Optimal biological dose selection in dose-finding trials with model-assisted designs based on efficacy and toxicity: a simulation study.

With the emergence of molecular targeted agents and immunotherapies in anti-cancer treatment, a concept of optimal biological dose (OBD), accounting for efficacy and toxicity in the framework of dose-finding, has been widely introduced into phase I oncology clinical trials. Various model-assisted designs with dose-escalation rules based jointly on toxicity and efficacy are now available to establish the OBD, where the OBD is generally selected at the end of the trial using all toxicity and efficacy data obtained from the entire cohort. Several measures to select the OBD and multiple methods to estimate the efficacy probability have been developed for the OBD selection, leading to many options in practice; however, their comparative performance is still uncertain, and practitioners need to take special care of which approaches would be the best for their applications. Therefore, we conducted a comprehensive simulation study to demonstrate the operating characteristics of the OBD selection approaches. The simulation study revealed key features of utility functions measuring the toxicity-efficacy trade-off and suggested that the measure used to select the OBD could vary depending on the choice of the dose-escalation procedure. Modelling the efficacy probability might lead to limited gains in OBD selection.

A generalized Bayesian optimal interval design for dose optimization in immunotherapy.

For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.

An analysis of the contents of the young-onset dementia helpline: profiles of clients who consulted the helpline themselves.

BACKGROUND: Young-onset dementia (YOD) community care requires personalised approaches. Yet, the specific details of YOD consultations are unclear. This study explored how initial consultations correlate with client profiles. METHODS: Data from regional YOD helplines were used to analyze the main characteristics of people living with YOD or who had concerns about the possibility of YOD (n = 132). Among several categorical variables, the following were used for analysis: age group, sex, type of living arrangement, employment status, presence of dementia, and content of the consultation. To identify groups of items that frequently occur together, strongly connected rules were identified using association rule analysis with the a priori algorithm. To focus on the characteristics of clients, rules related to client characteristics were extracted based on the type of consultation. RESULTS: A total of 51 rules were identified for the consultations. These rules fell into two categories: (1) consultations for medical matters, which mainly involved employed individuals with undiagnosed dementia, and (2) other consultations on daily life or work, which mainly involved individuals diagnosed with dementia and were characterised by the influence of sex. These rules indicate the importance of medical involvement in confirming the diagnosis and specific individualised care following diagnosis for people living with YOD. CONCLUSION: Clients with or without a dementia diagnosis were consulted differently in the YOD helplines. Before receiving a diagnosis, medical matters were the main theme of consultations, whereas after receiving a diagnosis, adjustments to daily life or work were the main themes. The results of this study suggest that the needs of people living with YOD and the services they require may vary depending on their backgrounds.

A Bayesian optimal interval design for dose optimization with a randomization scheme based on pharmacokinetics outcomes in oncology.

The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans from a pharmacological perspective. Several novel anticancer agents have been shown to have significant exposure-efficacy relationships, and some PK information has been considered an important predictor of efficacy. This paper proposes a Bayesian optimal interval design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients allocated to OD in various realistic settings.

TITE-gBOIN-ET: Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes.

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular-targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate-grade toxicities than dose-limiting toxicities. Besides, for efficacy, evaluating the overall response and long-term stable disease in solid tumors and considering the difference between complete remission and partial remission in lymphoma are preferable. It is also essential to accelerate early-stage trials to shorten the entire period of drug development. However, it is often challenging to make real-time adaptive decisions due to late-onset outcomes, fast accrual rates, and differences in outcome evaluation periods for efficacy and toxicity. To solve the issues, we propose a time-to-event generalized Bayesian optimal interval design to accelerate dose finding, accounting for efficacy and toxicity grades. The new design named "TITE-gBOIN-ET" design is model-assisted and straightforward to implement in actual oncology dose-finding trials. Simulation studies show that the TITE-gBOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment while having comparable or higher performance in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

Analysis of the complete genome sequences of Clostridium perfringens strains harbouring the binary enterotoxin BEC gene and comparative genomics of pCP13-like family plasmids.

BACKGROUND: BEC-producing Clostridium perfringens is a causative agent of foodborne gastroenteritis. It was first reported in 2014, and since then, several isolates have been identified in Japan and the United Kingdom. The novel binary ADP-ribosylating toxin BEC, which consists of two components (BECa and BECb), is encoded on a plasmid that is similar to pCP13 and harbours a conjugation locus, called Pcp, encoding homologous proteins of the type 4 secretion system. Despite the high in vitro conjugation frequency of pCP13, its dissemination and that of related plasmids, including bec-harbouring plasmids, in the natural environment have not been characterised. This lack of knowledge has limited our understanding of the genomic epidemiology of bec-harbouring C. perfringens strains. RESULTS: In this study, we determined the complete genome sequences of five bec-harbouring C. perfringens strains isolated from 2009 to 2019. Each isolate contains a ~ 3.36 Mbp chromosome and 1-3 plasmids of either the pCW3-like family, pCP13-like family, or an unknown family, and the bec-encoding region in all five isolates was located on a ~ 54 kbp pCP13-like plasmid. Phylogenetic and SNP analyses of these complete genome sequences and the 211 assembled C. perfringens genomes in GenBank showed that although these bec-harbouring strains were split into two phylogenetic clades, the sequences of the bec-encoding plasmids were nearly identical (>99.81%), with a significantly smaller SNP accumulation rate than that of their chromosomes. Given that the Pcp locus is conserved in these pCP13-like plasmids, we propose a mechanism in which the plasmids were disseminated by horizontal gene transfer. Data mining showed that strains carrying pCP13-like family plasmids were unexpectedly common (58/216 strains) and widely disseminated among the various C. perfringens clades. Although these plasmids possess a conserved Pcp locus, their 'accessory regions' can accommodate a wide variety of genes, including virulence-associated genes, such as becA/becB and cbp2. These results suggest that this family of plasmids can integrate various foreign genes and is transmissible among C. perfringens strains. CONCLUSION: This study demonstrates the potential significance of pCP13-like plasmids, including bec-encoding plasmids, for the characterisation and monitoring of the dissemination of pathogenic C. perfringens strains.

Evaluation of a novel medical device for pegfilgrastim administration.

Pegfilgrastim, a pegylated form of granulocyte colony-stimulating factor, has reduced the risk of developing febrile neutropenia, which is associated with an increase in severe infection and prolonged hospitalization. However, pegfilgrastim administration requires that patients visit hospital following cancer chemotherapy, thus imposing a burden on patients and those around them. An on-body injector (OBI), which automatically administers pegfilgrastim about 27 hours after chemotherapy, was used in this study. The OBI, which consists of a main pump unit and infusion set, is a drug delivery device designed to be attached to the patient's body, with a timer-controlled dosing function. This study was conducted in breast cancer patients to evaluate the safety of pegfilgrastim administered subcutaneously via the OBI. The study period consisted of screening and treatment observation periods involving four cycles of neoadjuvant or adjuvant chemotherapy with docetaxel plus cyclophosphamide. One 3.6-mg pegfilgrastim dose was administered subcutaneously via OBI during each cycle of chemotherapy. The study enrolled 35 patients, and no serious adverse events or febrile neutropenia occurred. Administration of pegfilgrastim was successfully completed at all times when the OBI was attached to the patient, and no safety concerns associated with OBI function arose. For outpatients requiring pegfilgrastim following cancer chemotherapy, the use of an OBI was considered to be a safe option to reduce the need for outpatient visits that restrict their activities of daily living.

Gene trapping reveals a new transcriptionally active genome element: The chromosome-specific clustered trap region.

Nearly half of the human genome consists of repetitive sequences such as long interspersed nuclear elements. The relationship between these repeating sequences and diseases has remained unclear. Gene trapping is a useful technique for disrupting a gene and expressing a reporter gene by using the promoter activity of the gene. The analysis of trapped genes revealed a new genome element-the chromosome-specific clustered trap (CSCT) region. For any examined sequence within this region, an equivalent was found using the BLAT of the University of California, Santa Cruz (UCSC) Genome Browser. CSCT13 mapped to chromosome 13 and contained only three genes. To elucidate its in vivo function, the whole CSCT13 region (1.6 Mbp) was deleted using the CRISPR/Cas9 system in mouse embryonic stem cells, and subsequently, a CSCT13 knockout mouse line was established. The rate of homozygotes was significantly lower than expected according to Mendel's laws. In addition, the number of offspring obtained by mating homozygotes was significantly smaller than that obtained by crossing controls. Furthermore, CSCT13 might have an effect on meiotic homologous recombination. This study identifies a transcriptionally active CSCT with an important role in mouse development.

Multiple imputation for longitudinal data using Bayesian lasso imputation model.

Multiple imputation is a promising approach to handle missing data and is widely used in analysis of longitudinal clinical studies. A key consideration in the implementation of multiple imputation is to obtain accurate imputed values by specifying an imputation model that incorporates auxiliary variables potentially associated with missing variables. The use of informative auxiliary variables is known to be beneficial to make the missing at random assumption more plausible and help to reduce uncertainty of the imputations; however, it is not straightforward to pre-specify them in many cases. We propose a data-driven specification of the imputation model using Bayesian lasso in the context of longitudinal clinical study, and develop a built-in function of the Bayesian lasso imputation model which is performed within the framework of multiple imputation using chained equations. A simulation study suggested that the Bayesian lasso imputation model worked well in a variety of longitudinal study settings, providing unbiased treatment effect estimates with well-controlled type I error rates and coverage probabilities of the confidence interval; in contrast, ignorance of the informative auxiliary variables led to serious bias and inflation of type I error rate. Moreover, the Bayesian lasso imputation model offered higher statistical powers compared with conventional imputation methods. In our simulation study, the gains in statistical power were remarkable when the sample size was small relative to the number of auxiliary variables. An illustration through a real example also suggested that the Bayesian lasso imputation model could give smaller standard errors of the treatment effect estimate.

Clinical parameters among patients in Japan with anemia and non-dialysis-dependent chronic kidney disease with and without diabetes mellitus who received roxadustat.

BACKGROUND: Roxadustat is an oral hypoxia-inducible factor prolyl hydroxylase inhibitor for treating anemia of chronic kidney disease (CKD). This post hoc analysis of a Japanese, open-label, partially randomized, phase 3 study in patients with non-dialysis-dependent (NDD) CKD evaluated disease state-related parameters among patients with and without diabetes mellitus who received roxadustat. In the 1517-CL-0310 study (NCT02988973), roxadustat was noninferior to darbepoetin alfa for change in average hemoglobin levels at Weeks 18-24 from baseline who received roxadustat. METHODS: Patients enrolled in the 1517-CL-0310 study who received roxadustat were included in this post hoc analysis. Hematologic (hemoglobin, reticulocyte/erythrocyte ratio, mean corpuscular volume [MCV], and mean corpuscular hemoglobin [MCH]), iron-related (ferritin, total iron-binding capacity, transferrin, ceruloplasmin, and hepcidin), metabolic (HbA1c, glycated albumin, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol), and renal (eGFR) parameters were summarized descriptively by visit through Week 52. RESULTS: Among 201 included patients, 105 (52.2%) and 96 (47.8%) were in the Diabetes and No Diabetes subgroups, respectively. There were no clinically meaningful differences through Week 52 for most hematologic, iron-related, metabolic, or renal parameters between patients in the Diabetes and No Diabetes subgroups. MCV and MCH remained lower and HbA1c and glycated albumin remained higher in patients in the Diabetes subgroup through Week 52. Both subgroups experienced a similar benefit from roxadustat in maintaining hemoglobin levels in the target range of 10-12 g/dL. CONCLUSION: Roxadustat maintained hemoglobin levels in the target range with similar clinical parameters irrespective of diabetes mellitus presence at baseline.

Confirmatory efficacy testing for individual dose-placebo comparisons using serial gatekeeping procedure in dose-finding trials with multiple comparison procedures-modeling.

Dose-finding trials play a key role in the entire drug development process to determine optimal doses for regulatory approval. We address confirmatory efficacy testing for individual dose-placebo comparisons in the context of a dose-finding trial designed with multiple comparison procedures-modeling (MCP-Mod). An extension of the MCP-Mod, called closed MCP-Mod, has been proposed to carry out the MCP-Mod in conjunction with pairwise dose-placebo comparisons; however, an issue associated with the misspecification of candidate dose-response models remains. We consider another way to combine the MCP-Mod and the individual dose-placebo comparisons using serial gatekeeping procedures with fixed sequence, Holm, Hochberg, and step-down Dunnett procedure. The method controls the family-wise error rate in the strong sense and is simple enough to be implemented by existing software. Simulation studies suggested that the serial gatekeeping procedure was comparable with the closed MCP-Mod in terms of statistical power to detect the efficacy of at least one dose, and both methods were capable of pursuing the efficacy claim rather than just establishing the dose-response signal with less than a 20% increase in sample size when assuming monotonic dose-response shapes. The serial gatekeeping procedure would have advantages in the simplicity of implementation and ease of interpretation. The dose-finding trials aiming to declare the dose-response signal, as well as the efficacy of individual doses, would be worth considering as an option to accelerate the drug development program in certain situations.

Factors Affecting Doses of Roxadustat Versus Darbepoetin Alfa for Anemia in Nondialysis Patients.

INTRODUCTION: Roxadustat is an oral hypoxia-inducible factor prolyl hydroxylase inhibitor for treating anemia of chronic kidney disease (CKD). This post hoc analysis of a Japanese, open-label, partially randomized, phase 3 study in nondialysis-dependent (NDD) CKD patients treated with traditional erythropoiesis-stimulating agents (ESAs) evaluated dosing trends of roxadustat and darbepoetin alfa (DA) required to maintain target hemoglobin concentrations in patients with risk factors associated with ESA hyporesponsiveness. METHODS: Patients enrolled in the 1517-CL-0310 study (NCT02988973) that demonstrated noninferiority of roxadustat to DA for change in average hemoglobin levels of week 18-24 from baseline who had used human recombinant erythropoietin or DA before conversion and who were randomized to either roxadustat or DA were included. The endpoints were the average allocated dose of roxadustat and DA per administration in the last 6 weeks (AAD/6W), assessed by subgroups known to be associated with ESA hyporesponsiveness. The analysis of variance was performed by the treatment group to test the influence of subgroup factors on the AAD/6W of study drug. The ratios between the mean AAD/6W in each subgroup category and the within-arm mean AAD/6W were calculated. RESULTS: Two hundred and sixty-two patients were randomized to either the roxadustat or DA comparative group and received treatment (roxadustat, n = 131; DA, n = 131). Higher mean (standard deviation) doses of both roxadustat (63.15 [24.84] mg) and DA (47.33 [29.79] µg) were required in the highest ESA resistance index (≥6.8) quartile (p = 0.003 and p < 0.001, respectively). Patients with adequate iron repletion had the lowest doses for both roxadustat (45.54 [18.01] mg) and DA (28.13 [20.98] µg). High-sensitivity C-reactive protein ≥28.57 nmol/L and the estimated glomerular filtration rate <15 mL/min/1.73 m2 were associated with requiring higher DA but not roxadustat doses. DISCUSSION/CONCLUSION: The roxadustat dose required to maintain target hemoglobin in NDD patients in Japan with anemia of CKD relative to DA dose may not be impacted by low-grade inflammation. Roxadustat may be beneficial for ESA-hyporesponsive NDD CKD patients.

Phase 3, Randomized, Double-Blind, Active-Comparator (Darbepoetin Alfa) Study of Oral Roxadustat in CKD Patients with Anemia on Hemodialysis in Japan.

BACKGROUND: Roxadustat is an oral hypoxia-inducible factor prolyl hydroxylase inhibitor approved in China for dialysis-dependent CKD anemia. METHODS: This phase 3, 24-week, double-blind, double-dummy study evaluated roxadustat's noninferiority to darbepoetin alfa for hemodialysis-dependent CKD anemia. We randomly assigned Japanese patients to oral roxadustat three times weekly or to darbepoetin alfa injections once weekly, titrating doses to maintain hemoglobin between 10-12 g/dl. The primary end point was change of average hemoglobin from baseline to weeks 18-24 (∆Hb18-24). Secondary end points were average hemoglobin and proportion of patients with hemoglobin between 10-12 g/dl (maintenance rate) at weeks 18-24, and iron parameters. Safety assessments included treatment-emergent adverse events and adjudicated ophthalmologic findings. RESULTS: We randomly assigned 303 patients to roxadustat (n=151) or darbepoetin alfa (n=152). The difference between roxadustat and darbepoetin alfa in ∆Hb18-24 was -0.02 g/dl (95% confidence interval, -0.18 to 0.15), confirming roxadustat's noninferiority to darbepoetin alfa. Average hemoglobin at weeks 18-24 with roxadustat was 10.99 g/dl (95% confidence interval: 10.88 to 11.10), confirming its efficacy. Among patients with one or more hemoglobin value during weeks 18-24, the maintenance rate was 95.2% with roxadustat and 91.3% with darbepoetin alfa. Serum iron, ferritin, and transferrin saturation remained clinically stable with roxadustat; transferrin and total iron binding capacity increased through week 4 before stabilizing. Common treatment-emergent adverse events were nasopharyngitis, shunt stenosis, diarrhea, contusion, and vomiting. The proportion of patients with new or worsening retinal hemorrhage was 32.4% with roxadustat and 36.6% with darbepoetin alfa. We observed no clinically meaningful changes in retinal thickness groups. CONCLUSIONS: Roxadustat maintained hemoglobin within 10-12 g/dl in patients on hemodialysis and was noninferior to darbepoetin alfa. Treatment-emergent adverse events were consistent with previous reports. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: A Study of Intermittent Oral Dosing of ASP1517 in Hemodialysis Chronic Kidney Disease Patients with Anemia, NCT02952092 (ClinicalTrials.gov).

A note on the bias of standard errors when orthogonality of mean and variance parameters is not satisfied in the mixed model for repeated measures analysis.

The mixed effect models for repeated measures (MMRM) analysis is sometimes used as a primary analysis in longitudinal randomized clinical trials. The SE for the treatment effect in the MMRM analysis is usually estimated by assuming the orthogonality of the fixed effect and variance-covariance parameters, which is the orthogonality property of a multivariate normal distribution, because of default settings of most standard statistical software. However, this property might be lost when analysis models are misspecified and/or data include missing values with the mechanism of being missing at random. In this study, we investigated the effect of the assumption of the orthogonality property on the estimation of the SE for the MMRM analysis. From simulation and case studies, it was shown that the SE with the assumption of orthogonality property had nonnegligible bias, especially when the analysis models assuming heteroscedasticity between treatment groups were applied. We also introduce the SAS code for the MMRM analysis without assuming the orthogonality property. Assuming the orthogonality property in the MMRM analysis would lead to invalid statistical inference, and it is necessary to be careful when applying the MMRM analysis with most standard software.

Borrowing external information to improve Bayesian confidence propagation neural network.

PURPOSE: A Bayesian confidence propagation neural network (BCPNN) is a signal detection method used by the World Health Organization Uppsala Monitoring Centre to analyze spontaneous reporting system databases. We modify the BCPNN to increase its sensitivity for detecting potential adverse drug reactions (ADRs). METHOD: In a BCPNN, the information component (IC) is defined as an index of disproportionality between the observed and expected number of reported drugs and events. Our proposed method adjusts the IC value by borrowing information about events that have occurred in drugs defined as similar to the target drug. We compare the performance of our method with that of a traditional BCPNN through a simulation study. RESULTS: The false positive rate of the proposed method was lower than that of the traditional BCPNN method and close to the nominal value, 0.025, around the true difference in ICs between the target drug and similar drugs equal to 0. The sensitivity of the proposed method was much higher than that of the traditional BCPNN method in case in which the difference in ICs between the target drug and similar drugs ranges from 0 to 2. When applied to a database managed by Japanese regulatory authority, the proposed method could detect known ADRs earlier than the traditional method. CONCLUSIONS: The proposed method is a novel criterion for early detection of signals if similar drugs have the same tendencies. The proposed BCPNN tends to have higher sensitivity when the true difference is greater than 0.

Multiple imputation for longitudinal data in the presence of heteroscedasticity between treatment groups.

Multiple imputation is a promising approach for handling of missing data. One uncertainty in applications of the multiple imputation to randomized controlled trials with longitudinal data is whether the imputation should be carried out across all subjects simultaneously or by treatment group separately, which leads to two different strategies for building imputation procedures and/or models. Indeed, it has not been sufficiently addressed and well-documented how the two imputation strategies work in the analysis of the longitudinal data. We consider situations in the presence of heteroscedasticity between treatment groups and conducted extensive simulation studies to examine how the choice of imputation strategy had impacts on the estimation of treatment effects under an assumption of missing at random mechanism. The choice of analysis model was also assessed. The simulation studies suggested that in the presence of heteroscedasticity, the separate imputation by treatment group was robust enough to provide unbiased and precise estimation of the treatment effects; in contrast, the simultaneous imputation, which is frequently used in applications, led to serious biases and poor coverage probabilities of 95% confidence interval for the treatment effects. The heteroscedasticity should be dealt with in more careful manners for the longitudinal data analysis, and if it could be the case in hand, we recommend using the separate imputation by treatment group, as well as applying unequal variance analysis methods for complete data with imputed values. The methods were illustrated with data from two real examples of pediatric research and mental health research.

Communities of mycorrhizal fungi in different trophic types of Asiatic Pyrola japonica sensu lato (Ericaceae).

Mixotrophic plants obtain carbon by their own photosynthetic activity and from their root-associated mycorrhizal fungi. Mixotrophy is deemed a pre-adaptation for evolution of mycoheterotrophic nutrition, where plants fully depend on fungi and lose their photosynthetic activity. The aim of this study was to clarify mycorrhizal dependency and heterotrophy level in various phenotypes of mixotrophic Pyrola japonica (Ericaceae), encompassing green individuals, rare achlorophyllous variants (albinos) and a form with minute leaves, P. japonica f. subaphylla. These three phenotypes were collected in two Japanese forests. Phylogenetic analysis of both plants and mycorrhizal fungi was conducted based on DNA barcoding. Enrichment in 13C among organs (leaves, stems and roots) of the phenotypes with reference plants and fungal fruitbodies were compared by measuring stable carbon isotopic ratio. All plants were placed in the same clade, with f. subaphylla as a separate subclade. Leaf 13C abundances of albinos were congruent with a fully mycoheterotrophic nutrition, suggesting that green P. japonica leaves are 36.8% heterotrophic, while rhizomes are 74.0% heterotrophic. There were no significant differences in δ13C values among organs in both albino P. japonica and P. japonica f. subaphylla, suggesting full and high mycoheterotrophic nutrition, respectively. Among 55 molecular operational taxonomic units (OTUs) detected as symbionts, the genus Russula was the most abundant in each phenotype and its dominance was significantly higher in albino P. japonica and P. japonica f. subaphylla. Russula spp. detected in P. japonica f. subaphylla showed higher dissimilarity with other phenotypes. These results suggest that P. japonica sensu lato is prone to evolve mycoheterotrophic variants, in a process that changes its mycorrhizal preferences, especially towards the genus Russula for which this species has a marked preference.

S-Allyl-L-cysteine sulfoxide, a garlic odor precursor, suppresses elevation in blood ethanol concentration by accelerating ethanol metabolism and preventing ethanol absorption from gut.

Alcoholic beverages are enjoyed together with meals worldwide, but their excessive intake is associated with an increased risk of various diseases. We investigated whether S-allyl-L-cysteine sulfoxide (ACSO), a sulfuric odor precursor of garlic, suppresses elevation in plasma ethanol concentration by accelerating ethanol metabolism and preventing ethanol absorption from the gut in rats. ACSO and garlic extract with a high ACSO content (Garlic-H) suppressed elevation in concentrations of ethanol and acetaldehyde in plasma and promoted the activities of alcohol dehydrogenase and aldehyde dehydrogenase. However, ACSO and Garlic-H did not affect plasma acetate so much. Furthermore, we examined the change in plasma ethanol concentration by injecting ACSO or Garlic-H into the ligated stomach or jejunum together with ethanol solution. ACSO and Garlic-H suppressed the absorption of ethanol from the stomach and jejunum, but suppression in the jejunum was less than in the stomach. In conclusion, ACSO inhibits ethanol absorption and accelerates ethanol metabolism.

A comparison of multiple imputation methods for incomplete longitudinal binary data.

Longitudinal binary data are commonly encountered in clinical trials. Multiple imputation is an approach for getting a valid estimation of treatment effects under an assumption of missing at random mechanism. Although there are a variety of multiple imputation methods for the longitudinal binary data, a limited number of researches have reported on relative performances of the methods. Moreover, when focusing on the treatment effect throughout a period that has often been used in clinical evaluations of specific disease areas, no definite investigations comparing the methods have been available. We conducted an extensive simulation study to examine comparative performances of six multiple imputation methods available in the SAS MI procedure for longitudinal binary data, where two endpoints of responder rates at a specified time point and throughout a period were assessed. The simulation study suggested that results from naive approaches of a single imputation with non-responders and a complete case analysis could be very sensitive against missing data. The multiple imputation methods using a monotone method and a full conditional specification with a logistic regression imputation model were recommended for obtaining unbiased and robust estimations of the treatment effect. The methods were illustrated with data from a mental health research.

A random effects meta-analysis model with Box-Cox transformation.

BACKGROUND: In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and a wrongly symmetric prediction interval. METHODS: We focus on problems caused by an inappropriate normality assumption of the random effects distribution, and propose a novel random effects meta-analysis model where a Box-Cox transformation is applied to the observed treatment effect estimates. The proposed model aims to normalise an overall distribution of observed treatment effect estimates, which is sum of the within-study sampling distributions and the random effects distribution. When sampling distributions are approximately normal, non-normality in the overall distribution will be mainly due to the random effects distribution, especially when the between-study variation is large relative to the within-study variation. The Box-Cox transformation addresses this flexibly according to the observed departure from normality. We use a Bayesian approach for estimating parameters in the proposed model, and suggest summarising the meta-analysis results by an overall median, an interquartile range and a prediction interval. The model can be applied for any kind of variables once the treatment effect estimate is defined from the variable. RESULTS: A simulation study suggested that when the overall distribution of treatment effect estimates are skewed, the overall mean and conventional I 2 from the normal random effects model could be inappropriate summaries, and the proposed model helped reduce this issue. We illustrated the proposed model using two examples, which revealed some important differences on summary results, heterogeneity measures and prediction intervals from the normal random effects model. CONCLUSIONS: The random effects meta-analysis with the Box-Cox transformation may be an important tool for examining robustness of traditional meta-analysis results against skewness on the observed treatment effect estimates. Further critical evaluation of the method is needed.