Tuning multiple imputation by predictive mean matching and local residual draws

Background Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local residual draws (LRD) instead borrows the donor's residual. Both methods relax some assumptions of parametric imputation, promising greater robustness when the imputation model is misspecified. Methods We review development of PMM and LRD and outline the various forms available, and aim to clarify some choices about how and when they should be used. We compare performance to fully parametric imputation in simulation studies, first when the imputation model is correctly specified and then when it is misspecified. Results In using PMM or LRD we strongly caution against using a single donor, the default value in some implementations, and instead advocate sampling from a pool of around 10 donors. We also clarify which matching metric is best. Among the current MI software there are several poor implementations. Conclusions PMM and LRD may have a role for imputing covariates (i) which are not strongly associated with outcome, and (ii) when the imputation model is thought to be slightly but not grossly misspecified

Non-Perturbative Renormalization Group for Simple Fluids

We present a new non perturbative renormalization group for classical simple fluids. The theory is built in the Grand Canonical ensemble and in the framework of two equivalent scalar field theories as well. The exact mapping between the three renormalization flows is established rigorously. In the Grand Canonical ensemble the theory may be seen as an extension of the Hierarchical Reference Theory (L. Reatto and A. Parola, \textit{Adv. Phys.}, \textbf{44}, 211 (1995)) but however does not suffer from its shortcomings at subcritical temperatures. In the framework of a new canonical field theory of liquid state developed in that aim our construction identifies with the effective average action approach developed recently (J. Berges, N. Tetradis, and C. Wetterich, \textit{Phys. Rep.}, \textbf{363} (2002))

Choosing sensitivity analyses for randomised trials: principles

Background Sensitivity analyses are an important tool for understanding the extent to which the results of randomised trials depend upon the assumptions of the analysis. There is currently no guidance governing the choice of sensitivity analyses. Discussion We provide a principled approach to choosing sensitivity analyses through the consideration of the following questions: 1) Does the proposed sensitivity analysis address the same question as the primary analysis? 2) Is it possible for the proposed sensitivity analysis to return a different result to the primary analysis? 3) If the results do differ, is there any uncertainty as to which will be believed? Answering all of these questions in the affirmative will help researchers to identify relevant sensitivity analyses. Treating analyses as sensitivity analyses when one or more of the answers are negative can be misleading and confuse the interpretation of studies. The value of these questions is illustrated with several examples. Summary By removing unreasonable analyses that might have been performed, these questions will lead to relevant sensitivity analyses, which help to assess the robustness of trial results

What impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry

Medical Research Council Clinical Trails Unit at UCL [Studentship to MS; MC EX G0800814 to JRC,TPM]

Report of a Joint Cancer Research UK/Medical Research Council workshop on cancer cachexia research at the Royal College of Physicians, Tuesday, 2 December 2003

A joint workshop held by Cancer Research UK and the Medical Research Council aimed to stimulate interest in further research into the area of cancer cachexia. The workshop was divided into four sessions: an overview of cancer cachexia, potential mechanisms involved and methodologies that might be used to understand cachexia, and also the experience of cachexia from other disease areas. The workshop identified a need to develop a multimodal therapeutic approach to cancer cachexia and a need to undertake more multidisciplinary research

Spina bifida-predisposing heterozygous mutations in Planar Cell Polarity genes and Zic2 reduce bone mass in young mice

Fractures are a common comorbidity in children with the neural tube defect (NTD) spina bifida. Mutations in the Wnt/planar cell polarity (PCP) pathway contribute to NTDs in humans and mice, but whether this pathway independently determines bone mass is poorly understood. Here, we first confirmed that core Wnt/PCP components are expressed in osteoblasts and osteoclasts in vitro. In vivo, we performed detailed µCT comparisons of bone structure in tibiae from young male mice heterozygous for NTD-associated mutations versus WT littermates. PCP signalling disruption caused by Vangl2 (Vangl2Lp/+) or Celsr1 (Celsr1Crsh/+) mutations significantly reduced trabecular bone mass and distal tibial cortical thickness. NTD-associated mutations in non-PCP transcription factors were also investigated. Pax3 mutation (Pax3Sp2H/+) had minimal effects on bone mass. Zic2 mutation (Zic2Ku/+) significantly altered the position of the tibia/fibula junction and diminished cortical bone in the proximal tibia. Beyond these genes, we bioinformatically documented the known extent of shared genetic networks between NTDs and bone properties. 46 genes involved in neural tube closure are annotated with bone-related ontologies. These findings document shared genetic networks between spina bifida risk and bone structure, including PCP components and Zic2. Genetic variants which predispose to spina bifida may therefore independently diminish bone mass

Frequency of ubiquitin and FUS-positive, TDP-43-negative frontotemporal lobar degeneration

Frontotemporal lobar degeneration (FTLD) is a clinically, genetically and pathologically heterogeneous disorder. Within FTLD with ubiquitin-positive inclusions (FTLD-U), a new pathological subtype named FTLD-FUS was recently found with fused in sarcoma (FUS) positive, TDP-43-negative inclusions, and striking atrophy of the caudate nucleus. The aim of this study was to determine the frequency of FTLD-FUS in our pathological FTLD series, and to describe the clinical, neuroimaging and neuropathological features of FTLD-FUS, especially caudate atrophy. Demographic and clinical data collected prospectively from 387 patients with frontotemporal dementia (FTD) yielded 74 brain specimens. Immunostaining was carried out using a panel of antibodies, including AT-8, ubiquitin, p62, FUS, and TDP-43. Cortical and caudate atrophy on MRI (n = 136) was rated as normal, mild-moderate or severe. Of the 37 FTLD-U cases, 33 were reclassified as FTLD-TDP and four (0.11, 95%: 0.00–0.21) as FTLD-FUS, with ubiquitin and FUS-positive, p62 and TDP-43-negative neuronal intranuclear inclusions (NII). All four FTLD-FUS cases had a negative family history, behavioural variant FTD (bvFTD), and three had an age at onset ≤40 years. MRI revealed mild-moderate or severe caudate atrophy in all, with a mean duration from onset till MRI of 63 months (range 16–119 months). In our total clinical FTD cohort, we found 11 patients (0.03; 95% CI: 0.01–0.05) with bvFTD, negative family history, and age at onset ≤40 years. Caudate atrophy was present in 10 out of 136 MRIs, and included all four FUS-cases. The newly identified FTLD-FUS has a frequency of 11% in FTLD-U, and an estimated frequency of three percent in our clinical FTD cohort. The existence of this pathological subtype can be predicted with reasonable certainty by age at onset ≤40 years, negative family history, bvFTD and caudate atrophy on MRI

Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis : A Tutorial

Cost-effectiveness analyses (CEA) of randomised controlled trials are a key source of information for health care decision makers. Missing data are, however, a common issue that can seriously undermine their validity. A major concern is that the chance of data being missing may be directly linked to the unobserved value itself [missing not at random (MNAR)]. For example, patients with poorer health may be less likely to complete quality-of-life questionnaires. However, the extent to which this occurs cannot be ascertained from the data at hand. Guidelines recommend conducting sensitivity analyses to assess the robustness of conclusions to plausible MNAR assumptions, but this is rarely done in practice, possibly because of a lack of practical guidance. This tutorial aims to address this by presenting an accessible framework and practical guidance for conducting sensitivity analysis for MNAR data in trial-based CEA. We review some of the methods for conducting sensitivity analysis, but focus on one particularly accessible approach, where the data are multiply-imputed and then modified to reflect plausible MNAR scenarios. We illustrate the implementation of this approach on a weight-loss trial, providing the software code. We then explore further issues around its use in practice

Considering Intra-individual Genetic Heterogeneity to Understand Biodiversity

In this chapter, I am concerned with the concept of Intra-individual Genetic Hetereogeneity (IGH) and its potential influence on biodiversity estimates. Definitions of biological individuality are often indirectly dependent on genetic sampling -and vice versa. Genetic sampling typically focuses on a particular locus or set of loci, found in the the mitochondrial, chloroplast or nuclear genome. If ecological function or evolutionary individuality can be defined on the level of multiple divergent genomes, as I shall argue is the case in IGH, our current genetic sampling strategies and analytic approaches may miss out on relevant biodiversity. Now that more and more examples of IGH are available, it is becoming possible to investigate the positive and negative effects of IGH on the functioning and evolution of multicellular individuals more systematically. I consider some examples and argue that studying diversity through the lens of IGH facilitates thinking not in terms of units, but in terms of interactions between biological entities. This, in turn, enables a fresh take on the ecological and evolutionary significance of biological diversity