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We apply effective field theory methods to compute bino-nucleon scattering, in the case where tree-level interactions are suppressed and the leading contribution is at loop order via heavy flavor squarks or sleptons. We find that leading log corrections to fixed-order calculations can increase the bino mass reach of direct detection experiments by a factor of 2 in some models. These effects are particularly large for the bino-sbottom coannihilation region, where bino dark matter as heavy as 5-10 TeV may be detected by near future experiments. For the case of stop- and selectron-loop mediated scattering, an experiment reaching the neutrino background will probe thermal binos as heavy as 500 and 300 GeV, respectively. We present three key examples that illustrate in detail the framework for determining weak scale coefficients, and for mapping onto a low-energy theory at hadronic scales, through a sequence of effective theories and renormalization group evolution. For the case of a squark degenerate with the bino, we extend the framework to include a squark degree of freedom at low energies using heavy particle effective theory, thus accounting for large logarithms through a "heavy-light current." Benchmark predictions for scattering cross sections are evaluated, including complete leading order matching onto quark and gluon operators, and a systematic treatment of perturbative and hadronic uncertainties.

A coordinated testing policy is an essential tool for responding to emerging epidemics, as was seen with COVID-19. However, it is very difficult to agree on the best policy when there are multiple conflicting objectives. A key objective is minimizing cost, which is why pooled testing (a method that involves pooling samples taken from multiple individuals and analyzing this with a single diagnostic test) has been suggested. In this article, we present results from an extensive and realistic simulation study comparing testing policies based on individually testing subjects with symptoms (a policy resembling the UK strategy at the start of the COVID-19 pandemic), individually testing subjects at random or pools of subjects randomly combined and tested. To compare these testing methods, a dynamic model compromised of a relationship network and an extended SEIR model is used. In contrast to most existing literature, testing capacity is considered as fixed and limited rather than unbounded. This article then explores the impact of the proportion of symptomatic infections on the expected performance of testing policies. Symptomatic testing performs better than pooled testing unless a low proportion of infections are symptomatic. Additionally, we include the novel feature for testing of non-compliance and perform a sensitivity analysis for different compliance assumptions. Our results suggest for the pooled testing scheme to be superior to testing symptomatic people individually, only a small proportion of the population ( > 10 % $$ >10\% $$ ) needs to not comply with the testing procedure., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: There is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases. An efficient way to test as many individuals as possible with a limited testing capacity is to consider pooling multiple samples to be analysed with a single test (known as pooled testing)., Methods: Two different pooled testing schemes for the asymptomatic testing are evaluated using an individual-based model representing the transmission of SARS-CoV-2 in a 'typical' English hospital. We adapt the modelling to reflect two scenarios: a) a retrospective look at earlier SARS-CoV-2 variants under lockdown or social restrictions, and b) transitioning back to 'normal life' without lockdown and with the omicron variant. The two pooled testing schemes analysed differ in the population that is eligible for testing. In the 'ward' testing scheme only healthcare workers who work on a single ward are eligible and in the 'full' testing scheme all healthcare workers are eligible including those that move across wards. Both pooled schemes are compared against the baseline scheme which tests only symptomatic healthcare workers., Results: Including a pooled asymptomatic testing scheme is found to have a modest (albeit statistically significant) effect, reducing the total number of nosocomial healthcare worker infections by about 2[Formula: see text] in both the lockdown and non-lockdown setting. However, this reduction must be balanced with the increase in cost and healthcare worker isolations. Both ward and full testing reduce HCW infections similarly but the cost for ward testing is much less. We also consider the use of lateral flow devices (LFDs) for follow-up testing. Considering LFDs reduces cost and time but LFDs have a different error profile to PCR tests., Conclusions: Whether a PCR-only or PCR and LFD ward testing scheme is chosen depends on the metrics of most interest to policy makers, the virus prevalence and whether there is a lockdown., (© 2023. The Author(s).)

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all p -values are available to be tested at a single time point. To address these challenges, a new field of methodology has developed over the past 15 years showing how to control error rates for online multiple hypothesis testing. In this framework, hypotheses arrive in a stream, and at each time point the analyst decides whether to reject the current hypothesis based both on the evidence against it, and on the previous rejection decisions. In this paper, we present a comprehensive exposition of the literature on online error rate control, with a review of key theory as well as a focus on applied examples. We also provide simulation results comparing different online testing algorithms and an up-to-date overview of the many methodological extensions that have been proposed.

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

A description is given of a sequence of events which would have led to the appearance of the organic compounds and living cells present on Earth, one of which is human cells. The evolutionary events involved are proposed as having taken place in phosphate ion-dominated aqueous pools formed in regions associated with volcanoes. The mechanism involved the unique molecular structure variations and chemical properties of polyphosphoric acid and compounds of this acid producing urea as the first organic compound formed on Earth and derivatives of urea giving rise to DNA and RNA. The occurrence of the process in present times is considered possible., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature.)

Response-adaptive randomization allows the probabilities of allocating patients to treatments in a clinical trial to change based on the previously observed response data, in order to achieve different experimental goals. One concern over the use of such designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. To address this, Robertson and Wason (Biometrics, 2019) proposed methodology that guarantees familywise error rate control for a large class of response-adaptive designs by re-weighting the usual z -test statistic. In this article, we propose an improvement of their method that is conceptually simpler, in the context where patients are allocated to the experimental treatment arms in a trial in blocks (i.e. groups) using response-adaptive randomization. We show the modified method guarantees that there will never be negative weights for the contribution of each block of data to the adjusted test statistics, and can also provide a substantial power advantage in practice.

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Motivation: While classical approaches for controlling the false discovery rate (FDR) of RNA sequencing (RNAseq) experiments have been well described, modern research workflows and growing databases enable a new paradigm of controlling the FDR globally across RNAseq experiments in the past, present and future. The simplest analysis strategy that analyses each RNAseq experiment separately and applies an FDR correction method can lead to inflation of the overall FDR. We propose applying recently developed methodology for online multiple hypothesis testing to control the global FDR in a principled way across multiple RNAseq experiments., Results: We show that repeated application of classical repeated offline approaches has variable control of global FDR of RNAseq experiments over time. We demonstrate that the online FDR algorithms are a principled way to control FDR. Furthermore, in certain simulation scenarios, we observe empirically that online approaches have comparable power to repeated offline approaches., Availability and Implementation: The onlineFDR package is freely available at http://www.bioconductor.org/packages/onlineFDR. Additional code used for the simulation studies can be found at https://github.com/latlio/onlinefdr_rnaseq_simulation., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the ability to select treatments, it is unclear how covariate adjustment affects various aspects of the study. Consider the design framework that relies on pre-specified treatment selection rule(s) and a combination test approach for hypothesis testing. It is our primary goal to evaluate the impact of covariate adjustment on adaptive multi-arm designs with treatment selection. Our secondary goal is to show how the Uniformly Minimum Variance Conditionally Unbiased Estimator can be extended to account for covariate adjustment analytically. We find that adjustment with different sets of covariates can lead to different treatment selection outcomes and hence probabilities of rejecting hypotheses. Nevertheless, we do not see any negative impact on the control of the familywise error rate when covariates are included in the analysis model. When adjusting for covariates that are moderately or highly correlated with the outcome, we see various benefits to the analysis of the design. Conversely, there is negligible impact when including covariates that are uncorrelated with the outcome. Overall, pre-specification of covariate adjustment is recommended for the analysis of adaptive multi-arm design with treatment selection. Having the statistical analysis plan in place prior to the interim and final analyses is crucial, especially when a non-collapsible measure of treatment effect is considered in the trial.

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward. Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the impact on the expected performance of multi-armed bandit strategies varies according to the way these strategies balance the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses (which being perceived as the arm with less observed information is deemed more appealing by the algorithm than it would otherwise be). In contrast, algorithms that are geared towards exploitation would rapidly assign a high value to samples from the arms with a current high mean irrespective of the level observations per arm. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach., Competing Interests: The authors have declared that no competing interests exist.

Background: Ofatumumab, the first fully human anti-CD20 monoclonal antibody, is approved in several countries for relapsing multiple sclerosis (RMS)., Objective: To demonstrate the bioequivalence of ofatumumab administered by an autoinjector versus a pre-filled syringe (PFS) and to explore the effect of ofatumumab on B-cell depletion., Methods: APLIOS (NCT03560739) is a 12-week, open-label, parallel-group, phase-2 study in patients with RMS receiving subcutaneous ofatumumab 20 mg every 4 weeks (q4w) (from Week 4, after initial doses on Days 1, 7, and 14). Patients were randomized 10:10:1:1 to autoinjector or PFS in the abdomen, or autoinjector or PFS in the thigh, respectively. Bioequivalence was determined by area under the curve (AUC τ ) and maximum plasma concentration ( C max ) for Weeks 8-12. B-cell depletion and safety/tolerability were assessed., Results: A total of 256 patients contributed to the bioequivalence analyses (autoinjector-abdomen, n = 128; PFS-abdomen, n = 128). Abdominal ofatumumab pharmacokinetic exposure was bioequivalent for autoinjector and PFS (geometric mean AUC τ , 487.7 vs 474.1 h × µg/mL (ratio 1.03); C max , 1.409 vs 1.409 µg/mL (ratio 1.00)). B-cell counts (median cells/µL) depleted rapidly in all groups from 214.0 (baseline) to 2.0 (Day 14). Ofatumumab was well tolerated., Conclusion: Ofatumumab 20 mg q4w self-administered subcutaneously via autoinjector is bioequivalent to PFS administration and provides rapid B-cell depletion.

Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Since the sample size is usually determined by an argument based on the power of the trial, an interim analysis raises the question of how the final sample size should be determined conditional on the accrued information. To this end, we first review and compare common approaches to estimating conditional power, which is often used in heuristic sample size recalculation rules. We then discuss the connection of heuristic sample size recalculation and optimal two-stage designs, demonstrating that the latter is the superior approach in a fully preplanned setting. Hence, unplanned design adaptations should only be conducted as reaction to trial-external new evidence, operational needs to violate the originally chosen design, or post hoc changes in the optimality criterion but not as a reaction to trial-internal data. We are able to show that commonly discussed sample size recalculation rules lead to paradoxical adaptations where an initially planned optimal design is not invariant under the adaptation rule even if the planning assumptions do not change. Finally, we propose two alternative ways of reacting to newly emerging trial-external evidence in ways that are consistent with the originally planned design to avoid such inconsistencies., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: This study establishes measurements to evaluate pathologic compensation in rotator cuff tear arthropathy and resultant considerations for reverse shoulder arthroplasty., Methods: Radiographs of patients with intact rotator cuffs were measured establishing interobserver and intraobserver reliability. Reverse shoulder arthroplasty cases performed by a single surgeon were then retrospectively reviewed. One year of follow-up radiographs were required for inclusion. Preoperative radiographs were analyzed for relative humeral head elevation ratio and humeral abduction relative to the glenoid face, termed the glenoid-intramedullary humeral angle. Statistical analyses assessed associations for radiographic measurements with presence and severity of scapular notching based on the Nerot-Sirveaux classification system., Results: A total of 221 patients met inclusion criteria. At the 1-year follow-up, 61 (27.6%) shoulders had radiographic notching. There was a moderately strong (r = -0.56) negative correlation between glenoid-intramedullary humeral angle and humeral head elevation ratio. Patients with humeral head elevation ratio ≥ 20% were significantly ( P  = .024) and 9.2 times more likely to have notching of any grade. Patients with glenoid-intramedullary humeral angle ≤ 5 degrees were significant ( P  < .0001) and 6.7 times more likely to have notching of any grade and significantly ( P  = .00018) and 145 times more likely to have high-grade (3 and 4) notching., Conclusions: Preoperative humeral head elevation and compensatory scapular rotation with relative adduction of the humerus have significant associations with high-grade notching. These radiographic findings have potential to help surgeons in preoperative decision-making regarding implant choice and patient education., (© 2021 The Authors.)

Sample size derivation is a crucial element of planning any confirmatory trial. The required sample size is typically derived based on constraints on the maximal acceptable Type I error rate and minimal desired power. Power depends on the unknown true effect and tends to be calculated either for the smallest relevant effect or a likely point alternative. The former might be problematic if the minimal relevant effect is close to the null, thus requiring an excessively large sample size, while the latter is dubious since it does not account for the a priori uncertainty about the likely alternative effect. A Bayesian perspective on sample size derivation for a frequentist trial can reconcile arguments about the relative a priori plausibility of alternative effects with ideas based on the relevance of effect sizes. Many suggestions as to how such "hybrid" approaches could be implemented in practice have been put forward. However, key quantities are often defined in subtly different ways in the literature. Starting from the traditional entirely frequentist approach to sample size derivation, we derive consistent definitions for the most commonly used hybrid quantities and highlight connections, before discussing and demonstrating their use in sample size derivation for clinical trials.

Multi-arm trials are an efficient way of simultaneously testing several experimental treatments against a shared control group. As well as reducing the sample size required compared to running each trial separately, they have important administrative and logistical advantages. There has been debate over whether multi-arm trials should correct for the fact that multiple null hypotheses are tested within the same experiment. Previous opinions have ranged from no correction is required, to a stringent correction (controlling the probability of making at least one type I error) being needed, with regulators arguing the latter for confirmatory settings. In this article, we propose that controlling the false-discovery rate (FDR) is a suitable compromise, with an appealing interpretation in multi-arm clinical trials. We investigate the properties of the different correction methods in terms of the positive and negative predictive value (respectively how confident we are that a recommended treatment is effective and that a non-recommended treatment is ineffective). The number of arms and proportion of treatments that are truly effective is varied. Controlling the FDR provides good properties. It retains the high positive predictive value of FWER correction in situations where a low proportion of treatments is effective. It also has a good negative predictive value in situations where a high proportion of treatments is effective. In a multi-arm trial testing distinct treatment arms, we recommend that sponsors and trialists consider use of the FDR., (© 2020 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

The work presented proposes origins of the extensive range of observations concerning changes in bone joints associated with rheumatoid arthritis, osteoarthritis and arthritis urica. These changes are shown to originate with alterations of cell reactions involving four basic cell biochemicals. The proposals allow explanations of, for example, the link between rheumatoid arthritis and anaemia. It is also shown that performic acid is a presently unrecognised cause of degradation of tissue in bone joints. Suggestions are made as to means of control of these changes., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

When simultaneously testing multiple hypotheses, the usual approach in the context of confirmatory clinical trials is to control the familywise error rate (FWER), which bounds the probability of making at least one false rejection. In many trial settings, these hypotheses will additionally have a hierarchical structure that reflects the relative importance and links between different clinical objectives. The graphical approach of Bretz et al (2009) is a flexible and easily communicable way of controlling the FWER while respecting complex trial objectives and multiple structured hypotheses. However, the FWER can be a very stringent criterion that leads to procedures with low power, and may not be appropriate in exploratory trial settings. This motivates controlling generalized error rates, particularly when the number of hypotheses tested is no longer small. We consider the generalized familywise error rate (k-FWER), which is the probability of making k or more false rejections, as well as the tail probability of the false discovery proportion (FDP), which is the probability that the proportion of false rejections is greater than some threshold. We also consider asymptotic control of the false discovery rate, which is the expectation of the FDP. In this article, we show how to control these generalized error rates when using the graphical approach and its extensions. We demonstrate the utility of the resulting graphical procedures on three clinical trial case studies., (© 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.)

Objective: The phase IIIb A Study to Evaluate the Effects of Ocrelizumab on Immune Responses in Participants With Relapsing Forms of Multiple Sclerosis (VELOCE) study (NCT02545868) assessed responses to selected vaccines in ocrelizumab (OCR)-treated patients with relapsing multiple sclerosis., Methods: Patients were randomized 2:1 into the OCR group (n = 68; OCR 600 mg) or control group (n = 34; interferon beta or no disease-modifying therapy). All received tetanus toxoid (TT)-containing vaccine, Pneumovax (23-valent pneumococcal polysaccharide vaccine [23-PPV]), and keyhole limpet hemocyanin (KLH). The OCR group was subdivided into OCR1 (n = 33) and OCR2 (n = 35) at randomization. The OCR1 group received Prevnar (13-valent conjugate pneumococcal vaccine) 4 weeks after 23-PPV; the OCR2 and control groups received influenza vaccine. Vaccinations started 12 weeks after OCR initiation (OCR group) or on day 1 (control group)., Results: Positive response rate to TT vaccine at 8 weeks was 23.9% in the OCR vs 54.5% in the control group. Positive response rate to ≥5 serotypes in 23-PPV at 4 weeks was 71.6% in the OCR and 100% in the control group. Prevnar did not enhance response to pneumococcal serotypes in common with Pneumovax. Humoral response to KLH was decreased in the OCR vs control group. Seroprotection rates at 4 weeks against 5 influenza strains ranged from 55.6% to 80.0% in the OCR2 group and 75.0% to 97.0% in the control group., Conclusion: Peripherally B-cell-depleted OCR recipients mounted attenuated humoral responses to clinically relevant vaccines and the neoantigen KLH, suggesting that use of standard nonlive vaccines while on OCR treatment remains a consideration. For seasonal influenza vaccines, it is recommended to vaccinate patients on OCR because a potentially protective humoral response, even if attenuated, can be expected., Classification of Evidence: This study provides Class II evidence confirming that the humoral response to nonlive vaccines in patients with relapsing multiple sclerosis after OCR treatment is attenuated compared with untreated or interferon beta-treated patients, but they can still be expected to be protective., Clinicaltrialsgov Identifier: NCT02545868., (© 2020 American Academy of Neurology.)

The variables involved in the initiation and operation of the human immune system are considered. It is shown that the number of variations associated with disease and other detrimental conditions exceeds the number of lymphocyte cells available for control of these conditions. It is proposed that the immune system functions by changes in the ionic strength of metabolic fluids which in turn control the formation and stability of cell membranes. The application of these conditions to the control of bacterial, virus particle and other antigens is detailed. Observations supporting the proposals are presented., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019. Published by Elsevier Ltd.)

Summary: In many areas of biological research, hypotheses are tested in a sequential manner, without having access to future P-values or even the number of hypotheses to be tested. A key setting where this online hypothesis testing occurs is in the context of publicly available data repositories, where the family of hypotheses to be tested is continually growing as new data is accumulated over time. Recently, Javanmard and Montanari proposed the first procedures that control the FDR for online hypothesis testing. We present an R package, onlineFDR, which implements these procedures and provides wrapper functions to apply them to a historic dataset or a growing data repository., Availability and Implementation: The R package is freely available through Bioconductor (http://www.bioconductor.org/packages/onlineFDR)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Response-adaptive designs allow the randomization probabilities to change during the course of a trial based on cumulated response data so that a greater proportion of patients can be allocated to the better performing treatments. A major concern over the use of response-adaptive designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. In particular, we show that the naïve z-test can have an inflated type I error rate even after applying a Bonferroni correction. Simulation studies have often been used to demonstrate error control but do not provide a guarantee. In this article, we present adaptive testing procedures for normally distributed outcomes that ensure strong familywise error control by iteratively applying the conditional invariance principle. Our approach can be used for fully sequential and block randomized trials and for a large class of adaptive randomization rules found in the literature. We show there is a high price to pay in terms of power to guarantee familywise error control for randomization schemes with extreme allocation probabilities. However, for proposed Bayesian adaptive randomization schemes in the literature, our adaptive tests maintain or increase the power of the trial compared to the z-test. We illustrate our method using a three-armed trial in primary hypercholesterolemia., (© 2019 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.)

The biochemicals and reactions involved in the present mechanism of degradation of tumour cells during chemotherapy are reconsidered and limitations noted. Alternative mechanisms and treatment methods are detailed.

To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz ( Statistics & Probability Letters , 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance. Our extension allows for multiple selected candidates, as well as unequal stage one and two sample sizes.

The formation of brain compounds linked to Parkinson's and Alzheimer's brain diseases are described. Metabolic mechanisms involved in the control of reacting biochemicals are detailed. Relationships are established between the biochemical reactions, biochemical control mechanisms and the onset of Parkinson's, Alzheimer's. Treatments based on these relationships are proposed., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

It is proposed that human immunodeficiency lentivirus particles (HIV) are of ancient origin and have existed at a low level for centuries. The recent increase in the extent and effects of this lentivirus have occurred principally from change in the saccharide component of the human diet. This increased greatly the supply of oxalic acid required to produce the uracil component of lentivirus RN., (Copyright © 2016 Elsevier Masson SAS. All rights reserved.)

The problem of selection bias has long been recognized in the analysis of two-stage trials, where promising candidates are selected in stage 1 for confirmatory analysis in stage 2. To efficiently correct for bias, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been proposed for a wide variety of trial settings, but where the population parameter estimates are assumed to be independent. We relax this assumption and derive the UMVCUE in the multivariate normal setting with an arbitrary known covariance structure. One area of application is the estimation of odds ratios (ORs) when combining a genome-wide scan with a replication study. Our framework explicitly accounts for correlated single nucleotide polymorphisms, as might occur due to linkage disequilibrium. We illustrate our approach on the measurement of the association between 11 genetic variants and the risk of Crohn's disease, as reported in Parkes and others (2007. Sequence variants in the autophagy gene IRGM and multiple other replicating loci contribute to Crohn's disease susceptibility. Nat. Gen. 39: (7), 830-832.), and show that the estimated ORs can vary substantially if both selection and correlation are taken into account., (© The Author 2016. Published by Oxford University Press.)

Seamless phase II/III clinical trials offer an efficient way to select an experimental treatment and perform confirmatory analysis within a single trial. However, combining the data from both stages in the final analysis can induce bias into the estimates of treatment effects. Methods for bias adjustment developed thus far have made restrictive assumptions about the design and selection rules followed. In order to address these shortcomings, we apply recent methodological advances to derive the uniformly minimum variance conditionally unbiased estimator for two-stage seamless phase II/III trials. Our framework allows for the precision of the treatment arm estimates to take arbitrary values, can be utilised for all treatments that are taken forward to phase III and is applicable when the decision to select or drop treatment arms is driven by a multiplicity-adjusted hypothesis testing procedure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd., (© 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

Objectives: To determine the relationship between serum 25-hydroxyvitamin D [25(OH)D] levels and the likelihood of postoperative complications and fracture reoperation rate in orthopaedic trauma patients receiving vitamin D and calcium supplementation., Design: Retrospective case series., Setting: Level I trauma center, Midwestern United States., Patients: All orthopaedic trauma patients-18 years or older-over a 20-month period were included with available initial and repeat 25(OH)D serum levels. In total, 201 patients met inclusion criteria., Intervention: All patients received 1000 IU of vitamin D3 and 1500 mg of calcium daily. Vitamin D deficient and insufficient patients also received 50,000 IU of ergocalciferol (vitamin D2) weekly until 25(OH)D levels normalized or fractures healed., Main Outcome Measurements: fracture complications and 25(OH)D levels., Results: Fifteen patients experienced postoperative healing complications. There was no significant difference between initial (P = 0.92) or repeat (P = 0.91) 25(OH)D levels between patients with and without fracture healing complications. Twenty-eight patients required repeat orthopaedic surgery. There was no significant difference between initial (P = 0.62) or repeat (P = 0.18) 25(OH)D levels between patients who did or did not require repeat orthopaedic surgery. There was no significant difference between initial (P = 0.66) or repeat (P = 0.89) 25(OH)D levels between patients who did or did not require nonorthopaedic surgery., Conclusions: Serum 25(OH)D levels did not significantly affect the likelihood of fracture healing complications requiring surgery or any nonorthopaedic injury-related surgery., Level of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Objective: The purpose of this study was to determine the effectiveness of our vitamin D treatment protocol in managing low serum vitamin D levels in orthopaedic trauma patients., Methods: A retrospective review was conducted of all orthopaedic trauma patients at a university level I trauma center over 20 months. Patients were included if they had an initial and repeat 25-hydroxy (OH) vitamin D serum level available. Vitamin D deficiency was defined as serum 25-hydroxy vitamin D level with less than 20 ng/mL. Vitamin D insufficiency was defined as serum 25-hydroxy vitamin D level between 20 and 32 ng/mL. The standard regimen for all patients was over-the-counter vitamin D3 1000 IU and 1500 mg of calcium daily. Patients with vitamin D deficiency or insufficiency also received 50,000 IU of ergocalciferol (D2) weekly until their 25-hydroxyvitamin D level normalized or their fracture healed. No compliance monitoring was performed except for questioning at each clinic visit., Results: A total of 201 patients met the inclusion criteria. Thirty-two patients had a normal initial 25-hydroxyvitamin D level, and 84% maintained their normal level, whereas 16% became insufficient or deficient. There were 88 patients insufficient initially and 54.5% improved to normal and 8% became deficient. In the vitamin D deficiency group (81), 26% remained deficient and 74% improved to insufficient. The average increase in serum 25-OH vitamin D with treatment (in nanograms per milliliter) was statistically significant for both the insufficient and deficient groups., Conclusions: Vitamin D therapy improved the majority of the patients' vitamin D-25-OH level but did not normalize most. Patients with initial deficiency had the largest improvement. This study indicates that vigilance is required to adequately treat a low serum vitamin D-25-OH level.

When developing a new diagnostic test for a disease, there are often multiple candidate classifiers to choose from, and it is unclear if any will offer an improvement in performance compared with current technology. A two-stage design can be used to select a promising classifier (if one exists) in stage one for definitive validation in stage two. However, estimating the true properties of the chosen classifier is complicated by the first stage selection rules. In particular, the usual maximum likelihood estimator (MLE) that combines data from both stages will be biased high. Consequently, confidence intervals and p-values flowing from the MLE will also be incorrect. Building on the results of Pepe et al. (SIM 28:762-779), we derive the most efficient conditionally unbiased estimator and exact confidence intervals for a classifier's sensitivity in a two-stage design with arbitrary selection rules; the condition being that the trial proceeds to the validation stage. We apply our estimation strategy to data from a recent family history screening tool validation study by Walter et al. (BJGP 63:393-400) and are able to identify and successfully adjust for bias in the tool's estimated sensitivity to detect those at higher risk of breast cancer., (© 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Maintaining evidence-based nursing practice requires information literacy (IL) skills that should be established prior to completing an undergraduate nursing degree. Based on Bandura's social cognitive theory, this cross-sectional descriptive correlational study assessed the perceived and applied IL skills of graduate nursing students from two family nurse practitioner (FNP) programs in the midwestern United States. Results showed that although the 26 newly admitted FNP students demonstrated a high level of confidence in their IL skills, the students did not perform well in the actual IL skills test. According to Bandura, the students' confidence in their IL knowledge should allow students to be engaged in course activities requiring IL skills. Nurse educators teaching in undergraduate or graduate programs are in key positions to incorporate IL experiences into class activities to allow for skill assessment and further practice. Further research is needed on nursing students' IL self-efficacy and performance., (Copyright 2015, SLACK Incorporated.)

The origins of the characteristics associated with cells from both benign and malignant (cancer) tumors are proposed. It is shown that these deviant cells can originate as the result of the failure of the normal biochemical control of the formation of DNA and RNA. This change in characteristics is proposed as occurring in regions of metabolism where fluid movement is altered becoming restricted and giving rise to both physical and chemical changes in the associated cells. Treatment of cancerous cells based on the compounds with properties related to the cellular compounds involved in the control of nucleic acids is proposed., (© 2013 Wiley Periodicals, Inc.)

It is proposed that percussion reactions take place in cells of the sensory organs and the brain. The percussion pulses produced by such reactions are the basis of the recording of information and images generated by the five sensory organs. Also proposed and described are the mechanisms by which this information and these images can be recalled for use in the present time., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The imidazole structure is involved in the formation of several compounds in the human metabolism including methyl imidazole, imidazole acetic acid, histidine, histamine, carnosine and homocarnosine. A number of these compounds are widely distributed metabolites such as histamine which is present in basophil and mast cells as well as being present in muscle and brain cells. This work advances detailed proposals on the metabolic chemical reactions which produce and decompose the compounds. The activity of these compounds in detrimental allergic medical conditions is discussed and the origin of the medical observations of the imidazole based medical compound known as cimetidine is discussed., (Copyright © 2011 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Biochemical reactions in cells which involve oxalic acid are described. It is shown that this compound is required for the formation of uracil and orotic acid. The former is a component of RNA which is common to all cells in the human metabolism. On the basis of the biochemical reactions described a possible treatment to relieve the effects of calcium oxalate renal calculi whose origin is related to the metabolic concentration of oxalic acid is proposed.

The formation of cell membranes through the physical-chemical interaction of two hydrophilic colloidal fluids is applied to the formation of the membranes of brain and neural cells. Also described is the membrane mechanism of transfer of ions and compounds necessary for brain and neural cell functions into the cerebrospinal fluid through the blood-brain barrier. Changes in the cerebrospinal fluid giving rise to degradation of brain and neural cells and the formation of precipitates within the brain are considered. Monitoring of electrolyte changes in metabolic fluids is shown to be a possible method of predicting the onset of degenerate brain conditions.

Acid hydrolysis of components from the diet in the stomach require the presence of an acid and a hydrolysing agent. The acid involved is hydrochloric acid. The present mechanism of hydrochloric acid production in the stomach is demonstrated to be incompatible with measured intracellular or intercellular concentrations of the relevant ions. An alternative set of chemical reactions whereby hydrochloric acid is formed in the stomach is presented. The hydrolysing agent is identified and a mechanism of transfer of chemical compounds into the metabolism is described. The hypothesis demonstrates that some of chemical compounds produced in the stomach can induce conditions such as asthma and that the conditions of osteoporosis and hemochromatosis can be linked to the function of the stomach. Possible treatments for these and other conditions such as stomach acidity and anaemia are proposed.