LFK index does not reliably detect small-study effects in meta-analysis: A simulation study.

The LFK index has been promoted as an improved method to detect bias in meta-analysis. Putatively, its performance does not depend on the number of studies in the meta-analysis. We conducted a simulation study, comparing the LFK index test to three standard tests for funnel plot asymmetry in settings with smaller or larger group sample sizes. In general, false positive rates of the LFK index test markedly depended on the number and size of studies as well as the between-study heterogeneity with values between 0% and almost 30%. Egger's test adhered well to the pre-specified significance level of 5% under homogeneity, but was too liberal (smaller groups) or conservative (larger groups) under heterogeneity. The rank test was too conservative for most simulation scenarios. The Thompson-Sharp test was too conservative under homogeneity, but adhered well to the significance level in case of heterogeneity. The true positive rate of the LFK index test was only larger compared with classic tests if the false positive rate was inflated. The power of classic tests was similar or larger than the LFK index test if the false positive rate of the LFK index test was used as significance level for the classic tests. Under ideal conditions, the false positive rate of the LFK index test markedly and unpredictably depends on the number and sample size of studies as well as the extent of between-study heterogeneity. The LFK index test in its current implementation should not be used to assess funnel plot asymmetry in meta-analysis.

Efficacy and safety of corticosteroids and ACTH in epileptic syndromes beyond Infantile Epileptic Spasms Syndrome (IESS): A systematic review and meta-analysis.

We conducted a systematic review investigating the efficacy and tolerability of adrenocorticotropic hormone (ACTH) and corticosteroids in children with epilepsies other than infantile epileptic spasm syndrome (IESS) that are resistant to anti-seizure medication (ASM). We included retrospective and prospective studies reporting on more than five patients and with clear case definitions and descriptions of treatment and outcome measures. We searched multiple databases and registries, and we assessed the risk of bias in the selected studies using a questionnaire based on published templates. Results were summarized with meta-analyses that pooled logit-transformed proportions or rates. Subgroup analyses and univariable and multivariable meta-regressions were performed to examine the influence of covariates. We included 38 studies (2 controlled and 5 uncontrolled prospective; 31 retrospective) involving 1152 patients. Meta-analysis of aggregate data for the primary outcomes of seizure response and reduction of electroencephalography (EEG) spikes at the end of treatment yielded pooled proportions (PPs) of 0.60 (95% confidence interval [CI] 0.52-0.67) and 0.56 (95% CI 0.43-0.68). The relapse rate was high (PP 0.33, 95% CI 0.27-0.40). Group analyses and meta-regression showed a small benefit of ACTH and no difference between all other corticosteroids, a slightly better effect in electric status epilepticus in slow sleep (ESES) and a weaker effect in patients with cognitive impairment and "symptomatic" etiology. Obesity and Cushing's syndrome were the most common adverse effects, occurring more frequently in trials addressing continuous ACTH (PP 0.73, 95% CI 0.48-0.89) or corticosteroids (PP 0.72, 95% CI 0.54-0.85) than intermittent intravenous or oral corticosteroid administration (PP 0.05, 95% CI 0.02-0.10). The validity of these results is limited by the high risk of bias in most included studies and large heterogeneity among study results. This report was registered under International Prospective Register of Systematic Reviews (PROSPERO) number CRD42022313846. We received no financial support.

When does the placebo effect have an impact on network meta-analysis results?

The placebo effect is the 'effect of the simulation of treatment that occurs due to a participant's belief or expectation that a treatment is effective'. Although the effect might be of little importance for some conditions, it can have a great role in others, mostly when the evaluated symptoms are subjective. Several characteristics that include informed consent, number of arms in a study, the occurrence of adverse events and quality of blinding may influence response to placebo and possibly bias the results of randomised controlled trials. Such a bias is inherited in systematic reviews of evidence and their quantitative components, pairwise meta-analysis (when two treatments are compared) and network meta-analysis (when more than two treatments are compared). In this paper, we aim to provide red flags as to when a placebo effect is likely to bias pairwise and network meta-analysis treatment effects. The classic paradigm has been that placebo-controlled randomised trials are focused on estimating the treatment effect. However, the magnitude of placebo effect itself may also in some instances be of interest and has also lately received attention. We use component network meta-analysis to estimate placebo effects. We apply these methods to a published network meta-analysis, examining the relative effectiveness of four psychotherapies and four control treatments for depression in 123 studies.

Pharmacologic interventions for the therapy of postanesthetic shivering in adults: a systematic review and network meta-analysis.

INTRODUCTION: Shivering is a common side effect after general anesthesia. Risk factors are hypothermia, young age and postoperative pain. Severe complications of shivering are rare but can occur due to increased oxygen consumption. Previous systematic reviews are outdated and have summarized the evidence on the topic using only pairwise comparisons. The objective of this manuscript was a quantitative synthesis of evidence on pharmacological interventions to treat postanesthetic shivering. EVIDENCE ACQUSITION: Systematic review and frequentist network meta-analysis using the R package netmeta. Endpoints were the risk ratio (RR) of persistent shivering at one, five and 10 minutes after treatment with saline/placebo as the comparator. Data were retrieved from Medline, Embase, Central and Web of Science up to January 2022. Eligibility criteria were: randomized, controlled, and blinded trials comparing pharmacological interventions to treat shivering after general anesthesia. Studies on shivering during or after any type of regional anesthesia were excluded as well as sedated patients after cardiac surgery. EVIDENCE SYNTHESIS: Thirty-two trials were eligible for data synthesis, including 28 pharmacological interventions. The largest network included 1431 patients. The network geometry was two-centered with most comparisons linked to saline/placebo or pethidine. The best interventions were after one minute: doxapram 2 mg/kg, tramadol 2 mg/kg and nefopam 10 mg, after 5 minutes: tramadol 2 mg/kg, nefopam 10 mg and clonidine 150 µg and after 10 minutes: nefopam 10 mg, methylphenidate 20 mg and tramadol 1 mg/kg, all reaching statistical significance. Pethidine 25 mg and clonidine 75 µg also performed well and with statistical significance in all networks. CONCLUSIONS: Nefopam, tramadol, pethidine and clonidine are the most effective treatments to stop postanesthetic shivering. The efficacy of doxapram is uncertain since different doses showed contradictory effects and the evidence for methylphenidate is based on a single comparison in only one network. Furthermore, both lack data on side effects. Further studies are needed to clarify the efficacy of dexmedetomidine to treat postanesthetic shivering.

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study.

BACKGROUND: Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to "reconnect" a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA. METHODS: We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities. RESULTS: CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist. CONCLUSIONS: CNMA methods are feasible for connected networks but questionable for disconnected networks.

Equianalgesic potency ratios of opioids used in patient-controlled analgesia: A network meta-analysis.

OBJECTIVE: To determine equianalgesic potency ratios for opioids with an -evidence-based approach without the use of pre-existing potency tables. DESIGN: Frequentist network meta-analysis (NMA) of randomized controlled trials (RCTs) comparing opioids in patient-controlled analgesia (PCA). SETTING: A systematic review. DATA SOURCES: A systematic search of MEDLINE, EMBASE, the Cochrane Library (CENTRAL), and Web of Science identified relevant RCTs from start of recording to 2019. ELIGIBILITY CRITERIA: RCTs comparing opioids via intravenous PCA in acute pain, with comparable resulting pain scores and identical treatment with coanalgesics at study level. The quality of studies was assessed using the Cochrane risk of bias tool with six items. RESULTS: 52 RCTs were identified with data for 16 opioids. Primary endpoint was the inverted ratio of means of the total consumption administered via PCA, which resembles the analgesic potency. The calculated analgesic potencies were sufentanil 423 [95 percent CI 334.99; 532.96], fentanyl 58 [48.22; 68.60], buprenorphine 37 [26.66; 50.81], remifentanil 13 [9.37; 19.13], alfentanil 7 [4.02; 11.01], hydromorphone 6 [4.96; 8.43], oxymorphone 6 [4.46; 8.84], butorphanol 4.5 [3.05; 6.73], diamorphine 2.2 [1.16; 4.10], morphine 1, oxycodone 0.9 [0.65; 1.34], piritramide 0.9 [0.55; 1.56], nalbuphine 0.7 [0.54; 0.95], pethidine 0.12 [0.10; 0.15], meptazinol 0.08 [0.03; 0.20], and tramadol 0.08 [0.07; 0.10]. CONCLUSIONS: The results in part contradict the values from the literature, which have been criticized for their imprecision. From clinical experience however, our findings seem very plausible. Short-acting opioids are less potent compared to longer acting drugs, eg, morphine, probably due to shorter intervals for -readministration.

Psychological interventions for inflammatory bowel disease: a systematic review and component network meta-analysis protocol.

INTRODUCTION: Patients with inflammatory bowel diseases (IBD) often report psychological problems, unemployment, disability, sick leave and compromised quality of life. The effect of psychological interventions on health-related outcomes in IBD is controversial as previous reviews faced the obstacle of high heterogeneity among provided multimodular interventions. The heterogeneity can be addressed with network meta-analysis (NMA) and (multi)component NMA (CNMA). We aim to investigate whether psychological interventions can improve quality of life, clinical and social outcomes in IBD using NMA and CNMA. This is the study protocol. METHODS AND ANALYSIS: We will consider randomised, quasi-randomised and non-randomised controlled trials, including cluster randomised and cross-over trials with 2 months of minimum follow-up. The conditions to be studied comprise Crohn's disease and ulcerative colitis in children, adolescents and adults. We will include any psychological intervention aiming to change the health status of the study participant.We will search Medline, Embase, Web of Science, CENTRAL, LILACS, Psyndex, PsycINFO, Google Scholar and trial registries from inception (the search will be updated before the review completion). Two authors will independently screen all references based on titles and abstracts. For data extraction, standard forms are developed and tested before extraction. All information will be assessed independently by at least two reviewers, and disagreements solved by consensus discussion or a third rater if necessary.The data synthesis will include a pairwise meta-analysis supported by meta-regression. We will conduct NMA (all treatments will constitute single nodes of the network) and CNMA (we will define all treatments as sums of core components, eg, cognitive +behaviour, or cognitive +behaviour + relaxation, and additionally consider interactions) using the R Package netmeta. ETHICS AND DISSEMINATION: No ethical approval is required. Reports will include the final report to the funder, conference presentation, peer-reviewed publication and a patient report. PROSPERO REGISTRATION NUMBER: CRD42021250446.

Assessing severe acute respiratory syndrome coronavirus 2 infectivity by reverse-transcription polymerase chain reaction: A systematic review and meta-analysis.

The cornerstone of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is reverse-transcription polymerase chain reaction (RT-PCR) of viral RNA. As a surrogate assay SARS-CoV-2 RNA detection does not necessarily imply infectivity. Only virus isolation in permissive cell culture systems can indicate infectivity. Here, we review the evidence on RT-PCR performance in detecting infectious SARS-CoV-2. We searched for any studies that used RT-PCR and cell culture to determine infectious SARS-CoV-2 in respiratory samples. We assessed (i) diagnostic accuracy of RT-PCR compared to cell culture as reference test, (ii) performed meta-analysis of positive predictive values (PPV) and (iii) determined the virus isolation probabilities depending on cycle threshold (Ct) or log10 genome copies/ml using logistic regression. We included 55 studies. There is substantial statistical and clinical heterogeneity. Seven studies were included for diagnostic accuracy. Sensitivity ranged from 90% to 99% and specificity from 29% to 92%. In meta-analysis, the PPVs varied across subgroups with different sampling times after symptom onset, with 1% (95% confidence interval [CI], 0%-7%) in sampling beyond 10 days and 27% (CI, 19%-36%) to 46% (CI, 33%-60%) in subgroups that also included earlier samples. Estimates of virus isolation probability varied between 6% (CI, 0%-100%) and 50% (CI, 0%-100%) at a Ct value of 30 and between 0% (CI, 0%-22%) and 63% (CI, 0%-100%) at 5 log10 genome copies/ml. Evidence on RT-PCR performance in detecting infectious SARS-CoV-2 in respiratory samples was limited. Major limitations were heterogeneity and poor reporting. RT-PCR and cell culture protocols need further standardisation.

Bayesian models for aggregate and individual patient data component network meta-analysis.

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics.

Network meta-analysis and random walks.

Network meta-analysis (NMA) is a central tool for evidence synthesis in clinical research. The results of an NMA depend critically on the quality of evidence being pooled. In assessing the validity of an NMA, it is therefore important to know the proportion contributions of each direct treatment comparison to each network treatment effect. The construction of proportion contributions is based on the observation that each row of the hat matrix represents a so-called "evidence flow network" for each treatment comparison. However, the existing algorithm used to calculate these values is associated with ambiguity according to the selection of paths. In this article, we present a novel analogy between NMA and random walks. We use this analogy to derive closed-form expressions for the proportion contributions. A random walk on a graph is a stochastic process that describes a succession of random "hops" between vertices which are connected by an edge. The weight of an edge relates to the probability that the walker moves along that edge. We use the graph representation of NMA to construct the transition matrix for a random walk on the network of evidence. We show that the net number of times a walker crosses each edge of the network is related to the evidence flow network. By then defining a random walk on the directed evidence flow network, we derive analytically the matrix of proportion contributions. The random-walk approach has none of the associated ambiguity of the existing algorithm.

Answering complex hierarchy questions in network meta-analysis.

BACKGROUND: Network meta-analysis estimates all relative effects between competing treatments and can produce a treatment hierarchy from the most to the least desirable option according to a health outcome. While about half of the published network meta-analyses present such a hierarchy, it is rarely the case that it is related to a clinically relevant decision question. METHODS: We first define treatment hierarchy and treatment ranking in a network meta-analysis and suggest a simulation method to estimate the probability of each possible hierarchy to occur. We then propose a stepwise approach to express clinically relevant decision questions as hierarchy questions and quantify the uncertainty of the criteria that constitute them. The steps of the approach are summarized as follows: a) a question of clinical relevance is defined, b) the hierarchies that satisfy the defined question are collected and c) the frequencies of the respective hierarchies are added; the resulted sum expresses the certainty of the defined set of criteria to hold. We then show how the frequencies of all possible hierarchies relate to common ranking metrics. RESULTS: We exemplify the method and its implementation using two networks. The first is a network of four treatments for chronic obstructive pulmonary disease where the most probable hierarchy has a frequency of 28%. The second is a network of 18 antidepressants, among which Vortioxetine, Bupropion and Escitalopram occupy the first three ranks with frequency 19%. CONCLUSIONS: The developed method offers a generalised approach of producing treatment hierarchies in network meta-analysis, which moves towards attaching treatment ranking to a clear decision question, relevant to all or a subset of competing treatments.

Diagnostic test accuracy network meta-analysis methods: A scoping review and empirical assessment.

OBJECTIVES: To (a) identify methodological and application papers reporting a model developed specifically for diagnostic test accuracy network meta-analysis (DTA-NMA) or a hierarchical meta-regression method for comparing at least three index tests; (b) review and summarize the characteristics of the methods and the application papers; and (c) compare DTA-NMA and hierarchical meta-regression methods empirically. STUDY DESIGN AND SETTING: We performed a scoping review and searched major databases until March 3rd, 2021. We assessed the characteristics of the identified methods, conducted a descriptive analysis of characteristics of the application articles, and applied the DTA-NMA and meta-regression methods to the available data. RESULTS: We included 49 articles (plus one companion report), of which nine were methodological (describing 11 DTA-NMA methods) and 40 were application papers (data available for 32 DTA-NMAs). Our results showed a steep increase in recent years in DTA-NMA publications. DTA-NMA models may lead to different results. Although sensitivity estimates were comparable between meta-regression and DTA-NMA models, specificity estimates were higher in meta-regression. CONCLUSION: The choice of a DTA-NMA model will depend on the available data, including the use of different thresholds for test positivity, different study designs, and software familiarity. Selection between the methods may impact on the NMA results, especially for specificity.

Diagnostic accuracy of cerebrospinal fluid biomarkers for the differential diagnosis of sporadic Creutzfeldt-Jakob disease: a (network) meta-analysis.

BACKGROUND: There are no systematic reviews of cerebrospinal fluid and blood biomarkers for sporadic Creutzfeldt-Jakob disease (sCJD) in specialized care settings that compare diagnostic accuracies in a network meta-analysis (NMA). METHODS: We searched Medline, Embase, and Cochrane Library for diagnostic studies of sCJD biomarkers. Studies had to use established diagnostic criteria for sCJD and for diseases in the non-CJD groups, which had to represent a consecutive population of patients suspected as a CJD case, as reference standard. Risk of bias was assessed with QUADAS-2. We conducted individual biomarker meta-analyses with generalized bivariate models. To investigate heterogeneity, we performed subgroup analyses based on QUADAS-2 quality and clinical criteria. For the NMA, we applied a Bayesian beta-binomial ANOVA model. The study protocol was registered at PROSPERO (CRD42019118830). RESULTS: Of 2976 publications screened, we included 16 studies, which investigated 14-3-3ß (n = 13), 14-3-3γ (n = 3), neurofilament light chain (NfL, n = 1), neuron-specific enolase (n = 1), p-tau181/t-tau ratio (n = 2), RT-QuIC (n = 7), S100B (n = 3), t-tau (n = 12), and t-tau/Aß42 ratio (n = 1). Excluded diagnostic studies had strong limitations in study design. In the NMA, RT-QuIC (0.91; 95% CI [0.83, 0.95]) and NfL (0.93 [0.78, 0.99]) were the most sensitive biomarkers for the diagnosis of definite, probable, and possible sCJD cases. RT-QuIC was the most specific biomarker (0.97 [0.89, 1.00]). Heterogeneity in accuracy estimates was high between studies. CONCLUSIONS: We identified RT-QuIC as the most accurate biomarker, partially confirming currently applied diagnostic criteria. The shortcomings identified in many diagnostic studies for sCJD biomarkers need to be addressed in future studies.

Evaluation of circulating microRNAs as non-invasive biomarkers in the diagnosis of ovarian cancer: a case-control study.

PURPOSE: Ovarian cancer is the seventh most frequent form of malignant diseases in women worldwide and over 150,000 women die from it every year. More than 70 percent of all ovarian cancer patients are diagnosed at a late-stage disease with poor prognosis necessitating the development of sufficient screening biomarkers. MicroRNAs displayed promising potential as early diagnostics in various malignant diseases including ovarian cancer. The presented study aimed at identifying single microRNAs and microRNA combinations detecting ovarian cancer in vitro and in vivo. METHODS: Intracellular, extracellular and urinary microRNA expression levels of twelve microRNAs (let-7a, let-7d, miR-10a, miR-15a, miR-15b, miR-19b, miR-20a, miR-21, miR-100, miR-125b, miR-155, miR-222) were quantified performing quantitative real-time-PCR. Therefore, the three ovarian cancer cell lines SK-OV-3, OAW-42, EFO-27 as well as urine samples of ovarian cancer patients and healthy controls were analyzed. RESULTS: MiR-15a, miR-20a and miR-222 showed expression level alterations extracellularly, whereas miR-125b did intracellularly across the analyzed cell lines. MicroRNA expression alterations in single cell lines suggest subtype specificity in both compartments. Hypoxia and acidosis showed scarce effects on single miRNA expression levels only. Furthermore, we were able to demonstrate the feasibility to clearly detect the 12 miRNAs in urine samples. In urine, miR-15a was upregulated whereas let-7a was down-regulated in ovarian cancer patients. CONCLUSION: Intracellular, extracellular and urinary microRNA expression alterations emphasize their great potential as biomarkers in liquid biopsies. Especially, miR-15a and let-7a qualify for possible circulating biomarkers in liquid biopsies of ovarian cancer patients.

Meta-Analysis of Proportions.

The meta-analysis of single proportions has become a popular application over the last two decades. Especially, systematic reviews of prevalence studies are conducted in various fields of science, including medicine, ecology, psychology, or social sciences. In this chapter, we illustrate meta-analysis methods to pool single proportions and to compare proportions from two groups. We introduce classic approaches based on the inverse variance method as well as generalized linear mixed models taking the binary structure of the data into account. The most common transformations of proportions and their back-transformations are described both for individual studies and in the meta-analysis setting.

Challenges in Comparative Meta-Analysis of the Accuracy of Multiple Diagnostic Tests.

The rapid increase in diagnostic and screening techniques has urged the need to choose among multiple diagnostic tests. For the majority of diseases, there is more than a single test available, and studies usually compare a subset of these tests. In such cases, a separate meta-analysis of each test cannot provide a reliable answer on the relative accuracy of the multiple available tests. Extensions of standard (hierarchical) meta-analysis to network meta-analysis (NMA) models for the comparison of at least three diagnostic tests have been the subject of methodological research in recent years. NMA can be used to jointly analyze the totality of evidence in order to provide estimates of relative accuracy (sensitivity and specificity ), to compare tests that have not been compared head-to-head, and to obtain a ranking of all competing tests in order to further facilitate the decision-making process.In this chapter, we illustrate current methodology for meta-analysis of multiple test comparisons, introduce NMA methods of diagnostic tests as an extension to the standard meta-analysis of diagnostic test accuracy (DTA) studies, and present existing approaches to rank tests according to their accuracy, specificity , and sensitivity . We also describe the basic concepts, underlying assumptions, and challenges in NMA of multiple diagnostic tests.

Anticarcinogenic Effects of Odorant Substances Citral, Citrathal R and Cyclovertal on Breast Cancer in vitro.

PURPOSE: In 2020, breast cancer still represents the most common type of cancer in women worldwide. Depending on the specific molecular subtype, clinical breast cancer management comprises surgery, radiotherapy, chemotherapy and targeted therapy. Furthermore, there are some therapeutic approaches from the field of complementary and alternative medicine. Current research focuses on the elucidation of new therapeutic targets for treatment development. Odorant substances affect apoptosis, proliferation and cell cycle in healthy and cancerous cells. Exact signalling pathways involved are not entirely clear. The present study aims to analyse their therapeutic potential in breast cancer. METHODS: This study focuses on the effect of commonly used odorant substances (citral, citrathal R, cyclovertal, para-cymol, hexylacetat, herbavert, dihydromyrcerol and limonen) on the breast cancer cell lines MDA-MB-231, T47-D and BT474. Methodologically, this study applied cell culturing, MTT assay for detection of IC50 of the odorant substance, RNA purification followed by qRT-PCR, protein isolation and Western Blot, as well as immunocytochemistry. Further, this study investigates the role of transient receptor potential channel V1 (TRPV1), involved in the mechanisms of action for some odorant substances. Therefore, capsazepine, a TRPV1 antagonist, was used. RESULTS: The odorant substances citral, citrathal R and cyclovertal have significant pro-apoptotic (p < 0.001), anti-proliferative (p < 0.001) and cell cycle-arresting effects measurable in RNA expression as well as in protein levels and immunocytochemical staining. The combination of citral and capsazepine no longer showed significant pro-apoptotic, antiproliferative, and cell cycle inhibitory effects compared to the compounds alone. This indicates that TRPV1 is necessary for the signal transduction of citral. CONCLUSION: This present study reveals three odorant substances with effects on cell viability, indicating their potential use in breast cancer therapy.

Prevalence of evidence of inconsistency and its association with network structural characteristics in 201 published networks of interventions.

BACKGROUND: Network meta-analysis (NMA) has attracted growing interest in evidence-based medicine. Consistency between different sources of evidence is fundamental to the reliability of the NMA results. The purpose of the present study was to estimate the prevalence of evidence of inconsistency and describe its association with different NMA characteristics. METHODS: We updated our collection of NMAs with articles published up to July 2018. We included networks with randomised clinical trials, at least four treatment nodes, at least one closed loop, a dichotomous primary outcome, and available arm-level data. We assessed consistency using the design-by-treatment interaction (DBT) model and testing all the inconsistency parameters globally through the Wald-type chi-squared test statistic. We estimated the prevalence of evidence of inconsistency and its association with different network characteristics (e.g., number of studies, interventions, intervention comparisons, loops). We evaluated the influence of the network characteristics on the DBT p-value via a multivariable regression analysis and the estimated Pearson correlation coefficients. We also evaluated heterogeneity in NMA (consistency) and DBT (inconsistency) random-effects models. RESULTS: We included 201 published NMAs. The p-value of the design-by-treatment interaction (DBT) model was lower than 0.05 in 14% of the networks and lower than 0.10 in 20% of the networks. Networks including many studies and comparing few interventions were more likely to have small DBT p-values (less than 0.10), which is probably because they yielded more precise estimates and power to detect differences between designs was higher. In the presence of inconsistency (DBT p-value lower than 0.10), the consistency model displayed higher heterogeneity than the DBT model. CONCLUSIONS: Our findings show that inconsistency was more frequent than what would be expected by chance, suggesting that researchers should devote more resources to exploring how to mitigate inconsistency. The results of this study highlight the need to develop strategies to detect inconsistency (because of the relatively high prevalence of evidence of inconsistency in published networks), and particularly in cases where the existing tests have low power.

Investigator initiated trials versus industry sponsored trials - translation of randomized controlled trials into clinical practice (IMPACT).

BACKGROUND: Healthcare decisions are ideally based on clinical trial results, published in study registries, as journal articles or summarized in secondary research articles. In this research project, we investigated the impact of academically and commercially sponsored clinical trials on medical practice by measuring the proportion of trials published and cited by systematic reviews and clinical guidelines. METHODS: We examined 691 multicenter, randomized controlled trials that started in 2005 or later and were completed by the end of 2016. To determine whether sponsorship/funding and place of conduct influence a trial's impact, we created four sub-cohorts of investigator initiated trials (IITs) and industry sponsored trials (ISTs): 120 IITs and 171 ISTs with German contribution compared to 200 IITs and 200 ISTs without German contribution. We balanced the groups for study phase and place of conduct. German IITs were funded by the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), or by another non-commercial research organization. All other trials were drawn from the German Clinical Trials Register or ClinicalTrials.gov. We investigated, to what extent study characteristics were associated with publication and impact using multivariable logistic regressions. RESULTS: For 80% of the 691 trials, results were published as result articles in a medical journal and/or study registry, 52% were cited by a systematic review, and 26% reached impact in a clinical guideline. Drug trials and larger trials were associated with a higher probability to be published and to have an impact than non-drug trials and smaller trials. Results of IITs were more often published as a journal article while results of ISTs were more often published in study registries. International ISTs less often gained impact by inclusion in systematic reviews or guidelines than IITs. CONCLUSION: An encouraging high proportion of the clinical trials were published, and a considerable proportion gained impact on clinical practice. However, there is still room for improvement. For publishing study results, study registries have become an alternative or complement to journal articles, especially for ISTs. IITs funded by governmental bodies in Germany reached an impact that is comparable to international IITs and ISTs.