disease prevalence is key for prevention of the pandemic increase in NCD morbidity and mortality. However, the long latency, the chronic course of some diseases and the necessity to address cumulative exposures over very long periods does mean that it is often difficult to identify causal environmental exposures. EU-funded COST Action DiMoPEx is developing new concepts for a better understanding of health- environment (including gene-environment) interactions in the etiology of NCDs. The overarching idea is to teach and train scientists and physicians to learn how to include efficient and valid exposure assessments in their research and in their clinical practice in current and future cooperative projects. DiMoPEx partners have identified some of the emerging research needs, which include the lack of evidence-based exposure data and the need for human-equivalent animal models mirroring human lifespan and low-dose cumulative exposures. Utilizing an interdisciplinary approach incorporating seven working groups, DiMoPEx will focus on aspects of air pollution with particulate matter including dust and fibers and on exposure to low doses of solvents and sensitizing agents. Biomarkers of early exposure and their associated effects as indicators of disease-derived information will be tested and standardized within individual projects. Risks arising from some NCDs, like pneumoconioses, cancers and allergies, are predictable and preventable. Consequently, preventative action could lead to decreasing disease morbidity and mortality for many of the NCDs that are of major public concern. DiMoPEx plans to catalyze and stimulate interaction of scientists with policy-makers in attacking these exposure-related diseases

Long-term outcome of displaced radial neck fractures in adulthood: 16–21 year follow-up of 5 patients treated with radial head excision

Background There have been no reports on the long-term outcome of radial neck Mason type IIIb fractures in adults

Genome-wide association study of germline variants and breast cancer-specific mortality.

BackgroundWe examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry.MethodsMeta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP).ResultsWe did not find any variant associated with breast cancer-specific mortality at P &lt; 5 × 10-8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10-7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10-7, HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster.ConclusionsWe uncovered germline variants on chromosome 7 at BFDP &lt; 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients