Understanding Treatment Response Heterogeneity Using Crossover Randomized Controlled Trials: A Primer for Exercise and Nutrition Scientists.

Consumption of lean fish and fish-derived proteins were effective for improving lipid profiles in published studies; however, evidence remains inconclusive. To evaluate the effectiveness of lean fish or fish-derived protein on serum/plasma lipid and lipoprotein levels by conducting a systematic review of the literature and meta-analysis of available randomized controlled trials (RCTs). Medline (Ovid), Scopus, CINAHL, and Food and Nutritional Sciences databases were searched from the start date of each database to September 2019 to identify RCTs determining the effect of lean fish on lipid profile. RCTs investigated lean fish and fish-derived proteins intake and determined at least 1 major lipid or lipoprotein measurement. Two reviewers independently evaluated 1217 studies against the inclusion and exclusion criteria. Relevant studies were assessed for risks of bias, and random-effects meta-analysis was conducted to generate average estimates of effect. A total of 24 studies met the inclusion criteria. Meta-analysis of data from 18 to 21 eligible crossover and parallel-design RCTs with a total of 1392 to 1456 participants found triacylglycerol-lowering effects for lean fish compared with no fish consumption. Lean fish intake showed no significant differences related to total cholesterol or lipoprotein levels. Subanalysis showed that parallel-group RCTs tended to find greater reduction effects on circulating triacylglycerol than did crossover RCTs. Additional better-designed, longer, and larger RCTs, particularly crossover RCTs, are needed to clarify the impact of lean fish and fish proteins on the serum/plasma lipid profile. Findings from such studies would enable practitioners to provide their patients evidence-based recommendations to meet the American Heart Association guidelines for fish consumption to reduce cardiovascular disease risk.

BackgroundThe endocrine disruptor field has been vexed by difficulties in reproducing various claims of effects at unusually low doses. In previous analyses, variations in control responses from experiment to experiment and problems with observing effects in positive controls have been identified as possible explanations of the resulting impasse.ObjectiveIn this article, we argue that both of these viewpoints fail to take sufficient account of the problems that exist in estimating low effects and low-effect doses. We have carried out post hoc power analyses on selected published data to illustrate that claims of low-dose effects (or their absence) are often compromised by insufficient statistical power of the chosen experimental design.ConclusionsWe demonstrate that low-dose estimates such as the no observed adverse effect levels derived from statistical hypothesis-testing procedures are dependent on the specific experimental conditions used for testing. Thus, below the statistical detection limit of the experiment, the presence of effects can neither be proven nor ruled out. Common practice is to attempt to establish “doses without effect.” However, low-dose estimations in the endocrine-disruptor field could be improved if decisions regarding the toxicologic effect size of relevance formed the starting point of testing procedures. Statistical power considerations could then reveal the resources necessary to demonstrate effect magnitudes of concern.

Nevin Stewart Scrimshaw

Statistical power considerations in the use of win ratio in cardiovascular outcome trials

BackgroundMulti-pathogen molecular diagnostics enable assignment of diarrhoea aetiology, but defining thresholds of pathogen quantity to accurately attribute aetiology is challenging in high-burden settings where coinfections are common. The Antibiotics for Children with severe Diarrhoea (ABCD) trial provides an opportunity to leverage the azithromycin treatment response to inform which diarrhoea episodes are bacterial.MethodsWe analysed data from ABCD, which randomized children with watery diarrhoea to azithromycin or placebo. We quantified heterogeneity in the azithromycin treatment response by the quantity of enteric pathogens detected by qPCR as a tool for understanding aetiology.ResultsThe heterogeneity in azithromycin treatment response was most prominent forShigella. The risk ratio for diarrhoea on day 3 post enrolment for azithromycin compared to placebo was 13% (95% CI:3, 23) lower per log10 increase inShigellaquantity. The protective effect of azithromycin on diarrhoea at day 3 also became stronger as pathogen quantities increased forVibrio cholerae, ST-ETEC, and tEPEC. No association between pathogen quantity and azithromycin response was observed forCampylobacter, LT-ETEC or EAEC. The associations were consistent for the outcome of 90-day hospitalisation or death.ConclusionsThe relationships between response to azithromycin treatment and bacterial pathogen quantities observed forShigella,Vibrio cholerae, ST-ETEC and tEPEC confirm prior evidence that these pathogens are the likely cause of diarrhoea when detected at high quantities. The lack of a similar response pattern forCampylobacter, LT-ETEC or EAEC is consistent with the limited association between pathogen quantity and diarrhoea symptoms previously observed in large studies of diarrhoea aetiology.Key message(3 succinct bullet points, each a single sentence)We investigated whether heterogeneity in treatment response observed in the ABCD trial, where children with diarrhoea were randomised to receive azithromycin or placebo, could be used to inform aetiological attribution of diarrhoea to bacterial enteric pathogens.The protective effect of azithromycin on diarrhoea at day 3 and hospitalisation or death at day 90 became stronger as pathogen quantities increased forShigella, Vibrio cholerae, ST-ETEC and tEPECbut not forCampylobacter, LT-ETEC or EAEC.The relationships betweenShigella, Vibrio cholerae, ST-ETEC and tEPEC quantity and response to antibiotic treatment confirm prior evidence that these pathogens are the likely cause of diarrhoea when detected at high quantities and could be used to inform which diarrhoea cases should be treated with antibiotics.

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Statistical Power Considerations for a Utility Endpoint in Observer Performance Studies

BackgroundDecision analyses of drug treatments in chronic diseases require modeling the progression of disease and treatment response beyond the time horizon of clinical or epidemiological studies. In many such models, progression and drug effect have been applied uniformly to all patients; heterogeneity in progression, including pharmacogenomic effects, has been ignored. ObjectiveWe sought to systematically evaluate the existence, direction and relative magnitude of a pharmacogenomics bias (PGX-Bias) resulting from failure to adjust for genetic heterogeneity in both treatment response (HT) and heterogeneity in progression of disease (HP) in decision-analytic studies based on clinical study data.MethodsWe performed a systematic literature search in electronic databases for studies regarding the effect of genetic heterogeneity on the validity of study results. Included studies have been summarized in evidence tables.In the case of lacking evidence from published studies we sought to perform our own simulation considering both HT and HP. We constructed two simple Markov models with three basic health states (early-stage disease, late-stage disease, dead), one adjusting and the other not adjusting for genetic heterogeneity. Adjustment was done by creating different disease states for presence (G+) and absence (G-) of a dichotomous genetic factor. We compared the life expectancy gains attributable to treatment resulting from both models and defined pharmacogenomics bias as percent deviation of treatment-related life expectancy gains in the unadjusted model from those in the adjusted model. We calculated the bias as a function of underlying model parameters to create generic results. We then applied our model to lipid-lowering therapy with pravastatin in patients with coronary atherosclerosis, incorporating the influence of two TaqIB polymorphism variants (B1 and B2) on progression and drug efficacy as reported in the DNA substudy of the REGRESS trial.ResultsWe found four studies that systematically evaluated heterogeneity bias. All of them indicated that there is a potential of heterogeneity bias. However, none of these studies explicitly investigated the effect of genetic heterogeneity. Therefore, we performed our own simulation study.Our generic simulation showed that a purely HT-related bias is negative (conservative) and a purely HP-related bias is positive (liberal). For many typical scenarios, the absolute bias is smaller than 10%. In case of joint HP and HT, the overall bias is likely triggered by the HP component and reaches positive values >100% if fractions of „fast progressors" and „strong treatment responders" are low. In the clinical example with pravastatin therapy, the unadjusted model overestimated the true life-years gained (LYG) by 5.5% (1.07 LYG vs. 0.99 LYG for 56-year-old men).ConclusionsWe have been able to predict the pharmacogenomics bias jointly caused by heterogeneity in progression of disease and heterogeneity in treatment response as a function of characteristics of patients, chronic disease, and treatment. In the case of joint presence of both types of heterogeneity, models ignoring this heterogeneity may generate results that overestimate the treatment benefit.

Objective: The objective of this systematic review was to synthesize the best available research evidence on the effectiveness of flavonoid-rich fruits in the treatment of hypertension. Introduction: Hypertension is a serious public health concern as it contributes to a significant burden of disease, leading to millions of deaths globally. Complementary therapies including flavonoids have generated interest in assisting the treatment of hypertension. Flavonoids are a type of polyphenol abundant in fruits and a growing body of evidence suggests antihypertensive effects of the flavonoids due to their antioxidant properties. To date, no systematic review has been performed to collate the evidence on the effects of flavonoid-rich fruits on hypertension in adults. Inclusion criteria: This systematic review included randomized controlled trials (RCTs) that compared the administration of any type of flavonoid-rich fruit or equivalent supplement with a placebo or other intervention in adults with hypertension. Trials that measured blood pressure using objective outcome measures such as a manual mercury sphygmomanometer were included. Studies that did not specify the flavonoid component of the fruit or fruit supplement were excluded from the review. Secondary outcomes, including change in weight, blood glucose level, triglycerides and total blood cholesterol levels, were also assessed. Methods: A three-step search was undertaken, including a comprehensive search of the MEDLINE, Embase, Cochrane Trials (CENTRAL) and CINAHL databases, in September 2018. We also searched Dissertation Abstracts International, ProQuest Dissertations and Theses, MedNar and ClinicalTrials.gov to identify unpublished studies. The title and abstracts of the studies were reviewed by two independent reviewers against the inclusion/exclusion criteria. The methodological quality of the potential studies for inclusion were assessed using the critical appraisal checklist for randomized controlled trials as recommended by JBI. Data were pooled in a statistical meta-analysis model. Subgroup-analysis according to type of intervention and length of intervention period was performed. Where statistical pooling was not possible, the findings have been presented in a narrative form. Results: Fifteen randomized controlled trials involving 572 participants were included in the review. The subclasses of flavonoids assessed included: anthocyanins, naringin, narirutin and flavan-3-ols. The overall methodological quality of the trials was high. Six trials investigated the effect of the flavonoid intervention on blood pressure within four weeks. Meta-analysis of four of the trials demonstrated no effect of flavonoids on systolic or diastolic blood pressure when compared to placebo (systolic mean difference = −1.02, 95% confidence interval [CI] −3.12, 1.07; p = 0.34, I2 = 0%; diastolic mean difference = −0.90, 95% CI −2.10, 0.31; p = 0.15, I2 = 0%). Similarly, pooled results from two crossover RCTs with two-timed dosed interventions in a 24-hour period demonstrated no effect on a reduction in diastolic blood pressure (p = 0.38) but did reveal evidence of a reduction in systolic blood pressure (p = 0). Six trials assessed blood pressure following the flavonoid intervention at more than four weeks follow-up. Meta-analysis of five of the trials demonstrated evidence of no effect on either systolic blood pressure (mean difference = −0.95, 95%CI −3.58, 1.68; p = 0.478, I2 = 0%) or diastolic blood pressure (mean difference = 0.86, 95%CI −1.11, 2.82; p = 0.393, I2 = 0%). Conclusions: The findings of this systematic review should be interpreted with caution, given that the results are obtained from single-center trials with small sample sizes. Flavonoids have no effect on systolic and diastolic blood pressure. Further robust RCTs using sample sizes based on power calculations are needed to provide evidence for the use of flavonoid-rich fruits for the management of hypertension.

Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for gene-metformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.

Mapping Voxel-Based Statistical Power on Parametric Images

Systemic autoimmune rheumatic diseases (SARDs) exhibit extensive heterogeneity in clinical presentation, disease course, and treatment response. Therefore, precision medicine - whereby treatment is tailored according to the underlying pathogenic mechanisms of an individual patient at a specific time - represents the 'holy grail' in SARD clinical care. Current strategies include treat-to-target therapies and autoantibody testing for patient stratification; however, these are far from optimal. Recent innovations in high-throughput 'omic' technologies are now enabling comprehensive profiling at multiple levels, helping to identify subgroups of patients who may taper off potentially toxic medications or better respond to current molecular targeted therapies. Such advances may help to optimize outcomes and identify new pathways for treatment, but there are many challenges along the path towards clinical translation. In this Review, we discuss recent efforts to dissect cellular and molecular heterogeneity across multiple SARDs and future directions for implementing stratification approaches for SARD treatment in the clinic.

Although there is a growing understanding of the importance of statistical power considerations when designing studies and of the value of confidence intervals when interpreting data, confusion exists about the reverse arrangement: the role of confidence intervals in study design and of power in interpretation. Confidence intervals should play an important role when setting sample size, and power should play no role once the data have been collected, but exactly the opposite procedure is widely practiced. In this commentary, we present the reasons why the calculation of power after a study is over is inappropriate and how confidence intervals can be used during both study design and study interpretation.

The standardization and Mantel-Haenszel approaches to the assessment of differential item functioning (DIF) are described and compared. For rightwrong scoring of items, these two approaches, which emphasize the importance of comparing comparable groups of examinees, use the same data base for analysis, namely, a 2 (Group) x 2 (Item Score: Correct or Incorrect) x S (Score Level) contingency table for each item studied. The two procedures differ with respect to how they operate on these basic data tables to compare the performance of the two groups of examinees. Whereas the operations employed by Mantel-Haenszel are motivated by statistical power considerations, the operations employed by standardization are motivated by datainterpretation considerations. These differences in operation culminate in different measures of DIF effect-size that are very highly related indicators of degree of departure from the null hypothesis of no DIF.

LetterOpen AccessEndocrine disruptors and testis development. R M Sharpe, K J Turner, and J P Sumpter R M Sharpe Search for more papers by this author , K J Turner Search for more papers by this author , and J P Sumpter Search for more papers by this author Published:1 May 1998https://doi.org/10.1289/ehp.106-1533085Cited by:4AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit "Endocrine disruptors and testis development.." Environmental Health Perspectives, 106(5), pp. A220–A221FiguresReferencesRelatedDetailsCited byTyl R (2009) Basic Exploratory Research versus Guideline-Compliant Studies Used for Hazard Evaluation and Risk Assessment: Bisphenol A as a Case Study, Environmental Health Perspectives, 117:11, (1644-1651), Online publication date: 1-Nov-2009.Scholze M and Kortenkamp A (2007) Statistical Power Considerations Show the Endocrine Disruptor Low-Dose Issue in a New Light, Environmental Health Perspectives, 115:Suppl 1, (84-90), Online publication date: 1-Dec-2007.Ashby J, Tinwell H, Odum J and Lefevre P (2018) Natural variability and the influence of concurrent control values on the detection and interpretation of low-dose or weak endocrine toxicities., Environmental Health Perspectives, 112:8, (847-853), Online publication date: 1-Jun-2004.Moore R, Rudy T, Lin T, Ko K and Peterson R (2018) Abnormalities of sexual development in male rats with in utero and lactational exposure to the antiandrogenic plasticizer Di(2-ethylhexyl) phthalate., Environmental Health Perspectives, 109:3, (229-237), Online publication date: 1-Mar-2001. Vol. 106, No. 5 May 1998Metrics About Article Metrics Publication History Originally published1 May 1998Published in print1 May 1998 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Note to readers with disabilities EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days.