Cancers Attributable to Infections in the US in 2017: A Meta-Analysis.

Importance: Infections are largely modifiable causes of cancer. However, there remains untapped potential for preventing and treating carcinogenic infections in the US. Objective: To estimate the percentage and number of incident cancers attributable to infections in the US among adults and children for the most recent year cancer incidence data were available (2017). Data Sources: A literature search from 1946 onward was performed in MEDLINE on January 6, 2023, to obtain the data required to calculate population attributable fractions for 31 infection-cancer pairs. National Health and Nutrition Examination Survey data were used to estimate the population prevalence of hepatitis B and C viruses and Helicobacter pylori. Study Selection: Studies conducted in the US or other Western countries were selected according to specific infection-cancer criteria. Data Extraction and Synthesis: Data from 128 studies were meta-analyzed to obtain the magnitude of an infection-cancer association or prevalence of the infection within cancer cells. Main Outcomes and Measures: The proportion of cancer incidence attributable to 8 infections. Results: Of the 1 666 102 cancers diagnosed in 2017 among individuals aged 20 years or older in the US, 71 485 (4.3%; 95% CI, 3.1%-5.3%) were attributable to infections. Human papillomavirus (n = 38 230) was responsible for the most cancers, followed by H pylori (n = 10 624), hepatitis C virus (n = 9006), Epstein-Barr virus (n = 7581), hepatitis B virus (n = 2310), Merkel cell polyomavirus (n = 2000), Kaposi sarcoma-associated herpesvirus (n = 1075), and human T-cell lymphotropic virus type 1 (n = 659). Cancers with the most infection-attributable cases were cervical (human papillomavirus; n = 12 829), gastric (H pylori and Epstein-Barr virus; n = 12 565), oropharynx (human papillomavirus; n = 12 430), and hepatocellular carcinoma (hepatitis B and C viruses; n = 10 017). The burden of infection-attributable cancers as a proportion of total cancer incidence ranged from 9.6% (95% CI, 9.2%-10.0%) for women aged 20 to 34 years to 3.2% (95% CI, 2.4%-3.8%) for women aged 65 years or older and from 6.1% (95% CI, 5.2%-7.0%) for men aged 20 to 34 years to 3.3% (95% CI, 1.9%-4.4%) for men aged 65 years or older. Among those aged 19 years or younger, 2.2% (95% CI, 1.3%-3.0%) of cancers diagnosed in 2017 were attributable to Epstein-Barr virus. Conclusions and Relevance: Infections were estimated to be responsible for 4.3% of cancers diagnosed among adults in the US in 2017 and, therefore, represent an important target for cancer prevention efforts.

Design and analysis of factorial clinical trials: The impact of one treatment's effectiveness on the statistical power and required sample size of the other.

Factorial allow for the simultaneous evaluation of more than one treatment, by randomizing patients to their possible combinations, including control. However, the statistical power of one treatment can be influenced by the effectiveness of the other, a matter that has not been widely recognized. In this paper, we evaluate the relationship between the observed effectiveness of one treatment and the implied power for a second treatment in the same trial, under a range of conditions. We provide analytic and numerical solutions for a binary outcome, under the additive, multiplicative, and odds ratio scales for treatment interaction. We demonstrate how the minimum required sample size for a trial depends on the two treatment effects. Relevant factors include the event rate in the control group, sample size, treatment effect sizes, and Type-I error rate thresholds. We show that that power for one treatment decreases as a function of the observed effectiveness of the other treatment if there is no multiplicative interaction. A similar pattern is observed with the odds ratio scale at low control rates, but at high control rates, power may increase if the first treatment is moderately more effective than its planned value. When treatments do not interact additively, power may either increase or decrease, depending on the control event rate. We also determine where the maximum power occurs for the second treatment. We illustrate these ideas with data from two actual factorial trials. These results can benefit investigators in planning the analysis of factorial clinical trials, in particular, to alert them to the potential for losses in power when one observed treatment effect differs from its originally postulated value. Updating the power calculation and modifying the associated required sample size can then ensure sufficient power for both treatments.

Taking a chance: How likely am I to receive my preferred treatment in a clinical trial?

Researchers should ideally conduct clinical trials under a presumption of clinical equipoise, but in fact trial patients will often prefer one or other of the treatments being compared. Receiving an unblinded preferred treatment may affect the study outcome, possibly beneficially, but receiving a non-preferred treatment may induce 'reluctant acquiescence', and poorer outcomes. Even in blinded trials, patients' primary motivation to enrol may be the chance of potentially receiving a desirable experimental treatment, which is otherwise unavailable. Study designs with a higher probability of receiving a preferred treatment (denoted as 'concordance') will be attractive to potential participants, and investigators, because they may improve recruitment and hence enhance study efficiency. Therefore, it is useful to consider the concordance rates associated with various study designs. We consider this question with a focus on comparing the standard, randomised, two-arm, parallel group design with the two-stage randomised patient preference design and Zelen designs; we also mention the fully randomised and partially randomised patient preference designs. For each of these designs, we evaluate the concordance rate as a function of the proportions randomised to the alternative treatments, the distribution of preferences over treatments, and (for the Zelen designs) the proportion of patients who consent to receive their assigned treatment. We also examine the equity of each design, which we define as the similarity between the concordance rates for participants with different treatment preferences. Finally, we contrast each of the alternative designs with the standard design in terms of gain in concordance and change in equity.

A method was developed for correcting the bias in the usual study weights in meta-analyses.

OBJECTIVES: Our goal was to evaluate the bias in the usual method of estimating study weights in a meta-analysis and to develop a suitable bias correction. STUDY DESIGN AND SETTING: In meta-analyses, it is standard practice to weight studies by the inverse variance of their treatment effects. Weights are usually calculated by taking reciprocals of the estimated variances, but we show that this approach is biased. We established an exact expression for the bias with continuous data, yielding a correction factor for the study weights that yields improved estimation of the treatment effect. RESULTS: With the usual method, the weight for each study is always overestimated, particularly with small samples; also, the variance of the summary treatment effect is underestimated. Our correction yields an unbiased estimate of the summary treatment effect with minimum variance. We illustrate the bias numerically for various scenarios and show how it can substantially affect actual meta-analyses in practice. CONCLUSION: We recommend that the standard method of obtaining study weights should be modified by our bias correction factor. Our method is simple and straightforward to apply. Elimination of this bias will enhance the validity of conclusions from a meta-analysis, compared with the situation when the standard weights are used.

Unified approach to optimal estimation of mean and standard deviation from sample summaries.

Recently, various methods have been developed to estimate the sample mean and standard deviation when only the sample size, and other selected sample summaries are reported. In this paper, we provide a unified approach to optimal estimation that can be easily adopted when only some summary statistics are reported. We show that the proposed estimators have the lowest variance among linear unbiased estimators. We also show that in the most commonly reported cases, that is, when only a three-number or five-number summary is reported, the newly proposed estimators match the previously developed estimators. Finally, we demonstrate the performance of the estimators numerically.

Estimation of standard deviations and inverse-variance weights from an observed range.

A variety of methods have been proposed to estimate a standard deviation, when only a sample range has been observed or reported. This problem occurs in the interpretation of individual clinical studies that are incompletely reported, and also in their incorporation into meta-analyses. The methods differ with respect to their focus being either on the standard deviation in the underlying population or on the particular sample in hand, a distinction that has not been widely recognized. In this article, we contrast and compare various estimators of these two quantities with respect to bias and mean squared error, for normally distributed data. We show that unbiased estimators are available for either quantity, and recommend our preferred methods. We also propose a Taylor series method to obtain inverse-variance weights, for samples where only the sample range is available; this method yields very little bias, even for quite small samples. In contrast, the naïve approach of simply taking the inverse of an estimated variance is shown to be substantially biased, and can place unduly large weight on small samples, such as small clinical trials in a meta-analysis. Accordingly, this naïve (but commonly used) method is not recommended.

The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic.

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.

Impact of centre volume, surgeon volume, surgeon experience and geographic location on reoperation after intramedullary nailing of tibial shaft fractures

Background: Tibial shaft fractures are the most common long-bone injury, with a reported annual incidence of more than 75 000 in the United States. This study aimed to determine whether patients with tibial fractures managed with intramedullary nails experience a lower rate of reoperation if treated at higher-volume hospitals, or by higher-volume or more experienced surgeons. Methods: The Study to Prospectively Evaluate Reamed Intramedullary Nails in Patients with Tibial Fractures (SPRINT) was a multicentre randomized clinical trial comparing reamed and nonreamed intramedullary nailing on rates of reoperation to promote fracture union, treat infection or preserve the limb in patients with open and closed fractures of the tibial shaft. Using data from SPRINT, we quantified centre and surgeon volumes into quintiles. We performed analyses adjusted for type of fracture (open v. closed), type of injury (isolated v. multitrauma), gender and age for the primary outcome of reoperation using multivariable logistic regression. Results: There were no significant differences in the odds of reoperation between high- and low-volume centres (p = 0.9). Overall, surgeon volume significantly affected the odds of reoperation (p = 0.03). The odds of reoperation among patients treated by moderate-volume surgeons were 50% less than those among patients treated by verylow-volume surgeons (odds ratio [OR] 0.50, 95% confidence interval [CI] 0.28­0.88), and the odds of reoperation among patients treated by high-volume surgeons were 47% less than those among patients treated by very-low-volume surgeons (OR 0.53, 95% CI 0.30­0.93). Conclusion: There appears to be no significant additional patient benefit in treatment by a higher-volume centre for intramedullary fixation of tibial shaft fractures. Additional research on the effects of surgical and clinical site volume in tibial shaft fracture management is needed to confirm this finding. The odds of reoperation were higher in patients treated by very-low-volume surgeons; this finding may be used to optimize the results of tibial shaft fracture management. Clinical trial registration: ClinicalTrials.gov, NCT00038129

Contexte: La fracture de la diaphyse tibiale est la plus commune des fractures des os longs, avec une incidence annuelle déclarée de plus 75 000 cas aux États-Unis. Cette étude visait à déterminer si les patients traités par enclouage intramédullaire pour une fracture du tibia sont moins souvent réopérés quand l'intervention est effectuée dans des établissements qui traitent de plus forts volumes de cas ou par des chirurgiens opérant un plus fort volume de cas ou plus expérimentés. Méthodes: L'étude SPRINT (Study to Prospectively Evaluate Reamed Intramedullary Nails in Patients with Tibial Fractures) est un essai clinique multicentrique randomisé qui a comparé l'effet de l'enclouage alésé c. non alésé sur le taux des réinterventions visant à promouvoir la consolidation osseuse de la fracture, à traiter une infection ou à préserver le membre chez des patients victimes de fractures fermées ou ouvertes de la diaphyse tibiale. À partir des données de l'étude SPRINT, nous avons classé les établissements et les chirurgiens en quintiles selon les volumes de cas traités. Nous avons effectué des analyses ajustées en fonction du type de fracture (ouverte c. fermée), du type de blessure (isolée c. polytraumatisme), du sexe et de l'âge, pour établir le taux de réintervention (paramètre principal), en utilisant la régression logistique multivariée. Résultats: On n'a noté aucune différence significative quant au risque de réintervention entre les centres qui traitaient des volumes élevés c. bas (p = 0,9). Dans l'ensemble le volume d'opérations des chirurgiens a significativement influé sur le risque de réintervention (p = 0,03). Le risque de réintervention chez les patients traités par des chirurgiens dont le volume d'interventions était moyen était de 50 % de moins que chez les patients traités par des chirurgiens dont le volume était très bas (risque relatif [RR] 0,50, intervalle de confiance [IC] à 95 % 0,28­0,88) et le risque de réintervention chez les patients traités par des chirurgiens dont le volume était très élevé était de 47 % de moins que chez les patients traités par des chirurgiens dont le volume était très bas (RR 0,53, IC à 95 % 0,30­0,93). Conclusion: Il ne semble y avoir aucun bienfait additionnel significatif au fait d'être opéré dans un centre où le volume d'interventions pour enclouage intramédullaire des fractures de la diaphyse tibiale est élevé. Il faudra approfondir la recherche sur les effets du volume chirurgical et de l'expérience clinique des établissements pour confirmer cette observation. Le risque de réintervention a été plus élevé chez les patients traités par des chirurgiens dont le volume d'interventions était très bas; cette observation pourrait être utilisée pour optimiser l'issue du traitement des fractures de la diaphyse tibiale. Enregistrement de l'essai clinique : ClinicalTrials. gov, NCT00038129.

Simulation study on the validity of the average risk approach in estimating population attributable fractions for continuous exposures.

BACKGROUND: The population attributable fraction (PAF) is an important metric for estimating disease burden associated with causal risk factors. In an International Agency for Research on Cancer working group report, an approach was introduced to estimate the PAF using the average of a continuous exposure and the incremental relative risk (RR) per unit. This 'average risk' approach has been subsequently applied in several studies conducted worldwide. However, no investigation of the validity of this method has been done. OBJECTIVE: To examine the validity and the potential magnitude of bias of the average risk approach. METHODS: We established analytically that the direction of the bias is determined by the shape of the RR function. We then used simulation models based on a variety of risk exposure distributions and a range of RR per unit. We estimated the unbiased PAF from integrating the exposure distribution and RR, and the PAF using the average risk approach. We examined the absolute and relative bias as the direct and relative difference in PAF estimated from the two approaches. We also examined the bias of the average risk approach using real-world data from the Canadian Population Attributable Risk of Cancer study. RESULTS: The average risk approach involves bias, which is underestimation or overestimation with a convex or concave RR function (a risk profile that increases more/less rapidly at higher levels of exposure). The magnitude of the bias is affected by the exposure distribution as well as the value of RR. This approach is approximately valid when the RR per unit is small or the RR function is approximately linear. The absolute and relative bias can both be large when RR is not small and the exposure distribution is skewed. CONCLUSIONS: We recommend that caution be taken when using the average risk approach to estimate PAF.

Estimation of diagnostic test accuracy: A "Rule of Three" for data with repeated observations but without a gold standard.

This article considers how to estimate the accuracy of a diagnostic test when there are repeated observations, but without the availability of a gold standard or reference test. We identify conditions under which the structure of the observed data is rich enough to provide sufficient degrees of freedom, such that a suitable latent class model can be fitted with identifiable accuracy parameters. We show that a Rule of Three applies, specifying that accuracy can be evaluated as long as there are at least three observations per individual with the given test. This rule also applies if the three observations arise from combinations of different test methods, or from a sequential design in which individuals are tested for a maximum number of times with the same test but stopping if a positive (or negative) result occurs. The rule pertains to tests having an arbitrary number of response categories. Accuracy is evaluated by parameters reflecting rates of misclassification among the response categories, and the model also provides estimates of the underlying distribution of the true disease state. These ideas are illustrated by data from two medical studies. Issues discussed include the advantages and disadvantages of analyzing the response variable as binary or multinomial, as well as the feasibility of testing goodness of fit when the model incorporates a large number of parameters. Comparisons are possible between models that do or do not assume equal accuracy rates for the observations, and between models where certain misclassification parameters are or are not assumed to be zero.

Estimating the future cancer management costs attributable to modifiable risk factors in Canada.

OBJECTIVES: An estimated 33-37% of incident cancers in Canada are attributable to modifiable risk factors. Interventions targeting these risk factors would minimize the substantial health and economic burdens Canadians face due to cancer. We estimate the future health and economic burden of cancer in Canada by incorporating data from the Canadian Population Attributable Risk of Cancer (ComPARe) study into OncoSim, a web-based microsimulation tool. METHODS: Using the integrated OncoSim population attributable risk and population impact measures, we evaluated risk factor-targeted intervention scenarios implemented in 2020, assuming the targeted risk factor prevalence reduction would be achieved by 2032 with a 12-year latency period. RESULTS: We estimate that smoking will be the largest contributor to cancer-related costs, with a cost of CAD $44.4 billion between 2032 and 2044. An estimated CAD $3.3 billion of the cost could be avoided with a 30% reduction in smoking prevalence by 2022. Following smoking, the next highest cancer management costs are associated with inadequate physical activity and excess body weight, accounting for CAD $10.7 billion ($2.7 billion avoidable) and CAD $9.8 billion ($3.2 billion avoidable), respectively. Avoidable costs for other risk factors range from CAD $90 million to CAD $2.5 billion. CONCLUSION: Interventions targeting modifiable cancer risk factors could prevent a substantial number of incident cancer cases and billions of dollars in cancer management costs. With limited budgets and rising costs in cancer care in Canada, these simulation models and results are valuable for researchers and policymakers to inform decisions and prioritize and evaluate intervention programs.

RéSUMé: OBJECTIFS: Il est estimé que de 33 % à 37 % des cancers incidents au Canada sont imputables à des facteurs de risque modifiables. Des interventions ciblant ces facteurs de risque réduiraient le fardeau sanitaire et économique considérable du cancer dans la population canadienne. Nous avons estimé le futur fardeau sanitaire et économique du cancer au Canada en intégrant les données de l'étude ComPARe (Canadian Population Attributable Risk of Cancer) dans l'outil de microsimulation en ligne OncoSim. MéTHODE: À l'aide des indicateurs d'impact dans la population et du risque attribuable dans la population intégrés dans OncoSim, nous avons évalué des scénarios d'intervention mis en œuvre en 2020 axés sur les facteurs de risque, en partant de l'hypothèse que la réduction de la prévalence des facteurs de risque ciblés serait atteinte d'ici 2032 avec une période de latence de 12 ans. RéSULTATS: Nous estimons que le tabagisme sera le facteur qui contribuera le plus aux coûts du cancer, avec un coût de 44,4 milliards $ CA entre 2032 et 2044. Il est estimé qu'une part de 3,3 milliards $ CA de ce coût pourrait être évitée en réduisant de 30 % la prévalence du tabagisme d'ici 2022. Après le tabagisme, les coûts de prise en charge du cancer les plus élevés sont associés à l'inactivité physique et au surpoids, qui représentent respectivement 10,7 milliard $ CA (dont 2,7 milliards $ évitables) et 9,8 milliards $ CA (dont 3,2 milliards $ évitables). Les coûts évitables pour d'autres facteurs de risque vont de 90 millions $ CA à 2,5 milliards $ CA. CONCLUSION: Des interventions ciblant les facteurs de risque de cancer modifiables pourraient prévenir un nombre considérable de cas de cancers incidents et épargner des milliards de dollars en coûts de prise en charge du cancer. Avec les budgets serrés et la hausse des coûts des soins du cancer au Canada, ces modèles de simulation et leurs résultats permettent aux chercheurs et aux responsables des politiques d'éclairer les décisions et de hiérarchiser et d'évaluer les programmes d'intervention.

Incorporating Portfolio Uncertainty in Decision Rules for Healthcare Resource Allocation.

Cost-effectiveness analysis is widely adopted as a means to inform policy and decision makers in setting priorities for healthcare resource allocation. In resource-constrained settings, decision makers are confronted with healthcare resource reallocation decisions, e.g., moving funds from one or more existing healthcare programs to fund new healthcare programs. The decision-making plane (DMP) has been developed as a means to graphically present the results of reallocating available healthcare resources when healthcare program costs and effects are uncertain. Mapping a value function over the DMP allows the analyst to value all possible combinations of net costs and net effects that may result from reallocating available healthcare resources under conditions of uncertainty. In this paper, we extend this approach to include a change in portfolio risk, stemming from a change in the portfolios of funded healthcare programs, as an additional source of uncertainty, and demonstrate how this can be incorporated into the value function over net costs and net effects for a risk-averse decision maker. The methodology presented in this paper is of particular interest to decision makers who are risk averse, as it will help to better incorporate their preferences in the process of deciding how to best allocate scarce healthcare resources.

Shifting the focus away from binary thinking of statistical significance and towards education for key stakeholders: revisiting the debate on whether it's time to de-emphasize or get rid of statistical significance.

There has been a long-standing controversy among scientists regarding the appropriate use of P-values and statistical significance in clinical research. This debate has resurfaced through recent calls to modify the threshold of P-value required to declare significance, or to retire statistical significance entirely. In this article, we revisit the issue by discussing: i) the connection between statistical thinking and evidence-based practice; ii) some history of statistical significance and P-values; iii) some practical challenges with statistical significance or P-value thresholds in clinical research; iv) the on-going debate on what to do with statistical significance; v) suggestions to shift the focus away from binary thinking of statistical significance and towards education for key stakeholders on research essentials including statistical thinking, critical thinking, good reporting, basic clinical research concepts and methods, and more. We then conclude with remarks and illustrations of the potential deleterious public health consequences of poor methods including selective choice of analysis approach and misguided reliance on binary use of P-values to report and interpret scientific findings.

The current burden of non-melanoma skin cancer attributable to ultraviolet radiation and related risk behaviours in Canada.

PURPOSE: Ultraviolet radiation (UVR) is an established cause of non-melanoma skin cancer (NMSC)-basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). The aim of this study was to estimate the current burden of BCC and SCC associated with UVR and modifiable UVR behaviours (sunburn, sunbathing, and indoor tanning) in Canada in 2015. METHODS: The current burden of BCC and SCC associated with UVR was estimated by comparing 2015 incidence rates with rates of less exposed body sites (trunk and lower limbs) after adjusting for estimated surface areas. The burden associated with modifiable UVR behaviours was estimated by using prevalence estimates among Caucasians from the Second National Sun Survey, and relative risks that are generalizable to Canadians from conducting meta-analyses of relevant studies. RESULTS: We estimated that 80.5% of BCCs and 83.0% of SCCs were attributable to UVR. Adult sunburn was associated with relative risks of 1.85 (95% CI 1.15-3.00) for BCC and 1.41 (95% CI 0.91-2.18) for SCC, while adult sunbathing was associated with relative risks of 1.82 (95% CI 1.52-2.17) for BCC and 1.14 (95% CI 0.53-2.46) for SCC. We estimated that 18.6% of BCCs and 9.9% of SCCs were attributable to adult sunburn, while 28.1% of BCCs were attributable to adult sunbathing. We estimated that 46.2% of BCCs and 17.3% of SCCs were attributable to modifiable UVR behaviours combined. CONCLUSION: Our results provide quantifiable estimates of the potentially avoidable burden of NMSCs among Canadians. These estimates can be used to motivate prevention efforts in Canada.

Effects of health literacy, screening, and participant choice on action plans for reducing unhealthy snacking in Australia: A randomised controlled trial.

BACKGROUND: Low health literacy is associated with poorer health outcomes. A key strategy to address health literacy is a universal precautions approach, which recommends using health-literate design for all health interventions, not just those targeting people with low health literacy. This approach has advantages: Health literacy assessment and tailoring are not required. However, action plans may be more effective when tailored by health literacy. This study evaluated the impact of health literacy and action plan type on unhealthy snacking for people who have high BMI or type 2 diabetes (Aim 1) and the most effective method of action plan allocation (Aim 2). METHODS AND FINDINGS: We performed a 2-stage randomised controlled trial in Australia between 14 February and 6 June 2019. In total, 1,769 participants (mean age: 49.8 years [SD = 11.7]; 56.1% female [n = 992]; mean BMI: 32.9 kg/m2 [SD = 8.7]; 29.6% self-reported type 2 diabetes [n = 523]) were randomised to 1 of 3 allocation methods (random, health literacy screening, or participant selection) and 1 of 2 action plans to reduce unhealthy snacking (standard versus literacy-sensitive). Regression analysis evaluated the impact of health literacy (Newest Vital Sign [NVS]), allocation method, and action plan on reduction in self-reported serves of unhealthy snacks (primary outcome) at 4-week follow-up. Secondary outcomes were perceived extent of unhealthy snacking, difficulty using the plans, habit strength, and action control. Analyses controlled for age, level of education, language spoken at home, diabetes status, baseline habit strength, and baseline self-reported serves of unhealthy snacks. Average NVS score was 3.6 out of 6 (SD = 2.0). Participants reported consuming 25.0 serves of snacks on average per week at baseline (SD = 28.0). Regarding Aim 1, 398 participants in the random allocation arm completed follow-up (67.7%). On average, people scoring 1 SD below the mean for health literacy consumed 10.0 fewer serves per week using the literacy-sensitive action plan compared to the standard action plan (95% CI: 0.05 to 19.5; p = 0.039), whereas those scoring 1 SD above the mean consumed 3.0 fewer serves using the standard action plan compared to the literacy-sensitive action plan (95% CI: -6.3 to 12.2; p = 0.529), although this difference did not reach statistical significance. In addition, we observed a non-significant action plan × health literacy (NVS) interaction (b = -3.25; 95% CI: -6.55 to 0.05; p = 0.054). Regarding Aim 2, 1,177 participants across the 3 allocation method arms completed follow-up (66.5%). There was no effect of allocation method on reduction of unhealthy snacking, including no effect of health literacy screening compared to participant selection (b = 1.79; 95% CI: -0.16 to 3.73; p = 0.067). Key limitations include low-moderate retention, use of a single-occasion self-reported primary outcome, and reporting of a number of extreme, yet plausible, snacking scores, which rendered interpretation more challenging. Adverse events were not assessed. CONCLUSIONS: In our study we observed nominal improvements in effectiveness of action plans tailored to health literacy; however, these improvements did not reach statistical significance, and the costs associated with such strategies compared with universal precautions need further investigation. This study highlights the importance of considering differential effects of health literacy on intervention effectiveness. TRIAL REGISTRATION: Australia and New Zealand Clinical Trial Registry ACTRN12618001409268.

Fragility of clinical trials across research fields: A synthesis of methodological reviews.

BACKGROUND: Randomized controlled trials (RCTs) are often used to inform clinical practice and it is desirable that their results be robust. A fragility index (FI), defined as the smallest number of participants in whom an outcome change from non-event to event would turn a statistically significant result to a non-significant result, can be computed to measure robustness. We sought to determine the distribution of fragility indices across various research areas and summarized the factors associated with fragility. METHODS: We searched PubMed between February 2014 and May 2019 and included reviews that reported on fragility indices and the associated factors. Two investigators independently screened articles for eligibility and extracted all relevant data from each review. Fragility indices were pooled using random effects meta-analysis. RESULTS: Twenty-four (24) reviews met the inclusion criteria. They contained a median of 41 trials (first quartile [Q1]-third quartile [Q3]: 17-120). The overall mean FI across different fields of research was 4 (95% confidence interval [CI] 3-5), indicating a high level of fragility. Higher journal impact factor, larger sample size, bigger effect size, more outcome events, a lower p-value, and adequate allocation concealment were reported to be associated with the higher FI. The ecological correlation between median FI and median sample size (22 studies) was 0.95 (95% CI 0.58-0.99). CONCLUSION: Trials across various fields of research are frequently fragile. We also identified some factors associated with fragility. Researchers should consider strategies to enhance the robustness of studies and minimize fragility.

Correction to: Estimation of treatment preference effects in clinical trials when some participants are indifferent to treatment choice.

In the original publication of this article [1], the number "- 0.49" in the below sentence in the Results section should be changed to "-3.23", and this typo does not affect the wider conclusions.

The fragility of trial results involves more than statistical significance alone.

OBJECTIVES: The fragility of clinical trial findings has been previously defined as the number of changes in outcomes that are required to change their statistical significance. We show that reliance on statistical significance alone provides only a limited and potentially misleading perspective, and an enhanced approach is developed. METHODS: Clinical importance of trial results and their quantitative stability are incorporated into an enhanced framework to assess fragility. RESULTS: Examples show that the small data changes required to affect statistical significance may actually be unlikely to occur. Recognizing this limitation, and because statistical significance conveys no information about the treatment effect size, our approach additionally takes into account the clinical importance of the results and their quantitative stability. The interpretation of studies with various combinations of these features is described. CONCLUSION: The concept of fragility should include clinical importance of trial findings and their quantitative stability, as well as statistical significance. Study results should be declared as stable only if they are statistically significant and quantitatively stable, but they can be either clinically important or unimportant; otherwise, the findings should be declared as unstable, or fragile.