Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 59
Filter
1.
Stud Health Technol Inform ; 290: 617-621, 2022 Jun 06.
Article in English | MEDLINE | ID: covidwho-1879414

ABSTRACT

Sample size is an important indicator of the power of randomized controlled trials (RCTs). In this paper, we designed a total sample size extractor using a combination of syntactic and machine learning methods, and evaluated it on 300 Covid-19 abstracts (Covid-Set) and 100 generic RCT abstracts (General-Set). To improve the performance, we applied transfer learning from a large public corpus of annotated abstracts. We achieved an average F1 score of 0.73 on the Covid-Set testing set, and 0.60 on the General-Set using exact matches. The F1 scores for loose matches on both datasets were over 0.74. Compared with the state-of-the-art tool, our extractor reports total sample sizes directly and improved F1 scores by at least 4% without transfer learning. We demonstrated that transfer learning improved the sample size extraction accuracy and minimized human labor on annotations.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Machine Learning , Natural Language Processing , Randomized Controlled Trials as Topic , Sample Size
2.
PLoS One ; 17(6): e0269420, 2022.
Article in English | MEDLINE | ID: covidwho-1879322

ABSTRACT

BACKGROUND: Child growth in populations is commonly characterised by cross-sectional surveys. These require data collection from large samples of individuals across age ranges spanning 1-20 years. Such surveys are expensive and impossible in restrictive situations, such as, e.g. the COVID pandemic or limited size of isolated communities. A method allowing description of child growth based on small samples is needed. METHODS: Small samples of data (N~50) for boys and girls 6-20 years old from different socio-economic situations in Africa and Europe were randomly extracted from surveys of thousands of children. Data included arm circumference, hip width, grip strength, height and weight. Polynomial regressions of these measurements on age were explored. FINDINGS: Polynomial curves based on small samples correlated well (r = 0.97 to 1.00) with results of surveys of thousands of children from same communities and correctly reflected sexual dimorphism and socio-economic differences. CONCLUSIONS: Fitting of curvilinear regressions to small data samples allows expeditious assessment of child growth in a number of characteristics when situations change rapidly, resources are limited and access to children is restricted.


Subject(s)
COVID-19 , Child Development , Adolescent , COVID-19/epidemiology , Child , Cross-Sectional Studies , Female , Humans , Male , Sample Size , Surveys and Questionnaires , Young Adult
3.
Trials ; 23(1): 361, 2022 Apr 27.
Article in English | MEDLINE | ID: covidwho-1817238

ABSTRACT

The CLARITY trial (Controlled evaLuation of Angiotensin Receptor Blockers for COVID-19 respIraTorY disease) is a two-arm, multi-centre, randomised controlled trial being run in India and Australia that investigates the effectiveness of angiotensin receptor blockers in addition to standard care compared to placebo (in Indian sites) with standard care in reducing the duration and severity of lung failure in patients with COVID-19. The trial was designed as a Bayesian adaptive sample size trial with regular planned analyses where pre-specified decision rules will be assessed to determine whether the trial should be stopped due to sufficient evidence of treatment effectiveness or futility. Here, we describe the statistical analysis plan for the trial and define the pre-specified decision rules, including those that could lead to the trial being halted. The primary outcome is clinical status on a 7-point ordinal scale adapted from the WHO Clinical Progression scale assessed at day 14. The primary analysis will follow the intention-to-treat principle. A Bayesian adaptive trial design was selected because there is considerable uncertainty about the extent of potential benefit of this treatment.Trial registrationClinicalTrials.gov NCT04394117 . Registered on 19 May 2020Clinical Trial Registry of India CTRI/2020/07/026831Version and revisionsVersion 1.0. No revisions.


Subject(s)
COVID-19 , Respiratory Tract Diseases , Angiotensin Receptor Antagonists/adverse effects , Bayes Theorem , COVID-19/drug therapy , Data Interpretation, Statistical , Humans , Sample Size
4.
Int J Environ Res Public Health ; 19(9)2022 04 27.
Article in English | MEDLINE | ID: covidwho-1809919

ABSTRACT

The purpose of this paper is to develop a multiple dependent state (MDS) sampling plan based on time-truncated sampling schemes for the daily number of cases of the coronavirus disease COVID-19 using gamma distribution under indeterminacy. The proposed sampling scheme parameters include average sample number (ASN) and accept and reject sample numbers when the indeterminacy parameter is known. In addition to the parameters of the proposed sampling schemes, the resultant tables are provided for different known indeterminacy parametric values. The outcomes resulting from various sampling schemes show that the ASN decreases as indeterminacy values increase. This shows that the indeterminacy parameter plays a vital role for the ASN. A comparative study between the proposed sampling schemes and existing sampling schemes based on indeterminacy is also discussed. The projected sampling scheme is illustrated with the help of the daily number of cases of COVID-19 data. From the results and real example, we conclude that the proposed MDS sampling scheme under indeterminacy requires a smaller sample size compared to the single sampling plan (SSP) and the existing MDS sampling plan.


Subject(s)
COVID-19 , COVID-19/diagnosis , COVID-19/epidemiology , Humans , Sample Size
5.
PLoS One ; 17(3): e0263679, 2022.
Article in English | MEDLINE | ID: covidwho-1742002

ABSTRACT

BACKGROUND: Reported cases of COVID-19 may be underestimated due to mild or asymptomatic cases and a low testing rate in the general population. RESEARCH QUESTION: What is the seroprevalence of SARS-CoV-2 infection in the general population and how it compares with the data on SARS-CoV-2 cases reported by a national health surveillance system (SNVS 2.0). STUDY DESIGN AND METHODS: This was a population-based, seroepidemiological, cross-sectional study in the city of Puerto Madryn, a middle size city in the Province of Chubut, Argentina. The study period was between March 3 and April 17, 2021. The sample size was calculated using the technique of calculation of confidence intervals for a proportion. Participants were selected using stratified and cluster probability sampling. A total of 1405 subjects were invited to participate in the study. Participants were divided into the following four age groups: 1) 0 to 14, 2) 15 to 39, 3) 40 to 64, and 4) 65 or older. After informed consent was obtained, a blood sample was taken by puncture of the fingertip, and a structured questionnaire was administered to evaluate demographics, socioeconomic status, level of education, comorbidities and symptoms suggestive of COVID-19. COVID-19 seroprevalence was documented using an immunoenzymatic test for the in vitro detection of IgG antibodies specific to the spike protein of SARS-CoV-2. RESULTS: A total of 987 participants completed the survey. Seropositivity in the full study population was 39,2% and in those under 15 years of age, 47.1%. Cases reported by the SNSV 2.0 amounted to 9.35% of the total population and 1.4% of those under 15 years of age. INTERPRETATION: The prevalence of COVID-19 infection in the general population is four times higher than the number of cases reported by the SNVS 2.0 in the city of Puerto Madryn. For each child under the age of 15 identified by the SNVS 2.0 with COVID-19, there are more than 30 unrecognized infections. Seroepidemiological studies are important to define the real extent of SARS-CoV-2 infection in a particular community. Children may play a significant role in the progression of the current pandemic.


Subject(s)
Antibodies, Viral/blood , COVID-19/epidemiology , Immunoglobulin G/blood , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Adolescent , Adult , Age Distribution , Aged , Argentina/epidemiology , COVID-19/blood , Child , Child, Preschool , Cross-Sectional Studies , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Prevalence , Sample Size , Seroepidemiologic Studies , Young Adult
6.
Stat Med ; 41(13): 2466-2482, 2022 Jun 15.
Article in English | MEDLINE | ID: covidwho-1729208

ABSTRACT

To control the SARS-CoV-2 pandemic and future pathogen outbreaks requires an understanding of which nonpharmaceutical interventions are effective at reducing transmission. Observational studies, however, are subject to biases that could erroneously suggest an impact on transmission, even when there is no true effect. Cluster randomized trials permit valid hypothesis tests of the effect of interventions on community transmission. While such trials could be completed in a relatively short period of time, they might require large sample sizes to achieve adequate power. However, the sample sizes required for such tests in outbreak settings are largely undeveloped, leaving unanswered the question of whether these designs are practical. 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 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, followed by more detailed methods 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 when transmission is extremely overdispersed, however, much larger sample sizes are required.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Pandemics/prevention & control , Randomized Controlled Trials as Topic , Sample Size
8.
Nat Genet ; 54(2): 121-124, 2022 02.
Article in English | MEDLINE | ID: covidwho-1637651

ABSTRACT

Using online surveys, we collected data regarding COVID-19-related loss of smell or taste from 69,841 individuals. We performed a multi-ancestry genome-wide association study and identified a genome-wide significant locus in the vicinity of the UGT2A1 and UGT2A2 genes. Both genes are expressed in the olfactory epithelium and play a role in metabolizing odorants. These findings provide a genetic link to the biological mechanisms underlying COVID-19-related loss of smell or taste.


Subject(s)
Ageusia/genetics , Anosmia/genetics , COVID-19/genetics , Genetic Loci , Genome-Wide Association Study , Glucuronosyltransferase/genetics , /genetics , Adult , Aged , Ageusia/enzymology , Anosmia/enzymology , Female , Humans , Male , Middle Aged , Phenotype , Polymorphism, Single Nucleotide/genetics , Sample Size
10.
Biom J ; 64(4): 681-695, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1565174

ABSTRACT

In Bayesian inference, prior distributions formalize preexperimental information and uncertainty on model parameters. Sometimes different sources of knowledge are available, possibly leading to divergent posterior distributions and inferences. Research has been recently devoted to the development of sample size criteria that guarantee agreement of posterior information in terms of credible intervals when multiple priors are available. In these articles, the goals of reaching consensus and evidence are typically kept separated. Adopting a Bayesian performance-based approach, the present article proposes new sample size criteria for superiority trials that jointly control the achievement of both minimal evidence and consensus, measured by appropriate functions of the posterior distributions. We develop both an average criterion and a more stringent criterion that accounts for the entire predictive distributions of the selected measures of minimal evidence and consensus. Methods are developed and illustrated via simulation for trials involving binary outcomes. A real clinical trial example on Covid-19 vaccine data is presented.


Subject(s)
COVID-19 Vaccines , COVID-19 , Bayes Theorem , Consensus , Humans , Research Design , Sample Size
11.
Nature ; 600(7890): 695-700, 2021 12.
Article in English | MEDLINE | ID: covidwho-1562062

ABSTRACT

Surveys are a crucial tool for understanding public opinion and behaviour, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the effect of survey bias: an instance of the Big Data Paradox1. Here we demonstrate this paradox in estimates of first-dose COVID-19 vaccine uptake in US adults from 9 January to 19 May 2021 from two large surveys: Delphi-Facebook2,3 (about 250,000 responses per week) and Census Household Pulse4 (about 75,000 every two weeks). In May 2021, Delphi-Facebook overestimated uptake by 17 percentage points (14-20 percentage points with 5% benchmark imprecision) and Census Household Pulse by 14 (11-17 percentage points with 5% benchmark imprecision), compared to a retroactively updated benchmark the Centers for Disease Control and Prevention published on 26 May 2021. Moreover, their large sample sizes led to miniscule margins of error on the incorrect estimates. By contrast, an Axios-Ipsos online panel5 with about 1,000 responses per week following survey research best practices6 provided reliable estimates and uncertainty quantification. We decompose observed error using a recent analytic framework1 to explain the inaccuracy in the three surveys. We then analyse the implications for vaccine hesitancy and willingness. We show how a survey of 250,000 respondents can produce an estimate of the population mean that is no more accurate than an estimate from a simple random sample of size 10. Our central message is that data quality matters more than data quantity, and that compensating the former with the latter is a mathematically provable losing proposition.


Subject(s)
COVID-19 Vaccines/administration & dosage , Health Care Surveys , Vaccination/statistics & numerical data , Benchmarking , Bias , Big Data , COVID-19/epidemiology , COVID-19/prevention & control , Centers for Disease Control and Prevention, U.S. , Datasets as Topic/standards , Female , Health Care Surveys/standards , Humans , Male , Research Design , Sample Size , Social Media , United States/epidemiology , /statistics & numerical data
13.
BMC Med Res Methodol ; 21(1): 229, 2021 10 25.
Article in English | MEDLINE | ID: covidwho-1484301

ABSTRACT

BACKGROUND: This research work is elaborated investigation of COVID-19 data for Weibull distribution under indeterminacy using time truncated repetitive sampling plan. The proposed design parameters like sample size, acceptance sample number and rejection sample number are obtained for known indeterminacy parameter. METHODS: The plan parameters and corresponding tables are developed for specified indeterminacy parametric values. The conclusion from the outcome of the proposed design is that when indeterminacy values increase the average sample number (ASN) reduces. RESULTS: The proposed repetitive sampling plan methodology application is given using COVID-19 data belong to Italy. The efficiency of the proposed sampling plan is compared with the existing sampling plans. CONCLUSIONS: Using the tables and COVID-19 data illustration, it is concluded that the proposed plan required a smaller sample size as examined with the available sampling plans in the literature.


Subject(s)
COVID-19 , Humans , Italy , SARS-CoV-2 , Sample Size , Statistical Distributions
14.
Clin Infect Dis ; 73(3): e842, 2021 08 02.
Article in English | MEDLINE | ID: covidwho-1459353
16.
J Am Med Inform Assoc ; 28(8): 1777-1784, 2021 07 30.
Article in English | MEDLINE | ID: covidwho-1447598

ABSTRACT

OBJECTIVE: We propose a bidirectional GPS imputation method that can recover real-world mobility trajectories even when a substantial proportion of the data are missing. The time complexity of our online method is linear in the sample size, and it provides accurate estimates on daily or hourly summary statistics such as time spent at home and distance traveled. MATERIALS AND METHODS: To preserve a smartphone's battery, GPS may be sampled only for a small portion of time, frequently <10%, which leads to a substantial missing data problem. We developed an algorithm that simulates an individual's trajectory based on observed GPS location traces using sparse online Gaussian Process to addresses the high computational complexity of the existing method. The method also retains the spherical geometry of the problem, and imputes the missing trajectory in a bidirectional fashion with multiple condition checks to improve accuracy. RESULTS: We demonstrated that (1) the imputed trajectories mimic the real-world trajectories, (2) the confidence intervals of summary statistics cover the ground truth in most cases, and (3) our algorithm is much faster than existing methods if we have more than 3 months of observations; (4) we also provide guidelines on optimal sampling strategies. CONCLUSIONS: Our approach outperformed existing methods and was significantly faster. It can be used in settings in which data need to be analyzed and acted on continuously, for example, to detect behavioral anomalies that might affect treatment adherence, or to learn about colocations of individuals during an epidemic.


Subject(s)
Algorithms , Research Design , Humans , Normal Distribution , Sample Size
17.
PLoS One ; 16(9): e0257878, 2021.
Article in English | MEDLINE | ID: covidwho-1443847

ABSTRACT

Extracellular microRNAs (miRNAs) have been proposed to function in cross-kingdom gene regulation. Among these, plant-derived miRNAs of dietary origin have been reported to survive the harsh conditions of the human digestive system, enter the circulatory system, and regulate gene expression and metabolic function. However, definitive evidence supporting the presence of plant-derived miRNAs of dietary origin in mammals has been difficult to obtain due to limited sample sizes. We have developed a bioinformatics pipeline (ePmiRNA_finder) that provides strident miRNA classification and applied it to analyze 421 small RNA sequencing data sets from 10 types of human body fluids and tissues and comparative samples from carnivores and herbivores. A total of 35 miRNAs were identified that map to plants typically found in the human diet and these miRNAs were found in at least one human blood sample and their abundance was significantly different when compared to samples from human microbiome or cow. The plant-derived miRNA profiles were body fluid/tissue-specific and highly abundant in the brain and the breast milk samples, indicating selective absorption and/or the ability to be transported across tissue/organ barriers. Our data provide conclusive evidence for the presence of plant-derived miRNAs as a consequence of dietary intake and their cross-kingdom regulatory function within human circulating system.


Subject(s)
Computational Biology/methods , MicroRNAs/genetics , Plants/genetics , Sequence Analysis, RNA/methods , Animal Feed/analysis , Animals , Brain Chemistry , Carnivora/genetics , Diet , Female , Herbivory/genetics , Humans , Milk, Human/chemistry , Organ Specificity , RNA, Plant/genetics , Sample Size
18.
J Biopharm Stat ; 31(6): 765-787, 2021 Nov 02.
Article in English | MEDLINE | ID: covidwho-1434265

ABSTRACT

The win odds is a distribution-free method of comparing locations of distributions of two independent random variables. Introduced as a method for analyzing hierarchical composite endpoints, it is well suited to be used in the analysis of ordinal scale endpoints in COVID-19 clinical trials. For a single outcome, we provide power and sample size calculation formulas for the win odds test. We also provide an implementation of the win odds analysis method for a single ordinal outcome in a commonly used statistical software to make the win odds analysis fully reproducible.


Subject(s)
COVID-19 , Humans , Research Design , Sample Size
SELECTION OF CITATIONS
SEARCH DETAIL