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1.
Trials ; 22(1): 598, 2021 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-34488848

RESUMO

BACKGROUND: Stepped-wedge designs (SWD) are increasingly used to evaluate the impact of changes to the process of care within health care systems. However, to generate definitive evidence, a correct sample size calculation is crucial to ensure such studies are properly powered. The seminal work of Hussey and Hughes (Contemp Clin Trials 28(2):182-91, 2004) provides an analytical formula for power calculations with normal outcomes using a linear model and simple random effects. However, minimal development and evaluation have been done for power calculation with non-normal outcomes on their natural scale (e.g., logit, log). For example, binary endpoints are common, and logistic regression is the natural multilevel model for such clustered data. METHODS: We propose a power calculation formula for SWD with either normal or non-normal outcomes in the context of generalized linear mixed models by adopting the Laplace approximation detailed in Breslow and Clayton (J Am Stat Assoc 88(421):9-25, 1993) to obtain the covariance matrix of the estimated parameters. RESULTS: We compare the performance of our proposed method with simulation-based sample size calculation and demonstrate its use on a study of patient-delivered partner therapy for STI treatment and a study that assesses the impact of providing additional benchmark prevalence information in a radiologic imaging report. To facilitate adoption of our methods we also provide a function embedded in the R package "swCRTdesign" for sample size and power calculation for multilevel stepped-wedge designs. CONCLUSIONS: Our method requires minimal computational power. Therefore, the proposed procedure facilitates rapid dynamic updates of sample size calculations and can be used to explore a wide range of design options or assumptions.


Assuntos
Projetos de Pesquisa , Simulação por Computador , Humanos , Tamanho da Amostra
2.
BMJ Open ; 11(8): e048985, 2021 08 24.
Artigo em Inglês | MEDLINE | ID: mdl-34429313

RESUMO

OBJECTIVES: The I CARE study (Improving Care After colon canceR treatment in the Netherlands) aims to compare surgeon-led to general practitioner (GP)-led colon cancer survivorship care. Recruitment to the trial took longer than expected. In this descriptive study, recruitment is critically reviewed. SETTING: Patients were recruited from eight Dutch medical centres. PARTICIPANTS: Patients treated with curative intent for stages I-III colon cancer. Target patient sample size was calculated at 300. INTERVENTIONS: Patients were randomised to surgeon-led (usual) versus GP-led care, with or without access to an eHealth application (Oncokompas). OUTCOME MEASURES: Baseline characteristics of (non-)participants, reasons for non-participation and strategies to improve recruitment were reviewed. RESULTS: Out of 1238 eligible patients, 353 patients were included. Of these, 50 patients dropped out shortly after randomisation and before start of the intervention, resulting in a participation rate of 25%. Participants were on average slightly younger (68.1 years vs 69.3 years) and more often male (67% vs 50%) in comparison to non-participants. A total of 806 patients declined participation for reasons most often relating to research (57%), including the wish to remain in specialist care (31%) and too much effort to participate (12%). Some patients mentioned health (9%) and confrontation with the disease (5%) as a reason. In 43 cases, GPs declined participation, often related to the study objective, need for financial compensation and time restraints. The generally low participation rate led to concerns about reaching the target sample size. Methods to overcome recruitment challenges included changes to the original recruitment procedure and the addition of new study centres. CONCLUSIONS: Challenges were faced in the recruitment to a randomised trial on GP-led colon cancer survivorship care. Research on the transition of care requires sufficient time, funding and support base among patients and healthcare professionals. These findings will help inform researchers and policy-makers on the development of future practices. TRIAL REGISTRATION NUMBER: NTR4860.


Assuntos
Neoplasias do Colo , Clínicos Gerais , Neoplasias do Colo/terapia , Humanos , Masculino , Tamanho da Amostra , Sobrevida , Sobrevivência
3.
BMC Med Res Methodol ; 21(1): 168, 2021 08 16.
Artigo em Inglês | MEDLINE | ID: mdl-34399696

RESUMO

BACKGROUND: Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial. METHODS: We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice. RESULTS: Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size. CONCLUSIONS: The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.


Assuntos
Distribuição Aleatória , Simulação por Computador , Humanos , Funções Verossimilhança , Tamanho da Amostra , Viés de Seleção
4.
BMC Med Res Methodol ; 21(1): 171, 2021 08 17.
Artigo em Inglês | MEDLINE | ID: mdl-34404344

RESUMO

BACKGROUND: Null hypothesis significance testing (NHST) is among the most frequently employed methods in the biomedical sciences. However, the problems of NHST and p-values have been discussed widely and various Bayesian alternatives have been proposed. Some proposals focus on equivalence testing, which aims at testing an interval hypothesis instead of a precise hypothesis. An interval hypothesis includes a small range of parameter values instead of a single null value and the idea goes back to Hodges and Lehmann. As researchers can always expect to observe some (although often negligibly small) effect size, interval hypotheses are more realistic for biomedical research. However, the selection of an equivalence region (the interval boundaries) often seems arbitrary and several Bayesian approaches to equivalence testing coexist. METHODS: A new proposal is made how to determine the equivalence region for Bayesian equivalence tests based on objective criteria like type I error rate and power. Existing approaches to Bayesian equivalence testing in the two-sample setting are discussed with a focus on the Bayes factor and the region of practical equivalence (ROPE). A simulation study derives the necessary results to make use of the new method in the two-sample setting, which is among the most frequently carried out procedures in biomedical research. RESULTS: Bayesian Hodges-Lehmann tests for statistical equivalence differ in their sensitivity to the prior modeling, power, and the associated type I error rates. The relationship between type I error rates, power and sample sizes for existing Bayesian equivalence tests is identified in the two-sample setting. Results allow to determine the equivalence region based on the new method by incorporating such objective criteria. Importantly, results show that not only can prior selection influence the type I error rate and power, but the relationship is even reverse for the Bayes factor and ROPE based equivalence tests. CONCLUSION: Based on the results, researchers can select between the existing Bayesian Hodges-Lehmann tests for statistical equivalence and determine the equivalence region based on objective criteria, thus improving the reproducibility of biomedical research.


Assuntos
Pesquisa Biomédica , Teorema de Bayes , Humanos , Reprodutibilidade dos Testes , Projetos de Pesquisa , Tamanho da Amostra
6.
Artigo em Inglês | MEDLINE | ID: mdl-34360131

RESUMO

This exploratory, nationwide cross-sectional study was performed to investigate the well-being of Portuguese nutritionists, in addition to outlining their professional and demographic profile. Descriptive analyses were carried out to determine the measures relating to centralising tendency and dispersion of the sample. We compared means and proportions through t-tests and Analysis of Variance (ANOVA). The sample size was 206 individuals, respecting a minimum of eight respondents per item to validate the instrument. We recruited Nutritionists from Portugal nationwide using the list of electronic mail provided by the Order of Nutritionists. We sent an electronic mail to all the Nutritionists registered in this Order. We also used messaging applications and social networks (Instagram, Facebook) to reach Nutritionists who were not accessing electronic mail. Most respondents are women (92.5%), young (mean age = 31.4 ± 8.07 years; 54.2% of participants aging under 30 years), single, and with no children. More than half are Catholic (73.8%) and have less than ten years of nutritionist undergraduate completion (55.4%). The only variable that influences well-being at work is the economic variable Household Monthly Income. Those who earn less than €500.00 per month perceive themselves at a lesser state of work well-being than those who earn from €2501.00 to €5000.00 per month.


Assuntos
Nutricionistas , Adulto , Estudos Transversais , Correio Eletrônico , Feminino , Humanos , Portugal , Tamanho da Amostra , Inquéritos e Questionários , Adulto Jovem
7.
BMC Med Res Methodol ; 21(1): 137, 2021 07 05.
Artigo em Inglês | MEDLINE | ID: mdl-34225659

RESUMO

BACKGROUND: A priori sample size calculation requires an a priori estimate of the size of the effect. An incorrect estimate may result in a sample size that is too low to detect effects or that is unnecessarily high. An alternative to a priori sample size calculation is Bayesian updating, a procedure that allows increasing sample size during the course of a study until sufficient support for a hypothesis is achieved. This procedure does not require and a priori estimate of the effect size. This paper introduces Bayesian updating to researchers in the biomedical field and presents a simulation study that gives insight in sample sizes that may be expected for two-group comparisons. METHODS: Bayesian updating uses the Bayes factor, which quantifies the degree of support for a hypothesis versus another one given the data. It can be re-calculated each time new subjects are added, without the need to correct for multiple interim analyses. A simulation study was conducted to study what sample size may be expected and how large the error rate is, that is, how often the Bayes factor shows most support for the hypothesis that was not used to generate the data. RESULTS: The results of the simulation study are presented in a Shiny app and summarized in this paper. Lower sample size is expected when the effect size is larger and the required degree of support is lower. However, larger error rates may be observed when a low degree of support is required and/or when the sample size at the start of the study is small. Furthermore, it may occur sufficient support for neither hypothesis is achieved when the sample size is bounded by a maximum. CONCLUSIONS: Bayesian updating is a useful alternative to a priori sample size calculation, especially so in studies where additional subjects can be recruited easily and data become available in a limited amount of time. The results of the simulation study show how large a sample size can be expected and how large the error rate is.


Assuntos
Pesquisadores , Teorema de Bayes , Simulação por Computador , Humanos , Tamanho da Amostra
8.
Br J Anaesth ; 127(3): 487-494, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34275603

RESUMO

BACKGROUND: Multicentre RCTs are widely used by critical care researchers to answer important clinical questions. However, few trials evaluating mortality outcomes report statistically significant results. We hypothesised that the low proportion of trials reporting statistically significant differences for mortality outcomes is plausibly explained by lower-than-expected effect sizes combined with a low proportion of participants who could realistically benefit from studied interventions. METHODS: We reviewed multicentre trials in critical care published over a 10-yr period in the New England Journal of Medicine, the Journal of the American Medical Association, and the Lancet. To test our hypothesis, we analysed the results using a Bayesian model to investigate the relationship between the proportion of effective interventions and the proportion of statistically significant results for prior distributions of effect size and trial participant susceptibility. RESULTS: Five of 54 trials (9.3%) reported a significant difference in mortality between the control and the intervention groups. The median expected and observed differences in absolute mortality were 8.0% and 2.0%, respectively. Our modelling shows that, across trials, a lower-than-expected effect size combined with a low proportion of potentially susceptible participants is consistent with the observed proportion of trials reporting significant differences even when most interventions are effective. CONCLUSIONS: When designing clinical trials, researchers most likely overestimate true population effect sizes for critical care interventions. Bayesian modelling demonstrates that that it is not necessarily the case that most studied interventions lack efficacy. In fact, it is plausible that many studied interventions have clinically important effects that are missed.


Assuntos
Cuidados Críticos/estatística & dados numéricos , Determinação de Ponto Final/estatística & dados numéricos , Mortalidade , Estudos Multicêntricos como Assunto/estatística & dados numéricos , Ensaios Clínicos Controlados Aleatórios como Assunto/estatística & dados numéricos , Projetos de Pesquisa/estatística & dados numéricos , Teorema de Bayes , Interpretação Estatística de Dados , Humanos , Modelos Estatísticos , Tamanho da Amostra , Resultado do Tratamento
9.
Artigo em Inglês | MEDLINE | ID: mdl-34325496

RESUMO

Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there is a shortage of personnel with programming skills, and commercial programs are often too expensive to use in practice. The review article aimed to explain the basic concepts of sample size calculation and power analysis; the process of sample estimation; and how to calculate sample size using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) with 5 statistical examples. The null and alternative hypothesis, effect size, power, alpha, type I error, and type II error should be described when calculating the sample size or power. G*Power is recommended for sample size and power calculations for various statistical methods (F, t, χ2, Z, and exact tests), because it is easy to use and free. The process of sample estimation consists of establishing research goals and hypotheses, choosing appropriate statistical tests, choosing one of 5 possible power analysis methods, inputting the required variables for analysis, and selecting the "calculate" button. The G*Power software supports sample size and power calculation for various statistical methods (F, t, χ2, z, and exact tests). This software is helpful for researchers to estimate the sample size and to conduct power analysis.


Assuntos
Projetos de Pesquisa , Software , Humanos , Tamanho da Amostra
10.
Radiol Phys Technol ; 14(3): 318-327, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34254251

RESUMO

Deep learning has demonstrated high efficacy for automatic segmentation in contour delineation, which is crucial in radiation therapy planning. However, the collection, labeling, and management of medical imaging data can be challenging. This study aims to elucidate the effects of sample size and data augmentation on the automatic segmentation of computed tomography images using U-Net, a deep learning method. For the chest and pelvic regions, 232 and 556 cases are evaluated, respectively. We investigate multiple conditions by changing the sum of the training and validation datasets across a broad range of values: 10-200 and 10-500 cases for the chest and pelvic regions, respectively. A U-Net is constructed, and horizontal-flip data augmentation, which produces left and right inverse images resulting in twice the number of images, is compared with no augmentation for each training session. All lung cases and more than 100 prostate, bladder, and rectum cases indicate that adding horizontal-flip data augmentation is almost as effective as doubling the number of cases. The slope of the Dice similarity coefficient (DSC) in all organs decreases rapidly until approximately 100 cases, stabilizes after 200 cases, and shows minimal changes as the number of cases is increased further. The DSCs stabilize at a smaller sample size with the incorporation of data augmentation in all organs except the heart. This finding is applicable to the automation of radiation therapy for rare cancers, where large datasets may be difficult to obtain.


Assuntos
Próstata , Tomografia Computadorizada por Raios X , Humanos , Pulmão , Masculino , Tamanho da Amostra , Tórax
11.
Nat Commun ; 12(1): 4192, 2021 07 07.
Artigo em Inglês | MEDLINE | ID: mdl-34234142

RESUMO

Most existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.


Assuntos
Previsões/métodos , Modelos Genéticos , Herança Multifatorial , Medicina de Precisão/métodos , Característica Quantitativa Herdável , Estudos de Casos e Controles , Conjuntos de Dados como Assunto , Estudo de Associação Genômica Ampla , Humanos , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Tamanho da Amostra , Software
12.
BMC Public Health ; 21(1): 1414, 2021 07 17.
Artigo em Inglês | MEDLINE | ID: mdl-34273940

RESUMO

BACKGROUND: Sampling a small number of participants from an entire country is not straightforward. In this case, researchers reluctantly sample from a single setting or few settings, which limits the generalizability of findings. Therefore, there is a need to design efficient sampling method for small sample size surveys that can produce generalizable results at the country level. METHODS: Data comprised of twenty proxy variables to measure health services demands, structures, and outcomes of 413 districts of Iran. We used two data mining methods (hierarchical clustering method (HCM) and model-based clustering method (MCM)) to create homogenous groups of districts, i.e., strata based on these variables. We compared the internal and stability validity of the methods by statistical indices. An expert group checked the face validity of the methods, particularly regarding the total number of strata and the combination of districts in each stratum. The efficiency of selected method, which is measured by the inverse of variance, was compared with a simple random sampling (SRS) through simulation. The sampling design was tested in a national study in Iran, which aimed to evaluate the quality and costs of medical care for eight selected diseases by only recruiting 300 participants per disease at the country level. RESULTS: MCM and HCM divided the districts into eight and two clusters, respectively. The measures of internal and stability validity showed that clusters created by MCM were more separated, compact, and stable, thus forming our optimum strata. The probability of death from stroke, chronic obstructive pulmonary disease, and in-hospital mortality rate were the most important indicators that distinguished the eight strata. Based on the simulation results, MCM increased the efficiency of the sampling design up to 1.7 times compared to SRS. CONCLUSIONS: The use of data mining improved the efficiency of sampling up to 1.7 times greater than SRS and markedly reduced the number of strata to eight in the entire country. The proposed sampling design also identified key variables that could be used to classify districts in Iran for sampling from these target populations in the future studies.


Assuntos
Atenção à Saúde , Análise por Conglomerados , Humanos , Irã (Geográfico) , Reprodutibilidade dos Testes , Tamanho da Amostra
13.
BMJ Open ; 11(7): e048126, 2021 07 28.
Artigo em Inglês | MEDLINE | ID: mdl-34321303

RESUMO

INTRODUCTION: At least 68% of persons with aphasia (PWA) experience reading difficulties. Even though strategy-based interventions are a promising treatment approach for text level reading comprehension deficits in PWA, empirical evidence for their efficacy remains rare. The primary objective of this study is the analysis of the efficacy of a strategy-based intervention on text-level reading comprehension and on reading activities in PWA. METHODS AND ANALYSIS: In a repeated measures trial, 24 PWA will first participate in a waiting period and then in a strategy-based intervention (14 face-to-face-sessions, 60 min each). We will apply two combinations of strategies to treat either the microstructure or the macrostructure, respectively. Participants will be randomly allocated to two parallel groups that will receive these combinations in interchanged sequences. Assessments will be implemented before and after each period as well as 3 and 6 months after the intervention. The primary outcome measure is text-level reading comprehension measured with a German version of the Test de Compréhension de Textes (TCT-D) and represented by the score TCT-D Total . A non-blinded and a blinded rater will evaluate the primary outcome measure. Secondary outcome measures will address specific reading functions, reading activities and cognitive functions. The sample size was determined with an a priori power analysis. For statistical analysis, we will use contrast analyses within repeated measures analysis of variance models. We expect significant improvements in primary and secondary outcome measures during the intervention as compared with changes during the waiting period. ETHICS AND DISSEMINATION: This study was approved by the ethics committee of Deutscher Bundesverband für akademische Sprachtherapie und Logopädie (20-10074-KA-MunmErw+Ko). Results and relevant data will be disseminated in peer-reviewed journals, at conferences and on the Open Science Framework. TRIAL REGISTRATION NUMBER: DRKS00021411 (see Supplementary Table 1).


Assuntos
Afasia , Compreensão , Afasia/terapia , Humanos , Leitura , Projetos de Pesquisa , Tamanho da Amostra
14.
BMC Infect Dis ; 21(1): 666, 2021 Jul 08.
Artigo em Inglês | MEDLINE | ID: mdl-34238240

RESUMO

BACKGROUND: This study was performed to investigate clinical features of patients with severe SARS-CoV-2 pneumonia and identify risk factors for converting to severe cases in those who had mild to moderate diseases at the start of the pandemic in China. METHODS: In this retrospective, multicenter cohort study, patients with mild to moderate SARS-CoV-2 pneumonia were included. Demographic data, symptoms, laboratory values, and clinical outcomes were collected. Data were compared between non-severe and severe patients. RESULTS: 58 patients were included in the final analysis. Compared with non-severe cases, severe patients with SARS-CoV-2 pneumonia had a longer: time to clinical recovery (12·9 ± 4·4 vs 8·3 ± 4·7; P = 0·0011), duration of viral shedding (15·7 ± 6·7 vs 11·8 ± 5·0; P = 0·0183), and hospital stay (20·7 ± 1·2 vs 14·4 ± 4·3; P = 0·0211). Multivariate logistic regression indicated that lymphocyte count was significantly associated with the rate of converting to severe cases (odds ratio 1·28, 95%CI 1·06-1·54, per 0·1 ×  109/L reduced; P = 0·007), while using of low-to-moderate doses of systematic corticosteroids was associated with reduced likelihood of converting to a severe case (odds ratio 0·14, 95%CI 0·02-0·80; P = 0·0275). CONCLUSIONS: The low peripheral blood lymphocyte count was an independent risk factor for SARS-CoV-2 pneumonia patients converting to severe cases. However, this study was carried out right after the start of the pandemic with small sample size. Further prospective studies are warranted to confirm these findings. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2000029839 . Registered 15 February 2020 - Retrospectively registered.


Assuntos
COVID-19/diagnóstico , COVID-19/fisiopatologia , Corticosteroides/administração & dosagem , Adulto , Idoso , COVID-19/epidemiologia , COVID-19/virologia , China/epidemiologia , Feminino , Humanos , Contagem de Linfócitos , Masculino , Pessoa de Meia-Idade , Pandemias , Prognóstico , Estudos Retrospectivos , Fatores de Risco , SARS-CoV-2/patogenicidade , Tamanho da Amostra , Eliminação de Partículas Virais
15.
PLoS Med ; 18(7): e1003660, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34228712

RESUMO

BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.


Assuntos
Antivirais/uso terapêutico , COVID-19/tratamento farmacológico , Ensaios Clínicos Controlados Aleatórios como Assunto , Tamanho da Amostra , Humanos , Modelos Biológicos , SARS-CoV-2 , Resultado do Tratamento , Carga Viral , Replicação Viral , Eliminação de Partículas Virais
16.
Artigo em Inglês | MEDLINE | ID: mdl-34299923

RESUMO

BACKGROUND: Large-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across subpopulations. Data in a survey for some small subpopulations are often not representative of the larger population. OBJECTIVE: We developed a multilevel regression and poststratification (MRP) model to estimate leisure-time physical activity across Brazilian state capitals and evaluated whether the MRP outperforms single-level regression estimates based on the Brazilian cross-sectional national survey VIGITEL (2018). METHODS: We used various approaches to compare the MRP and single-level model (complete-pooling) estimates, including cross-validation with various subsample proportions tested. RESULTS: MRP consistently had predictions closer to the estimation target than single-level regression estimations. The mean absolute errors were smaller for the MRP estimates than single-level regression estimates with smaller sample sizes. MRP presented substantially smaller uncertainty estimates compared to single-level regression estimates. Overall, the MRP was superior to single-level regression estimates, particularly with smaller sample sizes, yielding smaller errors and more accurate estimates. CONCLUSION: The MRP is a promising strategy to predict subpopulations' physical activity indicators from large surveys. The observations present in this study highlight the need for further research, which could, potentially, incorporate more information in the models to better interpret interactions and types of activities across target populations.


Assuntos
Exercício Físico , Estudos Transversais , Inquéritos Epidemiológicos , Humanos , Análise Multinível , Tamanho da Amostra
17.
Comput Methods Programs Biomed ; 208: 106255, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34260969

RESUMO

BACKGROUND: The attained power, calculated conditional on the realized allocation, of a clinical trial may differ from the expected power, obtained pre-randomization through averaging over all potential allocations that could be generated by the randomization algorithm (RA). For example, a two-arm trial using a RA that is expected to allocate 20 participants to each arm will attain less than the expected power if by chance it allocates 25 and 15 participants to the arms. Cluster randomized trials with unequal cluster sizes have elevated risk of realizing an allocation that yields an attained power much lower than the expected power when modest numbers of clusters are randomized. METHOD: We developed the R package CRTpowerdist, which implements both simulations and approximate analytic formulae to calculate the attained powers associated with different realized allocations and constructs the pre-randomization power distribution associated with the RA to facilitate assessing the risk of obtaining inadequate power. The package covers unequal cluster-size, cross-sectional stepped-wedge and parallel cluster randomized trials, with or without stratification. Allowed outcome types are: continuous (Gaussian), binary (Binomial) and count (Poisson). The analytic formulae-based calculations are also implemented in a Shiny app. RESULTS: The functionality of the CRTpowerdist is illustrated for each type of trial design. The examples show how to obtain the attained power, the power distribution, and the risk of low attained power, using both simulation and analytic formulae. CONCLUSION: For cluster randomized trials with unequal cluster sizes, the CRTpowerdist package can assist users in identifying an appropriate randomization algorithm by enabling the user to assess the risk that a randomization algorithm will lead to an allocation with inadequate attained power. The Shiny app makes these assessments accessible to researchers who are unable or do not wish to use the CRTpowerdist package.


Assuntos
Projetos de Pesquisa , Análise por Conglomerados , Simulação por Computador , Estudos Transversais , Humanos , Ensaios Clínicos Controlados Aleatórios como Assunto , Tamanho da Amostra
18.
Musculoskelet Sci Pract ; 54: 102405, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34090856
19.
Am J Hum Genet ; 108(7): 1350-1355, 2021 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-34115965

RESUMO

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.


Assuntos
COVID-19/diagnóstico , COVID-19/genética , Exoma/genética , Predisposição Genética para Doença , Hospitalização/estatística & dados numéricos , Sequenciamento Completo do Exoma , COVID-19/imunologia , COVID-19/terapia , Feminino , Humanos , Interferons/genética , Masculino , Prognóstico , SARS-CoV-2 , Tamanho da Amostra
20.
JAMA ; 325(22): 2262-2272, 2021 06 08.
Artigo em Inglês | MEDLINE | ID: mdl-34077499

RESUMO

Importance: Continuous glucose monitoring (CGM) has been shown to be beneficial for adults with type 2 diabetes using intensive insulin therapy, but its use in type 2 diabetes treated with basal insulin without prandial insulin has not been well studied. Objective: To determine the effectiveness of CGM in adults with type 2 diabetes treated with basal insulin without prandial insulin in primary care practices. Design, Setting, and Participants: This randomized clinical trial was conducted at 15 centers in the US (enrollment from July 30, 2018, to October 30, 2019; follow-up completed July 7, 2020) and included adults with type 2 diabetes receiving their diabetes care from a primary care clinician and treated with 1 or 2 daily injections of long- or intermediate-acting basal insulin without prandial insulin, with or without noninsulin glucose-lowering medications. Interventions: Random assignment 2:1 to CGM (n = 116) or traditional blood glucose meter (BGM) monitoring (n = 59). Main Outcomes and Measures: The primary outcome was hemoglobin A1c (HbA1c) level at 8 months. Key secondary outcomes were CGM-measured time in target glucose range of 70 to 180 mg/dL, time with glucose level at greater than 250 mg/dL, and mean glucose level at 8 months. Results: Among 175 randomized participants (mean [SD] age, 57 [9] years; 88 women [50%]; 92 racial/ethnic minority individuals [53%]; mean [SD] baseline HbA1c level, 9.1% [0.9%]), 165 (94%) completed the trial. Mean HbA1c level decreased from 9.1% at baseline to 8.0% at 8 months in the CGM group and from 9.0% to 8.4% in the BGM group (adjusted difference, -0.4% [95% CI, -0.8% to -0.1%]; P = .02). In the CGM group, compared with the BGM group, the mean percentage of CGM-measured time in the target glucose range of 70 to 180 mg/dL was 59% vs 43% (adjusted difference, 15% [95% CI, 8% to 23%]; P < .001), the mean percentage of time at greater than 250 mg/dL was 11% vs 27% (adjusted difference, -16% [95% CI, -21% to -11%]; P < .001), and the means of the mean glucose values were 179 mg/dL vs 206 mg/dL (adjusted difference, -26 mg/dL [95% CI, -41 to -12]; P < .001). Severe hypoglycemic events occurred in 1 participant (1%) in the CGM group and in 1 (2%) in the BGM group. Conclusions and Relevance: Among adults with poorly controlled type 2 diabetes treated with basal insulin without prandial insulin, continuous glucose monitoring, as compared with blood glucose meter monitoring, resulted in significantly lower HbA1c levels at 8 months. Trial Registration: ClinicalTrials.gov Identifier: NCT03566693.


Assuntos
Glicemia/análise , Diabetes Mellitus Tipo 2/tratamento farmacológico , Controle Glicêmico/métodos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Idoso , Intervalos de Confiança , Diabetes Mellitus Tipo 2/sangue , Feminino , Hemoglobina A Glicada/análise , Humanos , Masculino , Pessoa de Meia-Idade , Satisfação do Paciente , Período Pós-Prandial , Tamanho da Amostra , Fatores de Tempo , Resultado do Tratamento
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