scholarly journals Sample Size Calculations for Variant Surveillance in the Presence of Biological and Systematic Biases

2022 ◽  
Author(s):  
Shirlee Wohl ◽  
Elizabeth C Lee ◽  
Bethany L DiPrete ◽  
Justin Lessler
Keyword(s):  
Sample Size ◽  
Disease Outbreaks ◽  
Sample Sizes ◽  
Cross Sectional ◽  
Continuous Sampling ◽  
Infectious Disease Outbreaks ◽  
Sample Size Calculations ◽  
Surveillance Programs ◽  
Systematic Biases ◽  
Pathogen Genome

As demonstrated during the SARS-CoV-2 pandemic, detecting and tracking the emergence and spread of pathogen variants is an important component of monitoring infectious disease outbreaks. Pathogen genome sequencing has emerged as the primary tool for variant characterization, so it is important to consider the number of sequences needed when designing surveillance programs or studies, both to ensure accurate conclusions and to optimize use of limited resources. However, current approaches to calculating sample size for variant monitoring often do not account for the biological and logistical processes that can bias which infections are detected and which samples are ultimately selected for sequencing. In this manuscript, we introduce a framework that models the full process from infection detection to variant characterization and demonstrate how to use this framework to calculate appropriate sample sizes for sequencing-based surveillance studies. We consider both cross-sectional and continuous sampling, and we have implemented our method in a publicly available tool that allows users to estimate necessary sample sizes given a specific aim (e.g., variant detection or measuring variant prevalence) and sampling method. Our framework is designed to be easy to use, while also flexible enough to be adapted to other pathogens and surveillance scenarios.

scholarly journals The required size of cluster randomized trials of non-pharmaceutical interventions in epidemic settings

2021 ◽  
Author(s):  
Justin K Sheen ◽  
Johannes Haushofer ◽  
C. Jessica E. Metcalf ◽  
Lee Kennedy-Shaffer
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Disease Outbreaks ◽  
Cluster Randomized Trials ◽  
Sample Sizes ◽  
True Effect Size ◽  
Infectious Disease Outbreaks ◽  
True Effect ◽  
Cluster Randomized ◽  
Pharmaceutical Interventions

To control the SARS-CoV-2 pandemic and future pathogen outbreaks requires an understanding of which non-pharmaceutical interventions are effective at reducing transmission. Observational studies, however, are subject to biases, even when there is no true effect. Cluster randomized trials provide a means to conduct valid hypothesis tests of the effect of interventions on community transmission. While they may only require a short duration, they often require large sample sizes to achieve adequate power. However, the sample sizes required for such tests in an outbreak setting are largely undeveloped and the question of whether these designs are practical remains unanswered. We develop approximate sample size formulae and simulation-based sample size methods for cluster randomized trials in infectious disease outbreaks. We highlight key relationships between characteristics of transmission and the enrolled communities and the required sample sizes, describe settings where cluster randomized trials powered to detect a meaningful true effect size may be feasible, and provide recommendations for investigators in planning such trials. The approximate formulae and simulation banks may be used by investigators to quickly assess the feasibility of a trial, and then more detailed methods may be used to more precisely size the trial. For example, we show that community-scale trials requiring 220 clusters with 100 tested individuals per cluster are powered to identify interventions that reduce transmission by 40% in one generation interval, using parameters identified for SARS-CoV-2 transmission. For more modest treatment effects, or settings with extreme overdispersion of transmission, however, much larger sample sizes are required.

Control arm heterogeneity in trials for unselected advanced triple-negative breast cancer (aTNBC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1082-1082
Author(s):  
Kinisha Gala ◽  
Ankit Kalucha ◽  
Samuel Martinet ◽  
Anushri Goel ◽  
Kalpana Devi Narisetty ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Single Agent ◽  
Progression Free Survival ◽  
Sample Sizes ◽  
Time To Event ◽  
Sample Size Calculations ◽  
Subgroup Analyses ◽  
Combination Regimens

1082 Background: Primary endpoints of clinical trials frequently include subgroup-analyses. Several solid cancers such as aTNBC are heterogeneous, which can lead to unpredictable control arm performance impairing accurate assumptions for sample size calculations. We explore the value of a comprehensive clinical trial results repository in assessing control arm heterogeneity with aTNBC as the pilot. Methods: We identified P2/3 trials reporting median overall survival (mOS) and/or median progression-free survival (mPFS) in unselected aTNBC through a systematic search of PubMed, clinical trials databases and conference proceedings. Trial arms with sample sizes ≤25 or evaluating drugs no longer in development were excluded. Due to inconsistency among PD-L1 assays, PD-L1 subgroup analyses were not assessed separately. The primary aim was a descriptive analysis of control arm mOS and mPFS across all randomized trials in first line (1L) aTNBC. Secondary aims were to investigate time-to-event outcomes in control arms in later lines and to assess time-trends in aTNBC experimental and control arm outcomes. Results: We included 33 trials published between June 2013-Feb 2021. The mOS of control arms in 1L was 18.7mo (range 12.6-22.8) across 5 trials with single agent (nab-) paclitaxel [(n)P], and 18.1mo (similar range) for 7 trials including combination regimens (Table). The mPFS of control arms in 1L was 4.9mo (range 3.8-5.6) across 5 trials with single-agent (n)P, and 5.6mo (range 3.8-6.1) across 8 trials including combination regimens. Control arm mOS was 13.1mo (range 9.4-17.4) for 3 trials in first and second line (1/2L) and 8.7mo (range 6.7-10.8) across 5 trials in 2L and beyond. R2 for the mOS best-fit lines across control and experimental arms over time was 0.09, 0.01 and 0.04 for 1L, 1/2L and 2L and beyond, respectively. Conclusions: Median time-to-event outcomes of control arms in 1L aTNBC show considerable heterogeneity, even among trials with comparable regimens and large sample sizes. Disregarding important prognostic factors at stratification can lead to imbalances between arms, which may jeopardize accurate sample size calculations, trial results and interpretation. Optimizing stratification and assumptions for power calculations is of utmost importance in aTNBC and beyond. A digitized trial results repository with precisely defined patient populations and treatment settings could improve accuracy of assumptions during clinical trial design.[Table: see text]

scholarly journals Social distancing in America: Understanding long-term adherence to COVID-19 mitigation recommendations

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257945
Author(s):  
Christopher P. Reinders Folmer ◽  
Megan A. Brownlee ◽  
Adam D. Fine ◽  
Emmeke B. Kooistra ◽  
Malouke E. Kuiper ◽  
...  
Keyword(s):  
Social Norms ◽  
Disease Outbreaks ◽  
The United States ◽  
Mitigation Measures ◽  
Personal Factors ◽  
Cross Sectional ◽  
Social Distancing ◽  
Infectious Disease Outbreaks ◽  
Practical Capacity ◽  
Major Factors

A crucial question in the governance of infectious disease outbreaks is how to ensure that people continue to adhere to mitigation measures for the longer duration. The present paper examines this question by means of a set of cross-sectional studies conducted in the United States during the COVID-19 pandemic, in May, June, and July of 2020. Using stratified samples that mimic the demographic characteristics of the U.S. population, it seeks to understand to what extent Americans continued to adhere to social distancing measures in the period after the first lockdown ended. Moreover, it seeks to uncover which variables sustained (or undermined) adherence across this period. For this purpose, we examined a broad range of factors, relating to people’s (1) knowledge and understanding of the mitigation measures, (2) perceptions of their costs and benefits, (3) perceptions of legitimacy and procedural justice, (4) personal factors, (5) social environment, and (6) practical circumstances. Our findings reveal that adherence was chiefly shaped by three major factors: respondents adhered more when they (a) had greater practical capacity to adhere, (b) morally agreed more with the measures, and (c) perceived the virus as a more severe health threat. Adherence was shaped to a lesser extent by impulsivity, knowledge of social distancing measures, opportunities for violating, personal costs, and descriptive social norms. The results also reveal, however, that adherence declined across this period, which was partly explained by changes in people’s moral alignment, threat perceptions, knowledge, and perceived social norms. These findings show that adherence originates from a broad range of factors that develop dynamically across time. Practically these insights help to improve pandemic governance, as well as contributing theoretically to the study of compliance and the way that rules come to shape behavior.

scholarly journals Impacts of Sample Size and Quality-Adjusted Imputed Prices on Own-Price Elasticities Estimated Using Cross-Sectional Data

2003 ◽  
Vol 35 (2) ◽  
pp. 415-421
Author(s):  
Matthew C. Stockton
Keyword(s):  
Sample Size ◽  
Data Sets ◽  
Sample Sizes ◽  
Food Category ◽  
Cross Sectional ◽  
Data Set ◽  
Food Away From Home ◽  
Price Elasticities ◽  
Quality Adjustment

Cross-sectional data sets containing expenditure and quantity information are typically used to calculate quality-adjusted imputed prices. Do sample size and quality adjustment of price statistically alter estimates for own-price elasticities? This paper employs a data set pertaining to three food categories—pork, cheese, and food away from home—with four sample sizes for each food category. Twelve sample sizes were used for both adjusted and unadjusted prices to derive elasticities. No statistical differences were found between own-price elasticities among sample sizes. However, elasticities that were based on adjusted price imputations were significantly different from those that were based on unadjusted prices.

scholarly journals Using simulated infectious disease outbreaks to inform site selection and sample size for individually randomized vaccine trials during an ongoing epidemic

2021 ◽  
pp. 174077452110288
Author(s):  
Zachary J Madewell ◽  
Ana Pastore Y Piontti ◽  
Qian Zhang ◽  
Nathan Burton ◽  
Yang Yang ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Sample Size ◽  
Site Selection ◽  
Statistical Models ◽  
Vaccine Trial ◽  
Disease Outbreaks ◽  
Optimal Number ◽  
Transmission Model ◽  
Infectious Disease Outbreaks ◽  
Spatially Resolved

Background: Novel strategies are needed to make vaccine efficacy trials more robust given uncertain epidemiology of infectious disease outbreaks, such as arboviruses like Zika. Spatially resolved mathematical and statistical models can help investigators identify sites at highest risk of future transmission and prioritize these for inclusion in trials. Models can also characterize uncertainty in whether transmission will occur at a site, and how nearby or connected sites may have correlated outcomes. A structure is needed for how trials can use models to address key design questions, including how to prioritize sites, the optimal number of sites, and how to allocate participants across sites. Methods: We illustrate the added value of models using the motivating example of Zika vaccine trial planning during the 2015–2017 Zika epidemic. We used a stochastic, spatially resolved, transmission model (the Global Epidemic and Mobility model) to simulate epidemics and site-level incidence at 100 high-risk sites in the Americas. We considered several strategies for prioritizing sites (average site-level incidence of infection across epidemics, median incidence, probability of exceeding 1% incidence), selecting the number of sites, and allocating sample size across sites (equal enrollment, proportional to average incidence, proportional to rank). To evaluate each design, we stochastically simulated trials in each hypothetical epidemic by drawing observed cases from site-level incidence data. Results: When constraining overall trial size, the optimal number of sites represents a balance between prioritizing highest-risk sites and having enough sites to reduce the chance of observing too few endpoints. The optimal number of sites remained roughly constant regardless of the targeted number of events, although it is necessary to increase the sample size to achieve the desired power. Though different ranking strategies returned different site orders, they performed similarly with respect to trial power. Instead of enrolling participants equally from each site, investigators can allocate participants proportional to projected incidence, though this did not provide an advantage in our example because the top sites had similar risk profiles. Sites from the same geographic region may have similar outcomes, so optimal combinations of sites may be geographically dispersed, even when these are not the highest ranked sites. Conclusion: Mathematical and statistical models may assist in designing successful vaccination trials by capturing uncertainty and correlation in future transmission. Although many factors affect site selection, such as logistical feasibility, models can help investigators optimize site selection and the number and size of participating sites. Although our study focused on trial design for an emerging arbovirus, a similar approach can be made for any infectious disease with the appropriate model for the particular disease.

scholarly journals Validity of sample sizes in publications of randomised controlled trials on the treatment of age-related macular degeneration: cross-sectional evaluation

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e030312
Author(s):  
Sabrina Tulka ◽  
Berit Geis ◽  
Christine Baulig ◽  
Stephanie Knippschild ◽  
Frank Krummenauer
Keyword(s):  
Macular Degeneration ◽  
Sample Size ◽  
Primary Endpoint ◽  
Sample Size Calculation ◽  
Age Related Macular Degeneration ◽  
Cross Sectional ◽  
Age Related ◽  
Number Of Patients ◽  
Sample Size Calculations ◽  
Randomised Controlled

ObjectiveThe aim of this cross-sectional study was to examine the completeness and accuracy of the reporting of sample size calculations in randomised controlled trial (RCT) publications on the treatment of age-related macular degeneration (AMD).MethodsA sample of 97 RCTs published between 2004 and 2014 was reviewed for the calculation of their sample size. It was examined whether a (complete) description of the sample size calculation was presented. Furthermore, the sample size was recalculated, whenever possible based on the published details, in order to verify the reported number of patients.Primary outcome measureThe primary endpoint of this cross-sectional investigation was a described sample size calculation that was reproducible, complete and correct (maximum tolerated deviation between reported and replicated sample size ±2 participants per trial arm).ResultsA total of 50 publications (52%) did not provide any information on the justification of the number of patients included. Only 17 publications (18%) provided all the necessary parameters for recalculation; 8 of 97 (8%, 95%-CI: 4% to 16%) publications achieved the primary endpoint. The median relative deviation between reported and recalculated sample sizes was 1%, with a range from −43% to +66%.ConclusionAlthough a transparent sample size legitimation is a crucial determinant of an RCT’s methodological validity, more than half of the RCT publications considered failed to report them. Furthermore, reported sample size legitimations were often incomplete or incorrect. In summary, clinical authors should pay more attention to the transparent reporting of sample size calculation, and clinical journal reviewers may opt to reproduce reported sample size calculations.SynopsisMore than half of the analysed RCT publications on the treatment of AMD did not report a transparent sample size calculation. Only 8% reported a complete and correct sample size calculation.

Knowledge and Perception of COVID-19 among Healthcare Professionals in the United Arab Emirates: A Cross-sectional Online Survey

2020 ◽  
Vol 01 ◽  
Author(s):  
Akshaya Srikanth Bhagavathula ◽  
Abdullah Shehab
Keyword(s):  
United Arab Emirates ◽  
Online Survey ◽  
Disease Outbreaks ◽  
Educational Interventions ◽  
Cross Sectional Study ◽  
Sources Of Information ◽  
Cross Sectional ◽  
Infectious Disease Outbreaks ◽  
Government Websites ◽  
And Control

Background: During the first week of March, a large number of cases of COVID-19 were reported across the world including the UAE. Aim: To assess the knowledge and perceptions of COVID-19 among HCPs in the UAE. Methods: During the first week of March, a cross-sectional study was conducted among EMA HCPs. A 23-item survey questionnaire on knowledge and perceptions of COVID-19, including specific questions related to the different sources of information was used. Each correct response was scored as “1” and wrong as “0”. The sum of the knowledge scores ≤4 out of 7 was considered as poor knowledge and the sum of the perception scores (score >5 out of 7) as a positive perception. Results: A total of 353 HCPs completed the study and half of them were male (n=178; 50.4%), doctors (n=257;72.8%), and aged between 35-44 years (n=116; 32.9%). Although most of the participants were aware of COVID-19 (n=350; 99.2%), only a limited (n=168;47.6%) proportion of them got the opportunity to attend lectures/discussions related to COVID-19. Government websites (43.1%) and news bulletins (36%) were the primary sources for COVID-19 information. HCPs' knowledge about COVID-19 was found to be satisfactory (58.4%) and their perceptions were positive (78.5%). Conclusion: As the number of COVID-19 cases is consistently increasing in the UAE, it is important to improve the level of knowledge and perceptions among HCPs. Educational interventions focusing on prevention and control of COVID-19 should be prioritized to empower HCPs in infectious disease outbreaks.

COVID-19 Turning Threats into Opportunities: Knowledge and Attitude of Physicians Toward Relative Advantages of Telemedicine (Preprint)

2020 ◽  
Author(s):  
Alaeddin Mohammad Ahmad ◽  
Hadeel Omar Khalil ◽  
Lina Sameeh Al Momani ◽  
Taghreed Khirfan
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Disease Outbreaks ◽  
Relative Advantage ◽  
Equation Modeling ◽  
Cross Sectional ◽  
Policy Makers ◽  
Infectious Disease Outbreaks ◽  
Validated Questionnaire ◽  
Knowledge And Attitude

UNSTRUCTURED This study aimed to assess the knowledge and attitude of physicians toward telemedicine, examine the readiness of organizational aspects of telemedicine application, and investigate the knowledge and attitude of physicians toward telemedicine relative advantage moderated by organizational aspects during infectious disease outbreaks such as COVID-19. A cross-sectional descriptive analytical study was done using a validated questionnaire distributed to a purposive sample of 320 Jordanian physicians. Structural equation modeling was done using AMOS 22.0. Results supported a claim of good fit of the structural model and revealed a positive and significant effect of both knowledge and attitude on relative advantage of telemedicine. Moreover, there was a significant impact of organizational aspects as a moderator of the relation between knowledge and attitude and the relative advantages of telemedicine. These results contributed to existing literature and are beneficial to policy makers and practitioners at the healthcare sectors. Keywords: Knowledge, Attitude, Relative Advantages, Telemedicine, Physicians, COVID-19

scholarly journals Understanding the risks for post-disaster infectious disease outbreaks: a systematic review protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e039608
Author(s):  
Gina E C Charnley ◽  
Ilan Kelman ◽  
Katy Gaythorpe ◽  
Kris Murray
Keyword(s):  
Infectious Disease ◽  
Risk Factors ◽  
Systematic Review ◽  
Single Case ◽  
Meta Analysis ◽  
Disease Outbreaks ◽  
Publication Date ◽  
Cross Sectional ◽  
Infectious Disease Outbreaks ◽  
Post Disaster

IntroductionDisasters have many forms, including those related to natural hazards and armed conflict. Human-induced global change, such as climate change, may alter hazard parameters of these disasters. These alterations can have serious consequences for vulnerable populations, which often experience post-disaster infectious disease outbreaks, leading to morbidity and mortality. The risks and drivers for these outbreaks and their ability to form cascades are somewhat contested. Despite evidence for post-disaster outbreaks, reviews quantifying them have been on short time scales, specific geographic areas or specific hazards. This review aims to fill this gap and gain a greater understanding of the risk factors involved in these contextual outbreaks on a global level.Methods and analysisUsing the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist and Khan’s methodological framework, a systematic search strategy will be created and carried out in August 2020. The strategy will search MEDLINE, Embase and GlobalHealth electronic databases and reference lists of selected literature will also be screened. Eligible studies will include any retrospective cross-sectional, case–control or cohort studies investigating an infectious disease outbreak in a local disaster affected population. Studies will not be excluded based on geographic area or publication date. Excluded papers will include non-English studies, reviews, single case studies and research discussing general risk factors, international refugee camps, public health, mental health and other non-communicable diseases, pathogen genetics or economics. Following selection, data will be extracted into a data charting form, that will be reviewed by other members of the team. The data will then be analysed both numerically and narratively.Ethics and disseminationOnly secondary data will be used and there will be no public or patient involvement; therefore, no ethical approval is needed. Our findings will aim to be disseminated through a peer-reviewed journal. The authors intend to use the results to inform future mathematical modelling studies.

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