Reduction In Statistical Power Research Articles

Comparison of Blue and Infrared Light Transmission Through Dental Tissues and Restorative Materials.

The depth of cure using blue-light photocuring units (BL) is limited by tooth structure and qualities of the restorative material through which the activating wavelength must pass. Recent developments incorporate an infrared (IR) activated upconversion (UC) fluorescence of a lining agent filled with nanocrystals of NaYF4 and doped with YB+3 and Tm+3 that emit both blue and violet light locally at the interface of the liner and restorative resin. The purpose of this study was to evaluate the BL and 975 nm infrared (IR) light power transmission through dental tissues and restorative materials. Power transmissions of the IR laser (975 nm) and a monowave blue-only light-curing unit (Bluephase 16i) through dental tissues (enamel, dentin, and enamel/dentin junction, or DEJ), eight (8) various dental resin composites, and eight (8) dental ceramics, each at four thicknesses (1, 2, 3 and 4 mm) were evaluated (n=5) using a thermopile sensor (PM10, Coherent Inc) connected to a laser power meter (Fieldmate, Coherent Inc). Power transmission values of each light source and restorative material were subjected to analysis of variance and Tukey test at a pre-set alpha of 0.05. A linear correlation (r=0.9884) between the supplied current and emitted IR power of the laser diode was found, showing no statistical power reduction with increased distances (collimated beam). For tooth tissues, the highest power transmissions for both light sources were observed using 1.0 mm enamel while the lowest values were found for 2.0 mm dentin and an association of 2.0 mm DEJ and 1.0 mm dentin. The only group where IR demonstrated significantly higher transmission when compared to BL was 1.0 mm enamel. For all resin composites and dental ceramics, increased thickness resulted in a reduction of IR power transmission (except for EverX Posterior fiber-reinforced composite and e.max HT ceramic). IR resulted in higher transmission through all resin composites, except for Tetric EvoCeram White. The highest BL transmission was observed for SDR Flow, at all thicknesses. Higher IR/BL ratios were observed for EverX Posterior, Herculite Ultra, and Lava Ultimate, while the lowest ratio was observed for Tetric EvoCeram White. Reduced translucency shades within the same material resulted in lower power ratio values, especially for BL transmission. Higher IR/BL ratios were observed for e.Max LT, VitaVM7 Base Dentin, and e.max CAD HT, while the lowest values were found for VitaVM7 Enamel and Paradigm C. IR power transmission through enamel was higher when compared to blue light, while no difference was observed for dentin. The power transmission of IR was higher than BL for resin composites, except for a high value and low chroma shade. Fiber-reinforced resin composite demonstrated the highest IR/BL power transmission ratio. A greater IR/BL ratio was observed for lower translucency ceramics when compared to high translucency.

Dealing with missing values in proteomics data.

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

Powerful eQTL mapping through low-coverage RNA sequencing.

SummaryMapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-seq limits sample size, sequencing depth, and, therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-seq of whole-blood tissue across 1,490 individuals at low coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-seq study of 570 individuals at moderate coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-seq is an effective approach to increase discovery power in eQTL studies.

The Case for Digital Pill Use in Clinical Trials

Medication adherence in clinical trials is significantly overestimated through every phase of drug development. This can cause a reduction in statistical power, potentially resulting in incorrect conclusions regarding efficacy, safety, tolerability, and dose-response relationships, in addition to major cost overruns. Digital pill systems enable adherence measurement through an embedded ingestible sensor paired with an external receiver. An oral pharmaceutical product is over-encapsulated by a pharmaceutical-grade shell containing a biocompatible sensor. Upon exposure to gastrointestinal fluid, the shell dissolves and the sensor is activated. Medication ingestion data is transmitted via a digital signal. Clinicians and researchers use this data to track, in real time, when and if a medication was taken. These systems have demonstrated a 99.4% rate of accuracy, and have over 15-years of supporting experience and safety data. Spurred by the accelerated adoption of technology in healthcare and in everyday life, patients have become tech-savvy. They quickly adapt to these devices, and are able to use them safely and effectively. Digital pills can be implemented in most types of studies. In early-stage trials such as pharmacokinetic and pharmacodynamic studies, or dose-finding studies, accurate information on maximum-tolerated dose levels is essential and cannot be established unless study participants are highly adherent. In later-stage pivotal trials, effective medication adherence tracking can strengthen the dataset and confidence in the study results. Significant nonadherence may generate results that do not meet statistical or clinical significance for the critical endpoints, resulting in at worst, a failed trial, or at best, the need to enroll additional patients at substantial additional cost. Most clinical trials fail to achieve statistical significance, and poor medication adherence is often an important contributor. A digital pill system can ensure the quality and integrity of adherence data, increase confidence in the overall study data, and improve clinical trial efficiency

Addressing Missing Data in Quantitative Counseling Research

Although many statistical procedures that are utilized by counseling researchers require complete datasets, the problem of missing data represents a common analysis hurdle that must be overcome. Typically, counseling researchers address the problem of missing data using listwise deletion; however, this procedure has some statistical disadvantages (e.g., unnecessary reduction in statistical power and unintentional introduction of bias). The most recent versions of statistical packages such as SPSS now include more robust imputation procedures for dealing with missing data. However, utilizing any deletion or imputation procedures without a thorough understanding of the conditions in which these procedures should be used could negatively impact study findings. In this article, strategies for detecting missingness mechanisms and appropriately handling missing data using deletion and imputation available procedures in SPSS are discussed. The specific procedures reviewed include listwise deletion, pairwise deletion, mean substitution, expectation-maximization, hot deck, multiple imputation linear regression, and predictive mean matching.

The Efficacy of Cognitive Processing Therapy for PTSD Related to Military Sexual Trauma in Veterans: A Review

ABSTRACTWomen currently comprise 12% of the total number of service personnel who have served in Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn. Presently, 15% of active duty personnel, 17% of National Guard personnel, and 20% of new recruits are female. The number of female veterans utilizing the Veterans Health Administration has more than doubled, from 159,000 to 337,000 from 2000–2011 and is expected to double again within the next decade. Similar overall numbers of female and male service members report experiencing military sexual trauma (MST). Prevalence rates vary by study where 20 to 68% of female service members and 1% to 6% of male service members have experienced MST. Many of these service members subsequently develop mental health issues and are diagnosed with conditions related to MST.Purpose: The purpose of this review was to summarize outcome studies examining Cognitive Processing Therapy (CPT) for the treatment of Post-traumatic Stress Disorder (PTSD) related to Military Sexual Trauma.Methods: The informational databases that were utilized for the current review were ERIC (EBSCO), ERIC (ProQuest), Google Scholar, and PsycINFO. The dates for literature inclusion was from 2010 to March of 2019. The literature search was initiated in March and was terminated in April of 2019.Results: The results indicated that CPT is an effective intervention for the reduction of MST related PTSD symptoms, including negative cognitions, compared to Present-Centered Therapy (PCT). Additionally, at 6-month post-treatment follow-up symptom reduction was stable. Results were mixed for CPT associated with quality of life and exposure to child Sexual abuse (CSA). Veterans who self-identified as African-American and Caucasian female veterans experienced significant reductions in PTSD symptomology with no differences between groups.Discussion: All studies included in the review resulted in significant reductions in PTSD but four of five also suffered from treatment fidelity in the CPT treatment condition. Researchers chose to exclude participant data from the therapists who did not adhere to CPT protocol resulting in a reduction of statistical power in these studies. The final study included in the review administered multiple treatment modalities concurrently.

Prevalence and characteristics of energy intake under-reporting among Australian adults in 1995 and 2011 to 2012.

Misreporting of energy intake is a common source of measurement error found in dietary surveys, resulting in biased estimates and a reduction in statistical power. The present study aims to refine the conventional cut-off methods and to examine the extent to which Australian adults misreport their energy intake, and the characteristics of under-reporters between two time points. A revised Goldberg cut-off approach was used to identify those who reported implausible intake amounts in a secondary analysis of two large cross-sectional surveys. Identified low energy reporters were then used as the outcome variable in Poisson regressions to examine association with sex, age, body mass index (BMI), weight perceptions, education, relative household income, geographic remoteness and relative socioeconomic disadvantage. The prevalence of under-reporting increased from 32% in 1995 to 41% in 2012, most of which can be attributed to an increase in men. Under-reporting has a positive association with BMI and relative socioeconomic disadvantage, but an inverse association with age, education, relative household income and residence in inner regional areas. Under-reporting of energy intake is high in Australian adults, and appears have worsened over time in men, which could be partly explained by the upward trend in obesity. The use of conventional Goldberg method to identify under-reporters can greatly underestimate the prevalence of under-reporting, future studies should consider selecting a lower critical value to improve accuracy.

A Call for Hypothesis-Driven, Multi-Level Analysis in Research on Emotional Word Painting in Music: Commentary on Sun & Cuthbert (2018)

This commentary discusses Sun and Cuthbert's (2018) exploratory analysis of emotional word painting in a corpus of English-language popular and folk songs. The authors are complimented for their application of computational tools to an impressively large sample of a somewhat understudied musical genre, and for their detailed level of analysis mapping musical features to the semantic content of individual words. This work, however, suffers from a lack of a priori predictions which causes multiple comparison issues leading to a dramatic reduction in statistical power. The selection of musical features and analytical strategies also seems arbitrary at times due to the absence of motivating hypotheses. It is argued that the ethological literature on affective vocal communication in animals might offer an avenue for future hypothesis-driven research on this topic.

A study update newsletter or Post-it® note did not increase postal questionnaire response rates in a falls prevention trial: an embedded randomised factorial trial.

Background: Participants not returning data collection questionnaires is a problem for many randomised controlled trials. The resultant loss of data leads to a reduction in statistical power and can result in bias. The aim of this study was to assess whether the use of a study update newsletter and/or a handwritten or printed Post-it® note sticker increased postal questionnaire response rates for participants of a randomised controlled trial. Method: This study was a factorial trial embedded within a host trial of a falls-prevention intervention among men and women aged ≥65 years under podiatric care. Participants were randomised into one of six groups: newsletter plus handwritten Post-it®; newsletter plus printed Post-it®; newsletter only; handwritten Post-it® only; printed Post-it® only; or no newsletter or Post-it®. The results were combined with those from previous embedded randomised controlled trials in meta-analyses. Results: The overall 12-month response rate was 803/826 (97.2%) (newsletter 95.1%, no newsletter 99.3%, printed Post-it® 97.5%, handwritten Post-it® 97.1%, no Post-it® 97.1%). The study update newsletter had a detrimental effect on response rates (adjusted odds ratio 0.14, 95% CI 0.04 to 0.48, p<0.01) and time to return the questionnaire (adjusted hazard ratio 0.86, 95% CI 0.75 to 0.99, p=0.04). No other statistically significant differences were observed between the intervention groups on response rates, time to response, and the need for a reminder. Conclusions: Post-it® notes have been shown to be ineffective in three embedded trials, whereas the evidence for newsletter reminders is still uncertain.

A study update newsletter or Post-it® note did not increase postal questionnaire response rates in a falls prevention trial: an embedded randomised factorial trial

Background: Participants not returning data collection questionnaires is a problem for many randomised controlled trials. The resultant loss of data leads to a reduction in statistical power and can result in bias. The aim of this study was to assess whether the use of a study update newsletter and/or a handwritten or printed Post-it® note sticker increased postal questionnaire response rates for participants of a randomised controlled trial. Method: This study was a factorial trial embedded within a host trial of a falls-prevention intervention among men and women aged ≥65 years under podiatric care. Participants were randomised into one of six groups: newsletter plus handwritten Post-it®; newsletter plus printed Post-it®; newsletter only; handwritten Post-it® only; printed Post-it® only; or no newsletter or Post-it®. The results were combined with those from previous embedded randomised controlled trials in meta-analyses. Results: The overall 12-month response rate was 803/826 (97.2%) (newsletter 95.1%, no newsletter 99.3%, printed Post-it® 97.5%, handwritten Post-it® 97.1%, no Post-it® 97.1%). The study update newsletter had a detrimental effect on response rates (adjusted odds ratio 0.14, 95% CI 0.04 to 0.48, p<0.01) and time to return the questionnaire (adjusted hazard ratio 0.86, 95% CI 0.75 to 0.99, p=0.04). No other statistically significant differences were observed between the intervention groups on response rates, time to response, and the need for a reminder. Conclusions: Post-it® notes have been shown to be ineffective in three embedded trials, whereas the evidence for newsletter reminders is still uncertain.

Comparison of three sample size calculation methods for non-inferiority vaccine trials with multiple continuous co-primary endpoints

ABSTRACTClinical trials that study immunogenicity of combination vaccines often have less power than desirable. To make up for the reduction in statistical power at the study level, researchers have to increase the study sample size. To study immunogenicity variables, we used the geometric mean concentration of immune response after vaccination as immunologic endpoint and compared 3 sample size calculation methods: the “Inflation factors” method, the “Incrementing” method, and the “Bonferroni correction” method when there are multiple continuous co-primary endpoints. The parameters were set according to the actual situation of the use of combination vaccines and the simulation results were used as reference. The present study demonstrates that all 3 methods are applicable when the effect size of each endpoint is similar and the endpoints are at most weakly correlated, but when there is a true difference in effect sizes among endpoints, the “Incrementing” method has the best performance.

A nested randomised controlled trial of a newsletter and Post-it® note did not increase postal questionnaire response rates in a falls prevention trial

Background: Attrition (i.e. when participants do not return the questionnaires) is a problem for many randomised controlled trials. The resultant loss of data leads to a reduction in statistical power and can lead to bias. The aim of this study was to assess whether a pre-notification newsletter and/or a handwritten or printed Post-it® note sticker, as a reminder, increased postal questionnaire response rates for participants of randomised controlled trials. Method: This study was a factorial trial embedded within a trial of a falls-prevention intervention among men and women aged ≥65 years under podiatric care. Participants were randomised into one of six groups: newsletter plus handwritten Post-it®; newsletter plus printed Post-it®; newsletter only; handwritten Post-it® only; printed Post-it® only; or no newsletter or Post-it®. The results were combined with those from previous embedded randomised controlled trials in a meta-analysis. Results: The 12-month response rate was 803/826 (97.2%) (newsletter 95.1%, no newsletter 99.3%, printed Post-it® 97.5%, handwritten Post-it® 97.1%, no Post-it® 97.1%). Pre-notification with a newsletter had a detrimental effect on response rates (adjusted odds ratio (OR), 0.14; 95% CI, 0.04 to 0.48; p&lt;0.01) and time to return the questionnaire (adjusted hazard ratio, 0.86; 95% CI, 0.75 to 0.99; p=0.04). No other statistically significant differences were observed between the intervention groups on response rates, time to response, and the need for a reminder. Conclusions: Post-it® notes have been shown to be ineffective in three embedded trials, whereas the evidence for newsletter reminders is still uncertain.

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools.To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques.

Estimating Dynamic Signals From Trial Data With Censored Values.

Censored data occur commonly in trial-structured behavioral experiments and many other forms of longitudinal data. They can lead to severe bias and reduction of statistical power in subsequent analyses. Principled approaches for dealing with censored data, such as data imputation and methods based on the complete data’s likelihood, work well for estimating fixed features of statistical models but have not been extended to dynamic measures, such as serial estimates of an underlying latent variable over time. Here we propose an approach to the censored-data problem for dynamic behavioral signals. We developed a state-space modeling framework with a censored observation process at the trial timescale. We then developed a filter algorithm to compute the posterior distribution of the state process using the available data. We showed that special cases of this framework can incorporate the three most common approaches to censored observations: ignoring trials with censored data, imputing the censored data values, or using the full information available in the data likelihood. Finally, we derived a computationally efficient approximate Gaussian filter that is similar in structure to a Kalman filter, but that efficiently accounts for censored data. We compared the performances of these methods in a simulation study and provide recommendations of approaches to use, based on the expected amount of censored data in an experiment. These new techniques can broadly be applied in many research domains in which censored data interfere with estimation, including survival analysis and other clinical trial applications.

Covariate-adjusted response-adaptive randomization for multi-arm clinical trials using a modified forward looking Gittins index rule.

SummaryWe introduce a non-myopic, covariate-adjusted response adaptive (CARA) allocation design for multi-armed clinical trials. The allocation scheme is a computationally tractable procedure based on the Gittins index solution to the classic multi-armed bandit problem and extends the procedure recently proposed in Villar et al. (2015). Our proposed CARA randomization procedure is defined by reformulating the bandit problem with covariates into a classic bandit problem in which there are multiple combination arms, considering every arm per each covariate category as a distinct treatment arm. We then apply a heuristically modified Gittins index rule to solve the problem and define allocation probabilities from the resulting solution. We report the efficiency, balance, and ethical performance of our approach compared to existing CARA methods using a recently published clinical trial as motivation. The net savings in terms of expected number of treatment failures is considerably larger and probably enough to make this design attractive for certain studies where known covariates are expected to be important, stratification is not desired, treatment failures have a high ethical cost, and the disease under study is rare. In a two-armed context, this patient benefit advantage comes at the expense of increased variability in the allocation proportions and a reduction in statistical power. However, in a multi-armed context, simple modifications of the proposed CARA rule can be incorporated so that an ethical advantage can be offered without sacrificing power in comparison with balanced designs.

Statistical Power for Postlicensure Medical Product Safety Data Mining

Objective: To perform sample size calculations when using tree-based scan statistics in longitudinal observational databases.Methods: Tree-based scan statistics enable data mining on epidemiologic datasets where thousands of disease outcomes are organized into hierarchical tree structures with automatic adjustment for multiple testing. We show how to evaluate the statistical power of the unconditional and conditional Poisson versions. The null hypothesis is that there is no increase in the risk for any of the outcomes. The alternative is that one or more outcomes have an excess risk. We varied the excess risk, total sample size, frequency of the underlying event rate, and the level of across-the-board health care utilization. We also quantified the reduction in statistical power resulting from specifying a risk window that was too long or too short.Results: For 500,000 exposed people, we had at least 98 percent power to detect an excess risk of 1 event per 10,000 exposed for all outcomes. In the presence of potential temporal confounding due to across-the-board elevations of health care utilization in the risk window, the conditional tree-based scan statistic controlled type I error well, while the unconditional version did not.Discussion: Data mining analyses using tree-based scan statistics expand the pharmacovigilance toolbox, ensuring adequate monitoring of thousands of outcomes of interest while controlling for multiple hypothesis testing. These power evaluations enable investigators to design and optimize implementation of retrospective data mining analyses.

Statistical Power for Postlicensure Medical Product Safety Data Mining.

Objective:To perform sample size calculations when using tree-based scan statistics in longitudinal observational databases.Methods:Tree-based scan statistics enable data mining on epidemiologic datasets where thousands of disease outcomes are organized into hierarchical tree structures with automatic adjustment for multiple testing. We show how to evaluate the statistical power of the unconditional and conditional Poisson versions. The null hypothesis is that there is no increase in the risk for any of the outcomes. The alternative is that one or more outcomes have an excess risk. We varied the excess risk, total sample size, frequency of the underlying event rate, and the level of across-the-board health care utilization. We also quantified the reduction in statistical power resulting from specifying a risk window that was too long or too short.Results:For 500,000 exposed people, we had at least 98 percent power to detect an excess risk of 1 event per 10,000 exposed for all outcomes. In the presence of potential temporal confounding due to across-the-board elevations of health care utilization in the risk window, the conditional tree-based scan statistic controlled type I error well, while the unconditional version did not.Discussion:Data mining analyses using tree-based scan statistics expand the pharmacovigilance toolbox, ensuring adequate monitoring of thousands of outcomes of interest while controlling for multiple hypothesis testing. These power evaluations enable investigators to design and optimize implementation of retrospective data mining analyses.

Field evaluation of tolerance toTobacco streak virusin sunflower germplasm, and observations of seasonal disease spread

Strong statistical evidence was found for differences in tolerance to natural infections of Tobacco streak virus (TSV) in sunflower hybrids. Data from 470 plots involving 23 different sunflower hybrids tested in multiple trials over 5 years in Australia were analysed. Using a Bayesian Hierarchical Logistic Regression (BHLR) model for analysis provided: (a) a rigorous method for investigating the relative effects of hybrid, seasonal rainfall and proximity to inoculum source on the incidence of severe TSV disease; (b) a natural method for estimating the probability distributions of disease incidence in different hybrids under historical rainfall conditions; and (c) a method for undertaking all pairwise comparisons of disease incidence between hybrids while controlling the familywise error rate without any drastic reduction in statistical power. The tolerance identified in field trials was effective against the main TSV strain associated with disease outbreaks, TSV-parthenium. Glasshouse tests indicate this tolerance to also be effective against the other TSV strain found in central Queensland, TSV-crownbeard. The use of tolerant germplasm is critical to minimise the risk of TSV epidemics in sunflower in this region. We found strong statistical evidence that rainfall during the early growing months of March and April had a negative effect on the incidence of severe infection with greatly reduced disease incidence in years that had high rainfall during this period.

Dimensional Latent Structure of Relationship Quality: Results of Three Representative Population Samples

A fundamental question facing every latent construct (e.g., relationship quality) is whether the construct is categorical or dimensional in nature. The authors analyzed the latent status of relationship quality in 3 large general German population samples using the Partnership Questionnaire (PFB), the Quality of Relationship Inventory, and the short form of the PFB (PFB‐K; N1 = 1,330, N2 = 1,494, N3 = 1,390). They applied 3 widely used taxometric methods: (a) MAXEIG, (b) MAMBAC, and (c) L‐Mode. Simulation data were created to evaluate the comparison curve fit index values, which were below 0.5 across the 3 German population samples, supporting a dimensional solution. Hence, in the current study the latent structure of relationship quality encompassed differences in degree rather than kind. Implications of the dimensional latent status of relationship quality, as well as causes of the discrepant findings with previous taxometric studies, are discussed.

GFR Decline as an End Point for Clinical Trials in CKD: A Scientific Workshop Sponsored by the National Kidney Foundation and the US Food and Drug Administration

The US Food and Drug Administration currently accepts halving of glomerular filtration rate (GFR), assessed as doubling of serum creatinine level, as a surrogate end point for the development of kidney failure in clinical trials of kidney disease progression. A doubling of serum creatinine level generally is a late event in chronic kidney disease (CKD); thus, there is great interest in considering alternative end points for clinical trials to shorten their duration, reduce sample size, and extend their conduct to patients with earlier stages of CKD. However, the relationship between lesser declines in GFR and the subsequent development of kidney failure has not been well characterized. The National Kidney Foundation and Food and Drug Administration sponsored a scientific workshop to critically examine available data to determine whether alternative GFR-based end points have sufficiently strong relationships with important clinical outcomes of CKD to be used in clinical trials. Based on a series of meta-analyses of cohorts and clinical trials and simulations of trial designs and analytic methods, the workshop concluded that a confirmed decline in estimated GFR of 30% over 2 to 3 years may be an acceptable surrogate end point in some circumstances, but the pattern of treatment effects on GFR must be examined, specifically acute effects on estimated GFR. An estimated GFR decline of 40% may be more broadly acceptable than a 30% decline across a wider range of baseline GFRs and patterns of treatment effects on GFR. However, there are other circumstances in which these end points could lead to a reduction in statistical power or erroneous conclusions regarding benefits or harms of interventions. We encourage careful consideration of these alternative end points in the design of future clinical trials.