The reporting of methods for reducing and detecting bias: an example from the WHO Misoprostol Third Stage of Labour equivalence randomised controlled trial

BACKGROUND: The aim of this article is to explore ways in which selection bias and ascertainment bias can be reduced and investigated in trials, by using the example of a drug trial carried out in both developed and developing countries in hospital delivery wards. METHODS: We describe an innovative and practical design for the boxes for packing the drugs as a way of increasing the security of allocation concealment and blinding. We also assess ascertainment bias using sensitivity analyses, as some unblinding could have occurred due to a potential side effect of one of the drugs. RESULTS: The sensitivity analyses indicated that the conclusions about the relative effects of the treatments could be maintained even in the unlikely worst-case scenarios. CONCLUSIONS: Detailed description of the procedures protecting against common biases and of the assessment of ascertainment bias in this trial should allow readers to confidently appraise and interpret the results obtained. In addition, our experiences will assist others in planning trials in the future

Ascertainment bias in estimates of average heterozygosity

Journal ArticlePopulation geneticists work with a nonrandom sample of the human genome. Conventional practice ensures that unusually variable loci are most likely to be discovered and thus included in the sample of loci. Consequently, estimates of average heterozygosity are biased upward. In what follows we describe a model of this bias. When the mutation rate varies among loci, bias is increased. This effect is only moderate, however, so that a model of invariant mutation rates provides a reasonable approximation. Bias is pronounced when estimated heterozygosity is < approximately 35% Consequently, it probably affects estimates from classical polymorphisms as well as from restriction-site polymorphisms. Estimates from short-tandem-repeat polymorphisms have negligible bias, because of their high heterozygosity. Bias should vary not only among categories of polymorphism but also among populations. It should be largest in European populations, since these are the populations in which most polymorphisms were discovered. As this argument predicts, European estimates exceed those of Africa and Asia at systems with large bias. The magnitude of this European excess is consistent with the version of our model in which mutation rates vary across loci

Including autapomorphies is important for paleontological tip-dating with clocklike data, but not with non-clock data

Tip-dating, where fossils are included as dated terminal taxa in Bayesian dating inference, is an increasingly popular method. Data for these studies often come from morphological character matrices originally developed for non-dated, and usually parsimony, analyses. In parsimony, only shared derived characters (synapomorphies) provide grouping information, so many character matrices have an ascertainment bias: they omit autapomorphies (unique derived character states), which are considered uninformative. There has been no study of the effect of this ascertainment bias in tip-dating, but autapomorphies can be informative in model-based inference. We expected that excluding autapomorphies would shorten the morphological branchlengths of terminal branches, and thus bias downwards the time branchlengths inferred in tip-dating. We tested for this effect using a matrix for Carboniferous-Permian eureptiles where all autapomorphies had been deliberately coded. Surprisingly, date estimates are virtually unchanged when autapomorphies are excluded, although we find large changes in morphological rate estimates and small effects on topological and dating confidence. We hypothesized that the puzzling lack of effect on dating was caused by the non-clock nature of the eureptile data. We confirm this explanation by simulating strict clock and non-clock datasets, showing that autapomorphy exclusion biases dating only for the clocklike case. A theoretical solution to ascertainment bias is computing the ascertainment bias correction (Mkparsinf), but we explore this correction in detail, and show that it is computationally impractical for typical datasets with many character states and taxa. Therefore we recommend that palaeontologists collect autapomorphies whenever possible when assembling character matrices.Discovery Early Career Researcher Award (DECRA): DE150101773. National Institute for Mathematical and Biological Synthesis (NIMBioS). Institute sponsored by the National Science Foundation. US Department of Homeland Security. US Department of Agriculture through NSF: EFJ0832858, DBI-1300426. The University of Tennessee, Knoxville. NESCent. The University of Utah

Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers

Population genetic studies provide insights into the evolutionary processes that influence the distribution of sequence variants within and among wild populations. FST is among the most widely used measures for genetic differentiation and plays a central role in ecological and evolutionary genetic studies. It is commonly thought that large sample sizes are required in order to precisely infer FST and that small sample sizes lead to overestimation of genetic differentiation. Until recently, studies in ecological model organisms incorporated a limited number of genetic markers, but since the emergence of next generation sequencing, the panel size of genetic markers available even in non-reference organisms has rapidly increased. In this study we examine whether a large number of genetic markers can substitute for small sample sizes when estimating FST. We tested the behavior of three different estimators that infer FST and that are commonly used in population genetic studies. By simulating populations, we assessed the effects of sample size and the number of markers on the various estimates of genetic differentiation. Furthermore, we tested the effect of ascertainment bias on these estimates. We show that the population sample size can be significantly reduced (as small as n = 4–6) when using an appropriate estimator and a large number of bi-allelic genetic markers (k.1,000). Therefore, conservation genetic studies can now obtain almost the same statistical power as studies performed on model organisms using markers developed with next-generation sequencing

Familial Ménière's disease: clinical and genetic aspects

ABSTRACT Background and purpose:Mre's disease is not uncommon, with an incidence in Caucasians of about one in 2000. The incidence peaks in the fifth decade. Cases are usually isolated or sporadic, but in perhaps five per cent other family members are affected. We report here the clinical and genetic characteristics of a comprehensive set of familial Mre's disease cases from the UK.Methods:Forty-six affected families were studied. All cases were diagnosed using the American Academy of Otolaryngolog 8211;Head and Neck Surgery committee on hearing and equilibrium 1995, or more stringent, criteria.Outcomes and results:Autosomal dominant inheritance with reduced penetrance was the most likely mode of inheritance overall. Apparent genetic anticipation was observed, but may also be a result of ascertainment bias given the collection strategy. There was also a slight tendency for cases to result from maternal transmission within the families in this set. The family pedigrees are presented, and the authors have also set up a website at which all the pedigrees may be viewed in greater detai

Correcting for ascertainment bias in the inference of population structure

Background: The ascertainment process of molecular markers amounts to disregard loci carrying alleles with low frequencies. This can result in strong biases in inferences under population genetics models if not properly taken into account by the inference algorithm. Attempting to model this censoring process in view of making inference of population structure (i.e.identifying clusters of individuals) brings up challenging numerical difficulties. Method: These difficulties are related to the presence of intractable normalizing constants in Metropolis-Hastings acceptance ratios. This can be solved via an Markov chain Monte Carlo (MCMC) algorithm known as single variable exchange algorithm (SVEA). Result: We show how this general solution can be implemented for a class of clustering models of broad interest in population genetics that includes the models underlying the computer programs STRUCTURE, GENELAND and GESTE. We also implement the method proposed for a simple example and show that it allows us to reduce the bias substantially. Availability: Further details and a computer program implementing the method are available from http://folk.uio.no/gillesg/AscB/ Contact: [email protected]

Multiple-line inference of selection on quantitative traits

Trait differences between species may be attributable to natural selection. However, quantifying the strength of evidence for selection acting on a particular trait is a difficult task. Here we develop a population-genetic test for selection acting on a quantitative trait which is based on multiple-line crosses. We show that using multiple lines increases both the power and the scope of selection inference. First, a test based on three or more lines detects selection with strongly increased statistical significance, and we show explicitly how the sensitivity of the test depends on the number of lines. Second, a multiple-line test allows to distinguish different lineage-specific selection scenarios. Our analytical results are complemented by extensive numerical simulations. We then apply the multiple-line test to QTL data on floral character traits in plant species of the Mimulus genus and on photoperiodic traits in different maize strains, where we find a signatures of lineage-specific selection not seen in a two-line test.Comment: 21 pages, 11 figures; to appear in Genetic