The drawing effect: Evidence for costs and benefits using pure and mixed lists.

Drawing a referent of a to-be-remembered word often results in better recognition and recall of this word relative to a control task in which the word is written, a pattern dubbed the drawing effect. Although this effect is not always found in pure lists, we report three experiments in which the drawing effect emerged in both pure- and mixed-lists on recognition and recall tests, though the effect was larger in mixed lists. Our experiments then compared drawing effects on memory between pure- and mixed-list contexts to determine whether the larger mixed-list drawing effect reflected a benefit to draw items, a cost to write items, or a combination. In delayed recognition and free-recall tests, a mixed-list benefit emerged for draw items in which memory for mixed-list draw items was greater than pure-list draw items. This mixed-list drawing benefit was accompanied by a mixed-list writing cost compared to pure-list write items, indicating that the mixed-list drawing effect does not operate cost-free. Our findings of a pure-list drawing effect are consistent with a memory strength account, however, the larger drawing effect in mixed lists suggest that participants may also deploy a distinctiveness heuristic to aid retrieval of drawn items.

Practical considerations for sample size calculation for cluster randomized trials.

Cluster randomized trials are an essential design in public health and medical research, when individual randomization is infeasible or undesirable for scientific or logistical reasons. However, the correlation among observations within clusters leads to a decrease in statistical power compared to an individually randomised trial with the same total sample size. This correlation - often quantified using the intra-cluster correlation coefficient - must be accounted for in the sample size calculation to ensure that the trial is adequately powered. In this paper, we first describe the principles of sample size calculation for parallel-arm CRTs, and explain how these calculations can be extended to CRTs with cross-over designs, with a baseline measurement and stepped-wedge designs. We introduce tools to guide researchers with their sample size calculation and discuss methods to inform the choice of the a priori estimate of the intra-cluster correlation coefficient for the calculation. We also include additional considerations with respect to anticipated attrition, a small number of clusters, and use of covariates in the randomisation process and in the analysis.

Accounting for extra-binomial variability with differentially expressed genetic pathway data: a collaborative bioinformatic study.

We describe a collaborative project involving faculty and students in a university bioinformatics/biostatistics center. The project focuses on identification of differentially expressed gene sets ("pathways") in subjects expressing a disease state, medical intervention, or other distinguishable condition. The key feature of the endeavor is the data structure presented to the team: a single cohort of subjects with two samples taken from each subject - one for each of two differing conditions without replication. This particular structure leads to essentially a cohort of 2×2 contingency tables, where each table compares the differential gene state with the pathway condition. Recognizing that correlations both within and between pathway responses can disrupt standard 2×2 table analytics, we develop methods for analyzing this data structure in the presence of complicated intra-table correlations. These provide some convenient approaches for this problem, using design effect adjustments from sample survey theory and manipulations of the summary 2×2 table counts. Monte Carlo simulations show that the methods operate extremely well, validating their use in practice. In the end, the collaborative connections among the team members led to solutions no one of us would have envisioned separately.

Impact of ignoring sampling design in the prediction of binary health outcomes through logistic regression: evidence from Malawi demographic and health survey under-five mortality data; 2000-2016.

The birth and death rates of a population are among the crucial vital statistics for socio-economic policy planning in any country. Since the under-five mortality rate is one of the indicators for monitoring the health of a population, it requires regular and accurate estimation. The national demographic and health survey data, that are readily available to the puplic, have become a means for answering most health-related questions among African populations, using relevant statistical methods. However, many of such applications tend to ignore survey design effect in the estimations, despite the availability of statistical tools that support the analyses. Little is known about the amount of inaccurate information that is generated when predicting under-five mortality rates. This study estimates and compares the bias encountered when applying unweighted and weighted logistic regression methods to predict under-five mortality rate in Malawi using nationwide survey data. The Malawi demographic and health survey data of 2004, 2010, and 2015-16 were used to determine the bias. The analyses were carried out in R software version 3.6.3 and Stata version 12.0. A logistic regression model that included various bio- and socio-demographic factors concerning the child, mother and households was used to estimate the under-five mortality rate. The results showed that accuracy of predicting the national under-five mortality rate hinges on cluster-weighting of the overall predicted probability of child-deaths, regardless of whether the model was weighted or not. Weighting the model caused small positive and negative changes in various fixed-effect estimates, which diffused the result of weighting in the fitted probabilities of deaths. In turn, there was no difference between the overall predicted mortality rate obtained using the weighted model and that obtained in the unweighted model. We recommend considering survey cluster-weights during the computation of overall predicted probability of events for a binary health outcome. This can be done without worrying about the weights during model fitting, whose aim is prediction of the population parameter.

Use of appropriate statistical tools in biomedical research: Current trend & status.

Background & objectives: Due to lack of appropriate statistical knowledge, published research articles contain various errors related to the design, analysis and interpretation of results in the area of biomedical research. If research contains statistical error, however, costly, it may be of no use and the purpose of the investigation gets defeated. Many biomedical research articles published in different peer reviewed journals may retain several statistical errors and flaws in them. This study aimed to examine the trend and status of application of statistics in biomedical research articles. Study design, sample size estimation and statistical measures are crucial components of a study. These points were evaluated in published original research articles to understand the use or misuse of statistical tools. Methods: Three hundred original research articles from the latest issues of selected 37 journals were reviewed. These journals were from the five internationally recognized publication groups (CLINICAL KEY, BMJ Group, WILEY, CAMBRIDGE and OXFORD) accessible through the online library of SGPGI, Lucknow, India. Results: Among articles assessed under present investigation, 85.3 per cent (n=256) were observational, and 14.7 per cent (n=44) were interventional studies. In 93 per cent (n=279) of research articles, sample size estimation was not reproducible. The simple random sampling was encountered rarely in biomedical studies even though none of the articles was adjusted by design effect and, only five articles had used randomized test. The testing of assumption of normality was mentioned in only four studies before applying parametric tests. Interpretation & conclusions: In order to present biomedical research results with reliable and precise estimates based on data, the role of engaging statistical experts need to be appreciated. Journals must have standard rules for reporting study design, sample size and data analysis tools. Careful attention is needed while applying any statistical procedure as, it will not only help readers to trust in the published articles, but also rely on the inferences the published articles draw.

Designing three-level cluster randomized trials to assess treatment effect heterogeneity.

Cluster randomized trials often exhibit a three-level structure with participants nested in subclusters such as health care providers, and subclusters nested in clusters such as clinics. While the average treatment effect has been the primary focus in planning three-level randomized trials, interest is growing in understanding whether the treatment effect varies among prespecified patient subpopulations, such as those defined by demographics or baseline clinical characteristics. In this article, we derive novel analytical design formulas based on the asymptotic covariance matrix for powering confirmatory analyses of treatment effect heterogeneity in three-level trials, that are broadly applicable to the evaluation of cluster-level, subcluster-level, and participant-level effect modifiers and to designs where randomization can be carried out at any level. We characterize a nested exchangeable correlation structure for both the effect modifier and the outcome conditional on the effect modifier, and generate new insights from a study design perspective for conducting analyses of treatment effect heterogeneity based on a linear mixed analysis of covariance model. A simulation study is conducted to validate our new methods and two real-world trial examples are used for illustrations.

Design Effect in Multilevel Settings: A Commentary on a Latent Variable Modeling Procedure for Its Evaluation.

A latent variable modeling-based procedure is discussed that permits to readily point and interval estimate the design effect index in multilevel settings using widely circulated software. The method provides useful information about the relationship of important parameter standard errors when accounting for clustering effects relative to conducting single-level analyses. The approach can also be employed as an addendum to point and interval estimation of the intraclass correlation coefficient in empirical research. The discussed procedure makes it easily possible to evaluate the design effect in two-level studies by utilizing the popular latent variable modeling methodology and is illustrated with an example.

Intraclass correlation coefficients for weight loss cluster randomized trials in primary care: The PROPEL trial.

The aim of this study was to compute intra-class correlations (ICCs) for weight-related and patient-reported outcomes in a cluster randomized clinical trial (cRCT) for weight loss. Baseline and follow-up data from the Promoting Successful Weight Loss in Primary Care in Louisiana (PROPEL) cRCT were used in this analysis. ICCs were computed for baseline and follow-up measures, and changes in body weight, cardiometabolic risk factors and health-related and weight-related quality of life at 6, 12, 18 and 24 months. Baseline ICCs ranged from 0 for PROMIS measures of anxiety and fatigue to 0.055 for total cholesterol (median = 0.019). The ICCs were higher for changes and decreased over time during follow-up. The ICCs for changes were highest in the pooled sample (intervention and usual care combined) followed by the intervention and usual care groups, respectively. The results demonstrated significant ICCs for several outcomes in a weight loss cRCT. The ICCs differed in magnitude depending on whether baseline versus longitudinal data were used, whether data were combined across treatment arms or were considered separately, and varied across the follow-up period. All these factors must be considered when choosing an ICC to inform sample size estimates for future weight loss cRCTs conducted in primary care settings.

Sample size calculation for clustered survival data under subunit randomization.

Each cluster consists of multiple subunits from which outcome data are collected. In a subunit randomization trial, subunits are randomized into different intervention arms. Observations from subunits within each cluster tend to be positively correlated due to the shared common frailties, so that the outcome data from a subunit randomization trial have dependency between arms as well as within each arm. For subunit randomization trials with a survival endpoint, few methods have been proposed for sample size calculation showing the clear relationship between the joint survival distribution between subunits and the sample size, especially when the number of subunits from each cluster is variable. In this paper, we propose a closed form sample size formula for weighted rank test to compare the marginal survival distributions between intervention arms under subunit randomization, possibly with variable number of subunits among clusters. We conduct extensive simulations to evaluate the performance of our formula under various design settings, and demonstrate our sample size calculation method with some real clinical trials.

Power and sample size for observational studies of point exposure effects.

Inverse probability of treatment weights (IPTWs) are commonly used to control for confounding when estimating causal effects of point exposures from observational data. When planning a study that will be analyzed with IPTWs, determining the required sample size for a given level of statistical power is challenging because of the effect of weighting on the variance of the estimated causal means. This paper considers the utility of the design effect to quantify the effect of weighting on the precision of causal estimates. The design effect is defined as the ratio of the variance of the causal mean estimator divided by the variance of a naïve estimator if, counter to fact, no confounding had been present and weights were not needed. A simple, closed-form approximation of the design effect is derived that is outcome invariant and can be estimated during the study design phase. Once the design effect is approximated for each treatment group, sample size calculations are conducted as for a randomized trial, but with variances inflated by the design effects to account for weighting. Simulations demonstrate the accuracy of the design effect approximation, and practical considerations are discussed.

Snowball Sampling Study Design for Serosurveys Early in Disease Outbreaks.

Serological surveys can provide evidence of cases that were not previously detected, depict the spectrum of disease severity, and estimate the proportion of asymptomatic infections. To capture these parameters, survey sample sizes may need to be very large, especially when the overall infection rate is still low. Therefore, we propose the use of "snowball sampling" to enrich serological surveys by testing contacts of infected persons identified in the early stages of an outbreak. For future emerging pandemics, this observational study sampling design can answer many key questions, such as estimation of the asymptomatic proportion of all infected cases, the probability of a given clinical presentation for a seropositive individual, or the association between characteristics of either the host or the infection and seropositivity among contacts of index individuals. We provide examples, in the context of the coronavirus disease 2019 (COVID-19) pandemic, of studies and analysis methods that use a snowball sample and perform a simulation study that demonstrates scenarios where snowball sampling can answer these questions more efficiently than other sampling schemes. We hope such study designs can be applied to provide valuable information to slow the present pandemic as it enters its next stage and in early stages of future pandemics.

DEALING WITH INACCURATE MEASURES OF SIZE IN TWO-STAGE PROBABILITY PROPORTIONAL TO SIZE SAMPLE DESIGNS: APPLICATIONS IN AFRICAN HOUSEHOLD SURVEYS.

The units at the early stages of multi-stage area samples are generally sampled with probabilities proportional to their estimated sizes (PPES). With such a design, an overall equal probability (EP) sample design would yield a constant number of final stage units from each final stage cluster if the measures of size used in the PPES selection at each sampling stage were directly proportional to the number of final stage units. However, there are often sizable relative differences between the measures of size used in the PPES selections and the number of final stage units. Two common approaches for dealing with these differences are: (1) to retain a self-weighting sample design, allowing the sample sizes to vary across the sampled primary sampling units (PSUs) and (2) to retain the fixed sample size in each PSU and to compensate for the unequal selection probabilities by weighting adjustments in the analyses. This article examines these alternative designs in the context of two-stage sampling in which PSUs are sampled with PPES at the first stage, and an equal probability sample of final stage units is selected from each sampled PSU at the second stage. Two-stage sample designs of this type are used for household surveys in many countries. The discussion is illustrated with data from the Population-based HIV Impact Assessment surveys that were conducted using this design in several African countries.

[Preparation of Cangyi nanoemulsion in situ gel and drug release mechanism of nasal mucosa in vitro].

To prepare Cangyi nanoemulsion in situ gel and study its nasal mucosa release mechanism in vitro. After proper treatment of different drugs in the compound, the prescription of nanoemulsion was determined by pseudo-ternary phase diagram method. With the ratio of mixed emulsifier to oil phase [(S+COS)/O], the ratio of mixed emulsifier(K_m), the ratio of water phase to mixed emulsifier and oil phase[W/(S+COS+O)] as investigation factors and the normalized value(OD) as evaluation index, the prescription of Cangyi nanoemulsion was optimized by central composite design-response surface method. With the ratio of poloxamer 407(P407) and poloxamer 188(P188) as the investigation factors and the gelation temperature as the evaluation index, the in situ gel prescription of Cangyi nanoemulsion was optimized. The improved Franz diffusion cell was used to explore the nasal mucosa drug-release mechanism of Cangyi nanoemulsion in situ gel with oxymatrine, ferulic acid and salvianolic acid B content as indexes. The optimal prescription of Cangyi nanoemulsion in situ gel was as follows: 6.862% castor oil polyoxyl(EL), 4.262% absolute ethanol, 1.392% ethyl oleate, 7% P407 and 6% P188. The average pH was 5.55 and the average gelation temperature was 32.8 ℃. In vitro release studies showed that oxymatrine, ferulic acid and salvianolic acid B were released simultaneously and the drug release behavior was consistent with that in Higuchi model. The preparation process of Cangyi nanoemulsion in situ gel is stable, with suitable pH value, gelation temperature and viscosity. It has a certain slow-release effect, and can meet the needs of local nasal drug use.

Metodologías cuantitativas: Cálculo del tamaño de muestra con STATA y R / Quantitative methods: Sample size calculation with STATA and R

El cálculo de tamaño de muestra es un aspecto esencial del diseño de estudios cuantitativos. Un adecuado tamaño de muestra nos permite determinar cuál es la mínima cantidad de participantes necesarios para probar nuestra hipótesis de interés. De esta manera, podemos reducir costos, maximizar el uso de nuestros recursos de investigación y garantizar la factibilidad del estudio. Contradictoriamente, a pesar de su relevancia muy pocos investigadores dominan esta habilidad. Esta revisión tiene por objeto revisar los conceptos básicos para realizar un cálculo de tamaño de muestra y compartir códigos de Stata y R específicamente diseñados para facilitar estos cálculos.

The calculation of sample size is an essential aspect of the design of quantitative studies. An adequate sample size allows us to determine the minimum number of participants necessary to test our hypothesis of interest. Hence, we can reduce costs, maximize the use of our research resources and guarantee the feasibility of the study. Contradictorily, despite its relevance, very few researchers dominate this skill. This review aims to review the basics of sample size calculation and share Stata and R codes specifically designed to facilitate these calculations.

Sample size estimation for stratified individual and cluster randomized trials with binary outcomes.

Individual randomized trials (IRTs) and cluster randomized trials (CRTs) with binary outcomes arise in a variety of settings and are often analyzed by logistic regression (fitted using generalized estimating equations for CRTs). The effect of stratification on the required sample size is less well understood for trials with binary outcomes than for continuous outcomes. We propose easy-to-use methods for sample size estimation for stratified IRTs and CRTs and demonstrate the use of these methods for a tuberculosis prevention CRT currently being planned. For both IRTs and CRTs, we also identify the ratio of the sample size for a stratified trial vs a comparably powered unstratified trial, allowing investigators to evaluate how stratification will affect the required sample size when planning a trial. For CRTs, these can be used when the investigator has estimates of the within-stratum intracluster correlation coefficients (ICCs) or by assuming a common within-stratum ICC. Using these methods, we describe scenarios where stratification may have a practically important impact on the required sample size. We find that in the two-stratum case, for both IRTs and for CRTs with very small cluster sizes, there are unlikely to be plausible scenarios in which an important sample size reduction is achieved when the overall probability of a subject experiencing the event of interest is low. When the probability of events is not small, or when cluster sizes are large, however, there are scenarios where practically important reductions in sample size result from stratification.

Clustering effects of oral conditions based on clinical and radiographic examinations.

OBJECTIVES: The intra-class correlation coefficient (ICC) is a measure of intra-subject clustering effects. A priori estimates of the ICC and the associated design effect (DE) are required for sample size estimation in clustered studies, and should be considered during their analysis, too. We aimed to determine the clustering effects of carious lesions, apical lesions, periodontal bone loss, and periodontal pocketing, assessed in clinical or radiographic examinations. METHODS: A subsample of patients (n = 175) enrolled in the fifth German Oral Health Study provided data on clinically determined carious teeth (i.e., with untreated carious lesions, WHO method) as well as teeth with periodontal pocketing (i.e., with maximum probing-pocket-depths ≥ 4 mm). A sample of panoramic radiographs (n = 85) from randomly chosen patients, examined from 2010 to 2017 at the Charité dental hospital, provided data on radiographically determined carious teeth (i.e., with lesions extending into dentine or enamel), teeth with apical lesions (determined by dentists via majority vote), and teeth with periodontal bone loss (≥ 20% of root-length). The ICC and its 95% confidence interval (95% CI) were determined. RESULTS: There were 3839 and 1961 teeth assessed in clinical and radiographic evaluations, respectively. For clinically or radiographically determined carious lesions, the ICC (95% CI) was 0.20 (0.16-0.24) or 0.19 (0.14-0.25), respectively. For clinical pocketing or radiographic bone loss, the ICC was 0.40 (0.35-0.46) or 0.30 (0.24-0.38), respectively. The lowest ICC was found for apical lesions at 0.08 (0.06-0.13). CONCLUSIONS: The ICC varied between assessment methods and conditions. Clustered trials should account for this during study planning and data analysis. CLINICAL RELEVANCE: Within the limitations of this study, and considering the risk of selection bias and the limited sample sizes of both datasets, clustering effects were substantial but varied between dental conditions. Studies not accounting for this during planning and analysis may yield misleading estimates if clustering is present.

Comparative Study of Confidence Intervals for Proportions in Complex Sample Surveys.

The most widespread method of computing confidence intervals (CIs) in complex surveys is to add and subtract the margin of error (MOE) from the point estimate, where the MOE is the estimated standard error multiplied by the suitable Gaussian quantile. This Wald-type interval is used by the American Community Survey (ACS), the largest US household sample survey. For inferences on small proportions with moderate sample sizes, this method often results in marked under-coverage and lower CI endpoint less than 0. We assess via simulation the coverage and width, in complex sample surveys, of seven alternatives to the Wald interval for a binomial proportion with sample size replaced by the 'effective sample size,' that is, the sample size divided by the design effect. Building on previous work by the present authors, our simulations address the impact of clustering, stratification, different stratum sampling fractions, and stratum-specific proportions. We show that all intervals undercover when there is clustering and design effects are computed from a simple design-based estimator of sampling variance. Coverage can be better calibrated for the alternatives to Wald by improving estimation of the effective sample size through superpopulation modeling. This approach is more effective in our simulations than previously proposed modifications of effective sample size. We recommend intervals of the Wilson or Bayes uniform prior form, with the Jeffreys prior interval not far behind.

Relative efficiency of unequal cluster sizes in stepped wedge and other trial designs under longitudinal or cross-sectional sampling.

BACKGROUND: A cluster trial with unequal cluster sizes often has lower precision than one with equal clusters, with a corresponding inflation of the design effect. For parallel group trials, adjustments to the design effect are available under sampling models with a single intracluster correlation. Design effects for equal clusters under more complex scenarios have appeared recently (including stepped wedge trials under cross-sectional or longitudinal sampling). We investigate the impact of unequal cluster size in these more general settings. RESULTS: Assuming a linear mixed model with an exchangeable correlation structure that incorporates cluster and subject autocorrelation, we compute the relative efficiency (RE) of a trial with clusters of unequal size under a size-stratified randomization scheme, as compared to an equal cluster trial with the same total number of observations. If there are no within-cluster time effects, the RE exceeds that for a parallel trial. In general, the RE is a weighted average of the RE for a parallel trial and the RE for a crossover trial in the same clusters. Existing approximations for parallel designs are extended to the general setting. Increasing the cluster size by the factor (1 + CV2 ), where CV is the coefficient of variation of cluster size, leads to conservative sample sizes, as in a popular method for parallel trials. CONCLUSION: Methods to assess experimental precision for single-period parallel trials with unequal cluster sizes can be extended to stepped wedge and other complete layouts under longitudinal or cross-sectional sampling. In practice, the loss of precision due to unequal cluster sizes is unlikely to exceed 12%.

Repeat participation in annual cross-sectional surveys of drug users and its implications for analysis.

OBJECTIVE: We sought to establish the extent of repeat participation in a large annual cross-sectional survey of people who inject drugs and assess its implications for analysis. RESULTS: We used "porn star names" (the name of each participant's first pet followed by the name of the first street in which they lived) to identify repeat participation in three Australian Illicit Drug Reporting System surveys. Over 2013-2015, 2468 porn star names (96.2%) appeared only once, 88 (3.4%) twice, and nine (0.4%) in all 3 years. We measured design effects, based on the between-cluster variability for selected estimates, of 1.01-1.07 for seven key variables. These values indicate that the complex sample is (e.g.) 7% less efficient in estimating prevalence of heroin use (ever) than a simple random sample, and 1% less efficient in estimating number of heroin overdoses (ever). Porn star names are a useful means of tracking research participants longitudinally while maintaining their anonymity. Repeat participation in the Australian Illicit Drug Reporting System is low (less than 5% per annum), meaning point-prevalence and effect estimation without correction for the lack of independence in observations is unlikely to seriously affect population inference.

Cross-sectional versus longitudinal designs for function estimation, with an application to cerebral cortex development.

Motivated by studies of the development of the human cerebral cortex, we consider the estimation of a mean growth trajectory and the relative merits of cross-sectional and longitudinal data for that task. We define a class of relative efficiencies that compare function estimates in terms of aggregate variance of a parametric function estimate. These generalize the classical design effect for estimating a scalar with cross-sectional versus longitudinal data, and are shown to be bounded above by it in certain cases. Turning to nonparametric function estimation, we find that longitudinal fits may tend to have higher aggregate variance than cross-sectional ones, but that this may occur because the former have higher effective degrees of freedom reflecting greater sensitivity to subtle features of the estimand. These ideas are illustrated with cortical thickness data from a longitudinal neuroimaging study.