Who Is at Risk of What?

1999 ◽  
Vol 20 (10) ◽  
pp. 706-707 ◽  
Author(s):  
David Birnbaum
Keyword(s):  
At Risk ◽  
Observational Study ◽  
Sample Size ◽  
Correction Factor ◽  
Standard Error ◽  
Finite Population ◽  
Employee Survey ◽  
Estimate Standard Error

AbstractIf you have calculated the sample size required for an employee survey or an observational study of departmental practices but found that the number of observations required is larger than the number of employees, chances are the error is due to use of approximation formulae. Many of us unknowingly were taught to use approximations that fail to include the finite population correction factor. Depending on the objective of a study and the proportion of a population sampled, it may be necessary to consider this correction factor in order to estimate standard error and sample size accurately.

scholarly journals Subclinical atherosclerosis and silent myocardial ischaemia in patients with type 2 diabetes: a protocol of a clinico-observational study

Open Heart ◽  
2014 ◽  
Vol 1 (1) ◽  
pp. e000100 ◽  
Author(s):  
D S Prasad ◽  
Zubair Kabir ◽  
K Revathi Devi ◽  
A K Dash ◽  
B C Das
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
At Risk ◽  
Observational Study ◽  
Sample Size ◽  
Myocardial Ischaemia ◽  
Subclinical Atherosclerosis ◽  
Silent Myocardial Ischaemia ◽  
Patients With Diabetes

IntroductionAtherosclerotic cardiovascular disease is a significant modifiable complication in patients with diabetes and subclinical atherosclerosis is considered a surrogate marker of future vascular events. The clustering of cardiometabolic-risk factors in patients with diabetes and cardiovascular disease is increasingly being recognised. Recent evidence indicates that 20–50% of asymptomatic patients with diabetes may have silent coronary heart disease. However, the identification of subclinical atherosclerosis and silent myocardial ischaemia in patients with diabetes has been less well-explored, especially in low-resource population settings where cost-effective non-invasive clinical tools are available. The objective of this study is to identify patients with physician-diagnosed diabetes who are at risk of developing future cardiovascular events measured as subclinical atherosclerosis and silent myocardial ischaemia in an urban population of Eastern India.Methods and analysisThis is a cross-sectional clinico-observational study. A convenience sampling of approximately 350 consecutive patients with type 2 diabetes based on predefined inclusion and exclusion criteria will be identified at an urban diabetes center. This estimated sample size is based on an expected prevalence of silent myocardial ischaemia of 25% (± 5%), we computed the required sample size using OpenEpi online software assuming an α level of 0.05 (95% CI) to be 289. On factoring 20% non-response the estimated sample size is 350. Previously validated questionnaire tools and well-defined clinical, anthropometric and biochemical measurements will be utilised for data collection. The two primary outcomes—subclinical atherosclerosis and silent myocardial ischaemia will be measured using carotid intima-media thickness and exercise tolerance testing, respectively. Descriptive and multivariate logistic regression statistical techniques will be employed to identify ‘at risk’ patients with diabetes, and adjusted for potential confounders.Ethics and disseminationEthical approval was granted by the institutional review board of Kalinga Institute of Medical Sciences, Bhubaneshwar, India. Data will be presented at academic fora and published in peer-reviewed journals.

Inferential Methods for the Tetrachoric Correlation Coefficient

2005 ◽  
Vol 30 (2) ◽  
pp. 213-225 ◽  
Author(s):  
Douglas G. Bonett ◽  
Robert M. Price
Keyword(s):  
Confidence Interval ◽  
Sample Size ◽  
Standard Error ◽  
Point Estimate ◽  
Interval Estimate ◽  
Tetrachoric Correlation ◽  
Continuous Variables ◽  
Simple Method ◽  
Estimate Standard Error ◽  
Better Than

The tetrachoric correlation describes the linear relation between two continuous variables that have each been measured on a dichotomous scale. The treatment of the point estimate, standard error, interval estimate, and sample size requirement for the tetrachoric correlation is cursory and incomplete in modern psychometric and behavioral statistics texts. A new and simple method of accurately approximating the tetrachoric correlation is introduced. The tetrachoric approximation is then used to derive a simple standard error, confidence interval, and sample size planning formula. The new confidence interval is shown to perform far better than the confidence interval computed by SAS. A method to improve the SAS confidence interval is proposed. All of the new results are computationally simple and are ideally suited for textbook and classroom presentations.

scholarly journals Pain Trajectories Identify Patients at Risk of Persistent Pain After Knee Arthroplasty: An Observational Study

2013 ◽  
Vol 472 (5) ◽  
pp. 1409-1415 ◽  
Author(s):  
Patricia M. Lavand’homme ◽  
Irina Grosu ◽  
Marie-Noëlle France ◽  
Emmanuel Thienpont
Keyword(s):  
At Risk ◽  
Observational Study ◽  
Knee Arthroplasty ◽  
Persistent Pain ◽  
Patients At Risk ◽  
Pain Trajectories

User Characteristics of a Free Tobacco Cessation Smartphone App: An Observational Study (Preprint)

2021 ◽  
Author(s):  
Nick Fradkin ◽  
Susan M Zbikowski ◽  
Trevor Christensen
Keyword(s):  
At Risk ◽  
Observational Study ◽  
Tobacco Use ◽  
Household Income ◽  
Tobacco Cessation ◽  
Washington State ◽  
Smartphone App ◽  
Free Access ◽  
Awareness Campaigns ◽  
Rural Counties

BACKGROUND Tobacco use continues to be the leading preventable cause of death, disease, and disability. Cessation options have been available direct-to-consumer and through employers, health plans, and publicly sponsored programs such as those offered by states. State-sponsored efforts have included tobacco control policies, public awareness campaigns, and tobacco quitlines. Washington state has been offering quitline services since 2000, and in 2015, the state began offering free access to a smartphone app to absorb excess quitline demand and reach more people with cessation services. Since most publicly funded tobacco cessation programs are designed to provide access to underserved communities and groups disproportionately impacted by tobacco use, effectiveness and reach are both important considerations for these public health interventions. This paper aims to add to this literature. OBJECTIVE To understand who used a free cessation app and the extent to which users represented at-risk and underrepresented populations. METHODS An observational study of 1,280 adult Washington state residents who registered for and activated the cessation app between October 1, 2018 and December 31, 2020. Demographic data were collected as part of sign-up, examined using standard descriptive measures, and assessed against state-level surveillance data for representativeness. RESULTS Participants were primarily non-Hispanic white (80.3%), identified as female (63.1%), were between ages 25-54 (76.1%), had at least some college education (68.4%), and reported a household income under $50,000 (70.3%). A minority of respondents were from rural counties (29.4%), identified as LGBQA (12.5%), were uninsured (12.2%), or were currently pregnant, planning pregnancy, or breastfeeding (6.7%). There was high representation of women, 35-54 year-olds, LGBQA individuals, and college graduates, as well as individuals with low household income, poor mental health, Medicaid insurance, and residences in rural counties. CONCLUSIONS A diverse population of tobacco users will use a free cessation app. Individuals who used the app in this study largely represent the demographic groups most at risk for cigarette smoking and associated premature disease and death. This may have implications for health equity.

Equal probability sampling

2019 ◽  
pp. 23-47
Author(s):  
David G. Hankin ◽  
Michael S. Mohr ◽  
Ken B. Newman
Keyword(s):  
Sample Size ◽  
Relative Efficiency ◽  
Finite Population ◽  
Simple Random Sampling ◽  
Sampling Distribution ◽  
Equal Probability ◽  
Sampling Variance ◽  
Selection Methods ◽  
Target Variable ◽  
Sampling Fraction

This chapter presents a formal quantitative treatment of material covered conceptually in Chapter 2, all with respect to equal probability with replacement (SWR) and without replacement selection simple random sampling, (SRS) of samples of size n from a finite population of size N. Small sample space examples are used to illustrate unbiasedness of mean-per-unit estimators of the mean, total and proportion of the target variable, y, for SWR and SRS. Explicit formulas for sampling variance indicate how estimator uncertainty depends on finite population variance, sample size and sampling fraction. Measures of the relative performance of alternative sampling strategies (relative precision, relative efficiency, net relative efficiency) are introduced and applied to mean-per-unit estimators used for the SWR and SRS selection methods. Normality of the sampling distribution of the SRS mean-per-unit estimator depends on sample size but also on the shape of the distribution of the target variable, y, values over the finite population units. Normality of the sampling distribution is required to justify construction of valid 95% confidence intervals that may be constructed around sample estimates based on unbiased estimates of sampling variance. Methods to calculate sample size to achieve accuracy objectives are presented. Additional topics include Bernoulli sampling (a without replacement selection scheme for which sample size is a random variable), the Rao–Blackwell theorem (which allows improvement of estimators that are based on selection methods which may result in repeated selection of the same units), oversampling and nonresponse.

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