Network meta-analysis for an ordinal outcome when outcome categorization varies across trials.

BACKGROUND: Binary outcomes are likely the most common in randomized controlled trials, but ordinal outcomes can also be of interest. For example, rather than simply collecting data on diseased versus healthy study subjects, investigators may collect information on the severity of disease, with no disease, mild, moderate, and severe disease as possible levels of the outcome. While some investigators may be interested in all levels of the ordinal variable, others may combine levels that are not of particular interest. Therefore, when research synthesizers subsequently conduct a network meta-analysis on a network of trials for which an ordinal outcome was measured, they may encounter a network in which outcome categorization varies across trials. METHODS: The standard method for network meta-analysis for an ordinal outcome based on a multinomial generalized linear model is not designed to accommodate the multiple outcome categorizations that might occur across trials. In this paper, we propose a network meta-analysis model for an ordinal outcome that allows for multiple categorizations. The proposed model incorporates the partial information provided by trials that combine levels through modification of the multinomial likelihoods of the affected arms, allowing for all available data to be considered in estimation of the comparative effect parameters. A Bayesian fixed effect model is used throughout, where the ordinality of the outcome is accounted for through the use of the adjacent-categories logit link. RESULTS: We illustrate the method by analyzing a real network of trials on the use of antibiotics aimed at preventing liver abscesses in beef cattle and explore properties of the estimates of the comparative effect parameters through simulation. We find that even with the categorization of the levels varying across trials, the magnitudes of the biases are relatively small and that under a large sample size, the root mean square errors become small as well. CONCLUSIONS: Our proposed method to conduct a network meta-analysis for an ordinal outcome when the categorization of the outcome varies across trials, which utilizes the adjacent-categories logit link, performs well in estimation. Because the method considers all available data in a single estimation, it will be particularly useful to research synthesizers when the network of interest has only a limited number of trials for each categorization of the outcome.

A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E).

BACKGROUND: Observational epidemiologic studies provide critical data for the evaluation of the potential effects of environmental, occupational and behavioural exposures on human health. Systematic reviews of these studies play a key role in informing policy and practice. Systematic reviews should incorporate assessments of the risk of bias in results of the included studies. OBJECTIVE: To develop a new tool, Risk Of Bias In Non-randomized Studies - of Exposures (ROBINS-E) to assess risk of bias in estimates from cohort studies of the causal effect of an exposure on an outcome. METHODS AND RESULTS: ROBINS-E was developed by a large group of researchers from diverse research and public health disciplines through a series of working groups, in-person meetings and pilot testing phases. The tool aims to assess the risk of bias in a specific result (exposure effect estimate) from an individual observational study that examines the effect of an exposure on an outcome. A series of preliminary considerations informs the core ROBINS-E assessment, including details of the result being assessed and the causal effect being estimated. The assessment addresses bias within seven domains, through a series of 'signalling questions'. Domain-level judgements about risk of bias are derived from the answers to these questions, then combined to produce an overall risk of bias judgement for the result, together with judgements about the direction of bias. CONCLUSION: ROBINS-E provides a standardized framework for examining potential biases in results from cohort studies. Future work will produce variants of the tool for other epidemiologic study designs (e.g. case-control studies). We believe that ROBINS-E represents an important development in the integration of exposure assessment, evidence synthesis and causal inference.

OssaNMA: An R package for using information from network meta-analyses to optimize the power and sample allocation of a new two-arm trial.

Randomized clinical trials (RCTs) are designed for measuring the effectiveness of the treatments and testing a hypothesis regarding the relative effect between two or more treatments. Trial designers are often interested in maximizing power when the total sample size is fixed or minimizing the required total sample size to reach a pre-specified power. One approach to maximizing power proposed by previous researchers is to leverage prior evidence using meta-analysis (NMA) to inform the sample size determination of a new trial. For example, researchers may be interested in designing a two-arm trial comparing treatments A and B which are already in the existing trial network but do not have any direct comparison. The researchers' intention is to incorporate the result into an existing network for meta-analysis. Here we develop formulas to address these options and use simulations to validate our formula and evaluate the performance of different analysis methods in terms of power. We also implement our proposed method into the R package OssaNMA and publish an R Shiny app for the convenience of the application. The goal of the package is to enable researchers to readily adopt the proposed approach which can improve the power of an RCT and is therefore resource-saving. In the R Shiny app, We also provide the option to include the cost of each treatment which would enable researchers to compare the total treatment cost associated with each design and analysis approach. Further, we explore the effect of allocation to treatment group on study power when the a priori plan is to incorporate the new trial result into an existing network for meta-analysis.

Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment.

BACKGROUND: Planning the design of a new trial comparing two treatments already in a network of trials with an a-priori plan to estimate the effect size using a network meta-analysis increases power or reduces the sample size requirements. However, when the comparison of interest is between a treatment already in the existing network (old treatment) and a treatment that hasn't been studied previously (new treatment), the impact of leveraging information from the existing network to inform trial design has not been extensively investigated. We aim to identify the most powerful trial design for a comparison of interest between an old treatment A and a new treatment Z, given a fixed total sample size. We consider three possible designs: a two-arm trial between A and Z ('direct two-arm'), a two-arm trial between another old treatment B and Z ('indirect two-arm'), and a three-arm trial among A, B, and Z. METHODS: We compare the standard error of the estimated effect size between treatments A and Z for each of the three trial designs using formulas. For continuous outcomes, the direct two-arm trial always has the largest power, while for a binary outcome, the minimum variances among the three trial designs are conclusive only when [Formula: see text]. Simulation studies are conducted to demonstrate the potential for the indirect two-arm and three-arm trials to outperform the direct two-arm trial in terms of power under the condition of [Formula: see text]. RESULTS: Based on the simulation results, we observe that the indirect two-arm and three-arm trials have the potential to be more powerful than a direct two-arm trial only when [Formula: see text]. This power advantage is influenced by various factors, including the risk of the three treatments, the total sample size, and the standard error of the estimated effect size from the existing network meta-analysis. CONCLUSIONS: The standard two-arm trial design between two treatments in the comparison of interest may not always be the most powerful design. Utilizing information from the existing network meta-analysis, incorporating an additional old treatment into the trial design through an indirect two-arm trial or a three-arm trial can increase power.

Does Vaccine-Induced Maternally-Derived Immunity Protect Swine Offspring against Influenza a Viruses? A Systematic Review and Meta-Analysis of Challenge Trials from 1990 to May 2021.

It is unclear if piglets benefit from vaccination of sows against influenza. For the first time, methods of evidence-based medicine were applied to answer the question: "Does vaccine-induced maternally-derived immunity (MDI) protect swine offspring against influenza A viruses?". Challenge trials were reviewed that were published from 1990 to April 2021 and measured at least one of six outcomes in MDI-positive versus MDI-negative offspring (hemagglutination inhibition (HI) titers, virus titers, time to begin and time to stop shedding, risk of infection, average daily gain (ADG), and coughing) (n = 15). Screening and extraction of study characteristics was conducted in duplicate by two reviewers, with data extraction and assessment for risk of bias performed by one. Homology was defined by the antigenic match of vaccine and challenge virus hemagglutinin epitopes. Results: Homologous, but not heterologous MDI, reduced virus titers in piglets. There was no difference, calculated as relative risks (RR), in infection incidence risk over the entire study period; however, infection hazard (instantaneous risk) was decreased in pigs with MDI (log HR = -0.64, 95% CI: -1.13, -0.15). Overall, pigs with MDI took about a ½ day longer to begin shedding virus post-challenge (MD = 0.51, 95% CI: 0.03, 0.99) but the hazard of infected pigs ceasing to shed was not different (log HR = 0.32, 95% CI: -0.29, 0.93). HI titers were synthesized qualitatively and although data on ADG and coughing was extracted, details were insufficient for conducting meta-analyses. Conclusion: Homology of vaccine strains with challenge viruses is an important consideration when assessing vaccine effectiveness. Herd viral dynamics are complex and may include concurrent or sequential exposures in the field. The practical significance of reduced weaned pig virus titers is, therefore, not known and evidence from challenge trials is insufficient to make inferences on the effects of MDI on incidence risk, time to begin or to cease shedding virus, coughing, and ADG. The applicability of evidence from single-strain challenge trials to field practices is limited. Despite the synthesis of six outcomes, challenge trial evidence does not support or refute vaccination of sows against influenza to protect piglets. Additional research is needed; controlled trials with multi-strain concurrent or sequential heterologous challenges have not been conducted, and sequential homologous exposure trials were rare. Consensus is also warranted on (1) the selection of core outcomes, (2) the sizing of trial populations to be reflective of field populations, (3) the reporting of antigenic characterization of vaccines, challenge viruses, and sow exposure history, and (4) on the collection of non-aggregated individual pig data.

Lipolysis inhibition as a treatment of clinical ketosis in dairy cows: A randomized clinical trial.

Excessive and protracted lipolysis in adipose tissues of dairy cows is a major risk factor for clinical ketosis (CK). This metabolic disease is common in postpartum cows when lipolysis provides fatty acids as an energy substrate to offset negative energy balance. Lipolysis in cows can be induced by the canonical (hormonally induced) and inflammatory pathways. Current treatments for CK focus on improving glucose in blood (i.e., oral propylene glycol [PG], or i.v. dextrose). However, these therapies do not inhibit the canonical and inflammatory lipolytic pathways. Niacin (NIA) can reduce activation of the canonical pathway. Blocking inflammatory responses with cyclooxygenase inhibitors such as flunixin meglumine (FM) can inhibit inflammatory lipolytic activity. The objective of this study was to determine the effects of including NIA and FM in the standard PG treatment for postpartum CK on circulating concentrations of ketone bodies. A 4-group, parallel, individually randomized trial was conducted in multiparous Jersey cows (n = 80) from a commercial dairy in Michigan during a 7-mo period. Eligible cows had CK symptoms (lethargy, depressed appetite, and milk yield) and hyperketonemia (blood ß-hydroxybutyrate [BHB] ≥1.2 mmol/L). Cows with CK were randomly assigned to 1 of 3 groups where the first group received 310 g of oral PG once per day for 5 d; the second group received PG for 5 d + 24 g of oral NIA once per day for 3 d (PGNIA); and the third group received PG for 5 d + NIA for 3 d + 1.1 mg/kg i.v. FM once per day for 3 d (PGNIAFM). The control group consisted of cows that were clinically healthy (HC; untreated; BHB <1.2 mmol/L, n = 27) matching for parity and DIM with all 3 groups. Animals were sampled at enrollment (d 0), and d 3, 7, and 14 to evaluate ketone bodies and circulating metabolic and inflammatory biomarkers. Effects of treatment, sampling day, and their interactions were evaluated using mixed effects models. Logistic regression was used to calculate the odds ratio (OR) of returning to normoketonemia (BHB <1.2 mmol/L). Compared with HC, enrolled CK cows exhibited higher blood concentrations of dyslipidemia markers, including nonesterified fatty acids (NEFA) and BHB, and lower glucose and insulin levels. Cows with CK also had increased levels of biomarkers of pain (substance P), inflammation, including lipopolysaccharide-binding protein, haptoglobin, and serum amyloid A, and proinflammatory cytokines IL-4, MCP-1, MIP-1α, and TNFα. Importantly, 72.2% of CK cows presented endotoxemia and had higher circulating bacterial DNA compared with HC. By d 7, the percentage of cows with normoketonemia were higher in PGNIAFM = 87.5%, compared with PG = 58.33%, and PGNIA = 62.5%. At d 7 the OR for normoketonemia in PGNIAFM cows were 1.5 (95% CI, 1.03-2.17) and 1.4 (95% CI, 0.99-1.97) relative to PG and PGNIA, respectively. At d 3, 7, and 14, PGNIAFM cows presented the lowest values of BHB (PG = 1.36; PGNIA = 1.24; PGNIAFM = 0.89 ± 0.13 mmol/L), NEFA (PG = 0.58; PGNIA = 0.59; PGNIAFM = 0.45 ± 0.02 mmol/L), and acute phase proteins. Cows in PGNIAFM also presented the highest blood glucose increment across time points and insulin by d 7. These data provide evidence that bacteremia or endotoxemia, systemic inflammation, and pain may play a crucial role in CK pathogenesis. Additionally, targeting lipolysis and inflammation with NIA and FM during CK effectively reduces dyslipidemia biomarkers, improves glycemia, and improves overall clinical recovery.

When we shouldn't borrow information from an existing network of trials for planning a new trial.

Introduction: To achieve higher power or increased precision for a new trial, methods based on updating network meta-analysis (NMA) have been proposed by researchers. However, this approach could potentially lead to misinterpreted results and misstated conclusions. This work aims to investigate the potential inflation of type I error risk when a new trial is conducted only when, based on a p-value of the comparison in the existing network, a "promising" difference between two treatments is noticed. Methods: We use simulations to evaluate the scenarios of interest. In particular, a new trial is to be conducted independently or depending on the results from previous NMA in various scenarios. Three analysis methods are applied to each simulation scenario: with the existing network, sequential analysis and without the existing network. Results: For the scenario that the new trial will be conducted only when a promising finding (p-value <5%) is indicated by the existing network, the type I error risk increased dramatically (38.5% in our example data) when analyzed with the existing network and sequential analysis. The type I error is controlled at 5% when analyzing the new trial without the existing network. Conclusion: If the intention is to combine a trial result with an existing network of evidence, or if it is expected that the trial will eventually be included in a network meta-analysis, then the decision that a new trial is performed should not depend on a statistically "promising" finding indicated by the existing network.

The standards of reporting randomized trials in pets (PetSORT): Methods and development processes.

Background: Reporting of clinical trials conducted in client- and shelter-owned dog and cat populations is not optimal, which inhibits the ability to assess the reliability and validity of trial findings and precludes the ability to include some trials in evidence synthesis. Objective: To develop a reporting guideline for parallel group and crossover trials that addresses the unique features and reporting requirements for trials conducted in client- and shelter-owned dog and cat populations. Design: Consensus statement. Setting: Virtual. Participants: Fifty-six experts from North America, the United Kingdom, Europe, and Australia working in academia, government (research and regulatory agencies), industry, and clinical veterinary practice. Methods: A steering committee created a draft checklist for reporting criteria based upon the CONSORT statement and the CONSORT extensions for reporting of abstracts and crossover trials. Each item was presented to the expert participants and was modified and presented again until >85% of participants were in agreement about the inclusion and wording of each item in the checklist. Results: The final PetSORT checklist consists of 25 main items with several sub-items. Most items were modifications of items contained in the CONSORT 2010 checklist or the CONSORT extension for crossover trials, but 1 sub-item pertaining to euthanasia was created de novo. Conclusion: The methods and processes used to develop this guideline represent a novel departure from those used to create other reporting guidelines, by using a virtual format. The use of the PetSORT statement should improve reporting of trials conducted in client- and shelter-owned dogs and cats and published in the veterinary research literature.

The standards of reporting trials in pets (PetSORT): Explanation and elaboration.

Well-designed randomized controlled trials (RCTs) provide the best evidence of the primary research designs for evaluating the effectiveness of interventions. However, if RCTs are incompletely reported, the methodological rigor with which they were conducted cannot be reliably evaluated and it may not be possible to replicate the intervention. Missing information also may limit the reader's ability to evaluate the external validity of a trial. Reporting guidelines are available for clinical trials in human healthcare (CONSORT), livestock populations (REFLECT), and preclinical experimental research involving animals (ARRIVE 2.0). The PetSORT guidelines complement these existing guidelines, providing recommendations for reporting controlled trials in pet dogs and cats. The rationale and scientific background are explained for each of the 25 items in the PetSORT reporting recommendations checklist, with examples from well-reported trials.

Connecting a disconnected trial network with a new trial: optimizing the estimation of a comparative effect in a network meta-analysis.

BACKGROUND: In network meta-analysis, estimation of a comparative effect can be performed for treatments that are connected either directly or indirectly. However, disconnected trial networks may arise, which poses a challenge to comparing all available treatments of interest. Several modeling approaches attempt to compare treatments from disconnected networks but not without strong assumptions and limitations. Conducting a new trial to connect a disconnected network can enable calculation of all treatment comparisons and help researchers maximize the value of the existing networks. Here, we develop an approach to finding the best connecting trial given a specific comparison of interest. METHODS: We present formulas to quantify the variation in the estimation of a particular comparative effect of interest for any possible connecting two-arm trial. We propose a procedure to identify the optimal connecting trial that minimizes this variation in effect estimation. RESULTS: We show that connecting two treatments indirectly might be preferred to direct connection through a new trial, by leveraging information from the existing disconnected networks. Using a real network of studies on the use of vaccines in the treatment of bovine respiratory disease (BRD), we illustrate a procedure to identify the best connecting trial and confirm our findings via simulation. CONCLUSION: Researchers wishing to conduct a connecting two-arm study can use the procedure provided here to identify the best connecting trial. The choice of trial that minimizes the variance of a comparison of interest is network dependent and it is possible that connecting treatments indirectly may be preferred to direct connection.

Using information from network meta-analyses to optimize the power and sample allocation of a subsequent trial with a new treatment.

BACKGROUND: A critical step in trial design is determining the sample size and sample allocation to ensure the proposed study has sufficient power to test the hypothesis of interest: superiority, equivalence, or non-inferiority. When data are available from prior trials and leveraged with the new trial to answer the scientific questions, the value of society's investment in prior research is increased. When prior information is available, the trial design including the sample size and allocation should be adapted accordingly, yet the current approach to trial design does not utilize such information. Ensuring we maximize the value of prior research is essential as there are always constraints on resources, either physical or financial, and designing a trial with adequate power can be a challenge. METHODS: We propose an approach to increasing the power of a new trial by incorporating evidence from a network meta-analysis into the new trial design and analysis. We illustrate the methodology through an example network meta-analysis, where the goal is to identify the optimal allocation ratio for the new three-arm trial, which involves the reference treatment, the new treatment, and the negative control. The primary goal of the new trial is to show that the new treatment is non-inferior to the reference treatment. It may also be of interest to know if the new treatment is superior to the negative control. We propose an optimal treatment allocation strategy which is derived from minimizing the standard error of the log odds ratio estimate of the comparison of interest. We conducted a simulation study to assess the proposed methods to design a new trial while borrowing information from the existing network meta-analysis and compare it to even allocation methods. RESULTS: Using mathematical derivation and simulations, we document that our proposed approach can borrow information from a network meta-analysis to modify the treatment allocation ratio and increase the power of the new trial given a fixed total sample size or to reduce the total sample size needed to reach a desired power. CONCLUSIONS: When prior evidence about the hypotheses of interest is available, the traditional equal allocation strategy is not the most powerful approach anymore. Our proposed methodology can improve the power of trial design, reduce the cost of trials, and maximize the utility of prior investments in research.

Watch your language: An exploration of the use of causal wording in veterinary observational research.

Observational research may be conducted to predict an outcome or to identify associations between an intervention or risk factor (an "exposure") and an outcome. However, the end goal of observational research often is to identify exposures that can be manipulated to improve an outcome, meaning that the aim is identify causal relationships. Causal inference from observational studies may be appropriate when an exposure-outcome of interest is identified, causal reasoning is used to identify confounders, confounders are adequately controlled, and theoretical issues, such as temporality, are considered. If these conditions are not met, causal inference cannot be made in an observational study. The objective of our study was to explore the use of causal language in veterinary observational studies, and to compare the use of causal language between studies that appear to be predictive or associational in purpose vs. those that appear to be exploring causal relationships. The dataset comprised 200 observational studies in veterinary species published between 2020 and 2022. The majority (117 out of 200) were cross-sectional studies. There were 48 studies that described an exposure-outcome of interest, and we considered these studies to be exploring potential causal relationships; of note, this liberal categorization would be anticipated to overestimate the proportion of studies suitably designed for causal inference. Overall, 172 studies (86%) used causal wording in at least one section of the article. Causal language was used in 128/152 (84%) of studies exploring predictions or associations; this language implies causation when it is not appropriate to do so. In studies designed such that causal inference might be possible, 44/48 (92%) used causal language in one or more sections. There were no substantive differences in the use of causal wording between observational study designs, exposure types, or whether the first author's affiliation was a country in which English is an official language. There is a need for authors of veterinary observational studies to explicitly state the purpose of the study (associational, predictive, or causal), and to use causal wording appropriately based on the aim of the study.

Invited review: Maximizing value and minimizing waste in clinical trial research in dairy cattle: Selecting interventions and outcomes to build an evidence base.

Clinical trials are a valuable study design for evaluating interventions when it is ethical and feasible for investigators to randomly allocate study animals to intervention groups. Researchers may choose to evaluate the comparative efficacy of intervention groups for their effect on outcomes that are relevant to the specific objectives of their trial. However, the results across multiple trials on the same intervention and with the same outcome should be considered when making decisions on whether to use an intervention, because the results of a single trial are subject to sampling error and do not reflect all biological variability. The objective of this review was to provide an overview of important concepts when selecting intervention groups and outcomes within a randomized controlled trial, and when building a body of evidence for intervention efficacy across multiple trials. Empirical evidence is presented to highlight that integrating and interpreting the efficacy of an intervention across trials is hindered by a lack of replication of interventions across trials. Inconsistency in the outcomes and their measurement among trials also limits the ability to build a body of evidence for the efficacy of interventions. The development of core outcome sets for specific topic areas in dairy science, updated as necessary, may improve consistency across trials and aid in the development of a body of evidence for evidence-based decision-making.

Food insecurity among African Americans in the United States: A scoping review.

In 2019, the estimated prevalence of food insecurity for Black non-Hispanic households was higher than the national average due to health disparities exacerbated by forms of racial discrimination. During the COVID-19 pandemic, Black households have experienced higher rates of food insecurity when compared to other populations in the United States. The primary objectives of this review were to identify which risk factors have been investigated for an association with food insecurity, describe how food insecurity is measured across studies that have evaluated this outcome among African Americans, and determine which dimensions of food security (food accessibility, availability, and utilization) are captured by risk factors studied by authors. Food insecurity related studies were identified through a search of Google Scholar, PubMed, CINAHL Plus, MEDLINE®, PsycINFO, Health Source: Nursing/Academic Edition, and Web of Science™ (Clarivate), on May 20, 2021. Eligible studies were primary research studies, with a concurrent comparison group, published in English between 1995 and 2021. Ninety-eight relevant studies were included for data charting with 37 unique measurement tools, 115 risk factors, and 93 possible consequences of food insecurity identified. Few studies examined factors linked to racial discrimination, behaviour, or risk factors that mapped to the food availability dimension of food security. Infrequently studied factors, such as lifetime racial discrimination, socioeconomic status (SES), and income insecurity need further investigation while frequently studied factors such as age, education, race/ethnicity, and gender need to be summarized using a systematic review approach so that risk factor impact can be better assessed. Risk factors linked to racial discrimination and food insecurity need to be better understood in order to minimize health disparities among African American adults during the COVID-19 pandemic and beyond.

Levels of Evidence, Quality Assessment, and Risk of Bias: Evaluating the Internal Validity of Primary Research.

Clinical decisions in human and veterinary medicine should be based on the best available evidence. The results of primary research are an important component of that evidence base. Regardless of whether assessing studies for clinical case management, developing clinical practice guidelines, or performing systematic reviews, evidence from primary research should be evaluated for internal validity i.e., whether the results are free from bias (reflect the truth). Three broad approaches to evaluating internal validity are available: evaluating the potential for bias in a body of literature based on the study designs employed (levels of evidence), evaluating whether key study design features associated with the potential for bias were employed (quality assessment), and applying a judgement as to whether design elements of a study were likely to result in biased results given the specific context of the study (risk of bias assessment). The level of evidence framework for assessing internal validity assumes that internal validity can be determined based on the study design alone, and thus makes the strongest assumptions. Risk of bias assessments involve an evaluation of the potential for bias in the context of a specific study, and thus involve the least assumptions about internal validity. Quality assessment sits somewhere between the assumptions of these two. Because risk of bias assessment involves the least assumptions, this approach should be used to assess internal validity where possible. However, risk of bias instruments are not available for all study designs, some clinical questions may be addressed using multiple study designs, and some instruments that include an evaluation of internal validity also include additional components (e.g., evaluation of comprehensiveness of reporting, assessments of feasibility or an evaluation of external validity). Therefore, it may be necessary to embed questions related to risk of bias within existing quality assessment instruments. In this article, we overview the approaches to evaluating internal validity, highlight the current complexities, and propose ideas for approaching assessments of internal validity.

Publication and accessibility of results of controlled trials in dairy science.

Research allows for the discovery of new knowledge and is integral to evidence-based decision-making. However, research is only useful if it is available. The aim of this study was to explore publication and accessibility of full-text reports for controlled trials (experimental studies) conducted in dairy cattle. We determined the proportion of controlled trials presented as abstracts at the 2015 Joint Annual Meeting of the American Dairy Science Association and the American Society of Animal Science or the 2015 American Association of Bovine Practitioners Annual Conference that were subsequently published. Factors associated with publication or non-publication in a peer-reviewed journal were evaluated using risk ratios. For trials that were subsequently published, we compared the sample size, numerical results, and inference between the conference abstract and the subsequent publication. Approximately half of the trials (177 out of 380) reported at conferences were subsequently published. Source conference, whether the conference abstract results were described as preliminary, whether there was at least one positive outcome, author affiliation, whether the trial involved deliberate disease induction, and total sample size were not strongly associated with subsequent publication. For trials that were published, the sample size differed between the conference proceedings and full publications for 22%, the numerical results differed in 29%, and the inference differed for 11%. We also evaluated whether trials included in 9 recent systematic reviews were in English and were available without subscription or cost. Of the 390 trials included in recent systematic reviews, approximately 40% were available only through subscription or access fee. These results suggest that publication and accessibility of research results is suboptimal, representing an area of wastage in dairy cattle research. Researchers should ensure that they publish the results of trials comprehensively in searchable publications, even if the results are not novel or do not detect expected differences, and, when possible, make the results available freely.

Current use of biologic therapies for musculoskeletal disease: A survey of board-certified equine specialists.

OBJECTIVE: To evaluate the use of mesenchymal stem cells (MSCs), autologous conditioned serum (ACS), platelet-rich plasma (PRP), and autologous protein solution (APS) for the treatment of equine musculoskeletal disease by diplomates of the American College of Veterinary Surgery (ACVS), and American College of Veterinary Sports Medicine and Rehabilitation (ACVSMR). STUDY DESIGN: Cross-sectional study. SAMPLE POPULATION: Diplomates (n = 423). METHODS: An email link was sent to ACVS and ACVMR diplomates. A survey contained 59 questions regarding demographics, as well as indications, frequency, adverse effects, and limitations of use. Responses were analyzed using Fisher's exact test. RESULTS: One hundred and fifty four surveys were analyzed. Years in practice and type of practice were not associated with biologic therapy use. PRP was the most used therapy (120/137; 87.5%). PRP and MSCs were most often administered intralesionally while ACS and APS were most often administered intra-articularly. ACS (50/104; 48.1%) treatment was repeated commonly within 2 weeks of initial injection. MSCs (39/90; 43.3%) and PRP (38/100; 38%) were commonly repeated 1-2 months after initial injection and APS was typically repeated >4 months after initial injection (21/53; 39.6%). Local inflammation and expense were the most common adverse effect and limitation of use. CONCLUSION: Diplomates most commonly utilized PRP and MSC intralesionally for soft-tissue injuries, and ACS and ACP intra-articularly for joint injury. Protocols for repeated administration varied widely. Local inflammation was a clinical concern with the use of biologics. CLINICAL SIGNIFICANCE: Biologic therapies are used commonly by ACVS and ACVSMR diplomates for soft tissue and joint disease.

Salmonella detection in commercially prepared livestock feed and the raw ingredients and equipment used to manufacture the feed: A systematic review and meta-analysis.

Salmonella contamination of livestock feed is a serious veterinary and public health issue. In this study we used a systematic review to assess the prevalence and characterization of Salmonella isolates detected in raw feed components, feed milling equipment and finished feed from 97 studies published from 1955 to 2020 across seven global regions. Eighty-five studies were included in a meta-analyses to estimate the combined prevalence of Salmonella detection and to compare the risk of contamination associated with different sample types. We found the overall combined prevalence estimate of Salmonella detection was 0.14 with a prevalence of 0.18 in raw feed components, 0.09 in finished feed and 0.08 in feed milling equipment. Animal based raw feed components were 3.9 times more likely to be contaminated with Salmonella than plant based raw feed components. Differences between studies accounted for 99 % of the variance in the prevalence estimate for all sample types and there was no effect of region on the prevalence estimates. The combined prevalence of Salmonella detection in raw feed components decreased from 0.25 in 1955 to 0.11 in 2019. The proportion of Salmonella isolates that were resistant to antimicrobials was largest for amikacin (0.20), tetracycline (0.18) streptomycin (0.17), cefotaxime (0.14) and sulfisoxazole (0.11). The prevalence of Salmonella contamination of animal feed varies widely between individual studies and is an ongoing challenge for the commercial feed industry. Control relies on the vigilant monitoring and control of Salmonella in each individual mill.

A Bayesian approach to modeling antimicrobial multidrug resistance.

Multidrug resistance (MDR) has been a significant threat to public health and effective treatment of bacterial infections. Current identification of MDR is primarily based upon the large proportions of isolates resistant to multiple antibiotics simultaneously, and therefore is a belated evaluation. For bacteria with MDR, we expect to see strong correlations in both the quantitative minimum inhibitory concentration (MIC) and the binary susceptibility as classified by the pre-determined breakpoints. Being able to detect correlations from these two perspectives allows us to find multidrug resistant bacteria proactively. In this paper, we provide a Bayesian framework that estimates the resistance level jointly for antibiotics belonging to different classes with a Gaussian mixture model, where the correlation in the latent MIC can be inferred from the Gaussian parameters and the correlation in binary susceptibility can be inferred from the mixing weights. By augmenting the laboratory measurement with the latent MIC variable to account for the censored data, and by adopting the latent class variable to represent the MIC components, our model was shown to be accurate and robust compared with the current assessment of correlations. Applying the model to Salmonella heidelberg samples isolated from human participants in National Antimicrobial Resistance Monitoring System (NARMS) provides us with signs of joint resistance to Amoxicillin-clavulanic acid & Cephalothin and joint resistance to Ampicillin & Cephalothin. Large correlations estimated from our model could serve as a timely tool for early detection of MDR, and hence a signal for clinical intervention.

A likelihood ratio test for the homogeneity of between-study variance in network meta-analysis.

BACKGROUND: Network meta-analysis (NMA) is a statistical method used to combine results from several clinical trials and simultaneously compare multiple treatments using direct and indirect evidence. Statistical heterogeneity is a characteristic describing the variability in the intervention effects being evaluated in the different studies in network meta-analysis. One approach to dealing with statistical heterogeneity is to perform a random effects network meta-analysis that incorporates a between-study variance into the statistical model. A common assumption in the random effects model for network meta-analysis is the homogeneity of between-study variance across all interventions. However, there are applications of NMA where the single between-study assumption is potentially incorrect and instead the model should incorporate more than one between-study variances. METHODS: In this paper, we develop an approach to testing the homogeneity of between-study variance assumption based on a likelihood ratio test. A simulation study was conducted to assess the type I error and power of the proposed test. This method is then applied to a network meta-analysis of antibiotic treatments for Bovine respiratory disease (BRD). RESULTS: The type I error rate was well controlled in the Monte Carlo simulation. We found statistical evidence (p value = 0.052) against the homogeneous between-study variance assumption in the network meta-analysis BRD. The point estimate and confidence interval of relative effect sizes are strongly influenced by this assumption. CONCLUSIONS: Since homogeneous between-study variance assumption is a strong assumption, it is crucial to test the validity of this assumption before conducting a network meta-analysis. Here we propose and validate a method for testing this single between-study variance assumption which is widely used for many NMA.