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PubMedDate not supplied

A dual frame design for sampling elderly minorities and persons with disabilities.

Multiple data sources are sometimes available as potential sampling frames for population surveys, and in some situations the use of a multiple frame sample design is more advantageous than using a single sampling frame. The use of multiple sampling frames, however, has variance and bias implications, as well as sampling, data collection, and logistical considerations. These issues are addressed for a proposed dual frame sampling approach in the National Health Interview Survey (NHIS). The results of an investigation of the sampling efficiencies and operational issues in supplementing the NHIS area frame sample with a sample of elderly African and Hispanic Americans and persons with disabilities selected from Social Security Administration files are presented.

Adolescent↗

PubMed1987

Design and sampling considerations, response rates, and representativeness in a Finnish Twin Family Study.

Kinships composed of twin parents, their spouses and children, offer a robust and flexible sampling design for research in genetic epidemiology. Families-of-twins designs circumvent some of the sampling problems that arise when independent data sets are combined, and these designs provide unique evaluations of maternal influences, assortative mating and X-linkage. Unfortunately, empirical studies of families of twin parents have been limited by relatively small samples and by the self-selection biases intrinsic in ascertainment of families from volunteer twin registries. A large and representative cohort of monozygotic and dizygotic twin parents, drawn from a population-based twin registry, provides the optimal sampling frame for twin-family research. This paper reviews the sampling considerations underlying the initial family study based on the Finnish Twin Cohort and evaluates the representativeness of the sampled twins. Spouses and adult children (over 18 years) of 236 pairs of twins, about equally divided by gender and zygosity, were evaluated by a postal questionnaire. Individual response rates exceeded 86% and in 464 of the 472 nuclear families (98.3%), at last one member of the twin's family completed the questionnaire. The sampled twins, selected for fecundity to maximize statistical power of the obtained data, were broadly representative of non-selected twins drawn from the Cohort, with whom they were matched on age, gender, and zygosity. Such results suggest that the Finnish Cohort has excellent potential for extended twin-family research designs.

Adolescent↗

PubMed1997

Density of larval Culicoides belkini (Diptera:Ceratopogonidae) in relation to physicochemical variables in different habitats.

Immature density and population size of the biting midge Culicoides belkini (Wirth & Arnaud) were estimated for habitats on Moorea Island, French Polynesia, by means of random, 2- and 3-stage sampling designs. Samples were taken in March 1993 from 5 strata of a large larval habitat: a sandy-mud surface of approximately 5,000 m2 (stratum 1) in which approximately 12,000 land crab burrows (stratum 2) were counted, a small pond surrounded by approximately 300 m2 of muddy bank (stratum 3), and a high organic muddy area (Kopara) of approximately 1,200 m2 (stratum 4) with approximately 3,500 crab burrows (stratum 5). Larval density was usually higher in the mud of crab burrows, especially those in the Kopara stratum. Larval density was significantly lower in the sediment of the sandy area as compared with pond banks or Kopara surface. The sampling designs and techniques were logistically adequate, statistically relevant, and were recommended for future studies on C. belkini larval density. Larval habitats were characterized by means of multivariate analysis. Comparison of larval densities with selected environmental variables indicated that larvae density was higher in wet sediments with high levels of organic matter (approximately 8% of dry weight of sediment) and low salinity (approximately 0.5-1.5% NaCl equivalents). These variables were considered significant if larval control by means of habitat modification has to be achieved. Nevertheless, C. belkini can tolerate a broad spectrum of variation in the other environmental variables measured and breed in a variety of ecological situations. Therefore, it has a high potential for colonizing new habitats.

Animals↗

PubMed1990

Measuring the activities of daily living: comparisons across national surveys.

The "activities of daily living," or ADLs, are the basic tasks of everyday life, such as eating, bathing, dressing, toileting, and transferring. Reported estimates of the size of the elderly population with ADL disabilities differ substantially across national surveys. Differences in which ADL items are being measured and in what constitutes a disability account for much of the variation. Other likely explanations are differences in sample design, sample size, survey methodology, and age structure of the population to which the sample refers. When essentially equivalent ADL measures are compared, estimates for the community-based population vary by up to 3.1 percentage points; and for the institutionalized population, with the exception of toileting, by no more than 3.2 percentage points. As small as these differences are in absolute terms, they can be large in percent differences across surveys. For example, the National Medical Expenditure Survey estimates that there are 60 percent more elderly people with ADL problems than does the Supplement on Aging.

Activities of Daily Living↗

PubMed1995

Human tissue monitoring and specimen banking: opportunities for exposure assessment, risk assessment, and epidemiologic research.

A symposium on Human Tissue Monitoring and Specimen Banking: Opportunities for Exposure Assessment, Risk Assessment, and Epidemiologic Research was held from 30 March to 1 April 1993 in Research Triangle Park, North Carolina. There were 117 registered participants from 18 states and 5 foreign countries. The first 2 days featured 21 invited speakers from the U.S. Environmental Protection Agency, the Centers for Disease Control and Prevention, the National Institute of Environmental Health Sciences, various other government agencies, and universities in the United States, Canada, Germany, and Norway. The speakers provided a state-of-the-art overview of human exposure assessment techniques (especially applications of biological markers) and their relevance to human tissue specimen banking. Issues relevant to large-scale specimen banking were discussed, including program design, sample design, data collection, tissue collection, and ethical ramifications. The final group of presentations concerned practical experiences of major specimen banking and human tissue monitoring programs in the United States and Europe. The symposium addressed the utility and research opportunities afforded by specimen banking programs for future research needs in the areas of human exposure assessment, risk assessment, and environmental epidemiology. The third day of the symposium consisted of a small workshop convened to discuss and develop recommendations to the U.S. Environmental Protection Agency regarding applications and utility of large-scale specimen banking, biological monitoring, and biological markers for risk assessment activities.

Biomarkers↗

PubMed1992

Sample size requirements for stratified cluster randomization designs.

Sample size requirements are provided for designs of studies in which clusters are randomized within each of several strata, where cluster size itself may be a stratifying factor. The approach generalizes a formula derived by Woolson et al., which provides sample size requirements for the Cochran-Mantel-Haenszel statistic. Issues of data analysis are also discussed.

Cluster Analysis↗

PubMed1995

Issues associated with the design of a national probability sample for human exposure assessment.

Data obtained from national probability sample surveys provide important information on the prevalence of various health conditions and distributions of physical and biochemical characteristics of the U.S. population. The sample design of a survey specifies how sampling from a designated population over a stated period is to be accomplished. A survey's analytical objectives and interests--in particular subpopulations--affect the sample design strategy. Selected subdomains of the population often must be oversampled so that estimates can be made with acceptable precision. This article addresses sample design considerations for a national probability sample for human tissue monitoring and specimen banking. Among the sampling issues addressed are the oversampling of special populations e.g., minority groups and at-risk groups such as low income or elderly persons; geographic coverage; and sample size considerations. The sample design for a major health survey, the Third National Health and Nutrition Examination Survey (NHANES III), is used to illustrate a complex, multistage probability sample design and to highlight some of the sampling issues discussed in this article.

Data Collection↗

PubMed1980

The SENIC sampling process: design for choosing hospitals and patients and results of sample selection.

To achieve its primary objectives, the Study on the Efficacy of Nosocomial Infection Control (SENIC Project) focused its attention on a target population of patients referred to as SENIC-eligible admissions in a target population of hospitals referred to as the "SENIC Universe." SENIC thus required a design for sampling hospitals and patients within these hospitals and a valid procedure for projecting sample results to the target population. This paper presents the details of the sampling design used, describes the actual process of selecting hospitals and patients for the surveys, explains the procedure used to project sample results to the target population, and examines the possibility of bias in the design and hospital selection process. As with most large-scale sample surveys, the design and sample selection processes for the surveys in Phases II and III of SENIC were complicated by incomplete frame, nonresponse and measurement problems. Nevertheless, adjustments to reduce the effects of some of these problems have been made through the development of a valid procedure for projecting sample results to the target population, and it appears unlikely that practically important nonsampling biases will result from the estimation procedures applied to this sample of hospitals.

Cross Infection↗

PubMedDate not supplied

A population survey on legislative measures to restrict smoking in Ontario: 1. Design, methodology, and sample representativeness.

Legislative measures restricting cigarette smoking have the potential to influence whether a person begins or continues to smoke and to affect the impact of passive smoking. We surveyed a representative sample of the adult population in Ontario on their knowledge of existing legislation and of the adverse effects of primary and secondary smoking on health. We also assessed their attitudes toward a range of restrictions and changes in legislation as well as their views on the enactment and enforcement of such legislation. This paper reports on the sample design, methods, response rates, and representativeness of the respondents. We used a three-stage stratified cluster design, covering both urban areas (with or without existing smoking bylaws) and rural areas and incorporating telephone interviews using random-digit dialing. The total number of respondents was 1,383, for an overall response rate of 67.5 percent. Despite attempts to ensure anonymity and to convey the importance of participation, we did not achieve total representativeness in sex ratio, age distribution, and certain educational and occupational categories. A companion paper reports on the population estimates of the variables under study.

Canada↗

PubMed2004

A sample size computation method for non-linear mixed effects models with applications to pharmacokinetics models.

We propose a simple method to compute sample size for an arbitrary test hypothesis in population pharmacokinetics (PK) studies analysed with non-linear mixed effects models. Sample size procedures exist for linear mixed effects model, and have been recently extended by Rochon using the generalized estimating equation of Liang and Zeger. Thus, full model based inference in sample size computation has been possible. The method we propose extends the approach using a first-order linearization of the non-linear mixed effects model and use of the Wald chi(2) test statistic. The proposed method is general. It allows an arbitrary non-linear model as well as arbitrary distribution of random effects characterizing both inter- and intra-individual variability of the mixed effects model. To illustrate possible uses of the method we present tables of minimum sample sizes, in particular, with an illustration of the effect of sampling design on sample size. We demonstrate how (D-)optimal or frequent sampling requires fewer subjects in comparison to a sparse sampling design. We also present results from Monte Carlo simulations showing that the computed sample size can produce the desired power. The proposed method greatly reduces computing times compared with simulation-based methods of estimating sample sizes for population PK studies.

Black People↗

PubMed2003

Critical assessment of side-chain conformational space sampling procedures designed for quantifying the effect of side-chain environment.

We introduce a family of procedures designed to sample side-chain conformational space at particular locations in protein structures. These procedures (CRSP) use intensive cycles of random assignment of side-chain conformations followed by minimization to determine all the conformations that a group of side-chains can adopt simultaneously. First, we consider a procedure evolving in the dihedral space (dCRSP). Our results suggest that it can accurately map low-energy conformations adopted by clusters of side-chains of a protein. dCRSP is relatively insensitive to various important parameters, and it is sufficiently accurate to capture efficiently the constraint induced by the environment on the conformations a particular side-chain can adopt. Our results show that dCRSP, compared with molecular dynamics (MD), can overcome the problem of the limited set of conformations reached in a reasonable amount of simulations. Next, we introduce procedures (vCRSP) in which valence angles are relaxed, and we assess how efficiently they quantify the conformational entropy of side-chains in the protein native state. For simple peptides, entropies obtained with vCRSP are fully compatible with those obtained with a Monte Carlo procedure. For side-chains in a protein environment, however, vCRSP appears of limited use. Finally, we consider a two-step procedure that combines dCRSP and vCRSP. Our tests suggest that it is able to overcome the limitations of vCRSP. We also note that dCRSP provides a reasonable initial approximation. This family of procedures offers promise in quantifying the contribution of conformational entropy to the energetics of protein structures.

Algorithms↗

PubMed2003

Estimates, power and sample size calculations for two-sample ordinal outcomes under before-after study designs.

Sample size calculations are given for comparing two groups of subjects, typically referring to active and non-active intervention groups, on an ordinal outcome in experiments where the subjects are measured before and after intervention. These calculations apply to log-odds models with random intercepts, treatment, time and treatment-by-time interaction terms, the latter being the term of interest. The assumed forms of the odds ratios are flexible, allowing for proportional odds, adjacent categories, or other conditional models for ordinal responses. Simulations studies show that, for given sample sizes, the nominal and actual powers of the proposed test are similar.

Clinical Trials as Topic↗

PubMed1989

Single-stage cluster sampling with a telescopic respondent rule: a variation motivated by a survey of dementia in elderly residents of Shanghai.

In this report, we consider the situation in which one wishes to identify a cohort of a specified number of individuals within each of several domains for future follow-up studies based on a single-stage cluster sampling design. We develop sample size formulae relevant to this situation and introduce a variation of single-stage cluster sampling that seems more suitable in this situation than is ordinary single-stage cluster sampling. The basis for this variation is the concept that the definition of eligible respondents is not the same for all clusters. The use of this modified respondent rule (which we call telescopic) enables one to meet specified sample sizes in all domains of interest without the need to sample extra individuals in some domains. We used a version of this sampling design successfully in the field with a survey of elderly persons conducted in Shanghai, People's Republic of China.

Age Factors↗

PubMed1992

Application of theoretically optimal sampling schedule designs for fiber digestion estimation in sacco.

Three different geometrically spaced sampling schedule designs, a theoretically optimal design, and a design that included all sampling times were evaluated by comparing parameter estimates, half-life, R2, and an indicator of variance-covariance space. Alfalfa and oat hays were tested using nylon bags placed in the rumen of a fistulated, non-lactating cow, and the amount of NDF remaining was measured at specified times. Parameters were estimated from f(t, phi) = Ae-K(t-lag) + U, where f (t, phi) = NDF at time t (h), A = degradable NDF, U = undegradable NDF, lag = time before digestion, and K = rate constant (h). A, U, and f(t, phi) are expressed as a fraction of DM at time 0. Estimates A and U did not fluctuate, whereas K and lag varied across designs. All R2 were over .96 and did not vary across designs. Comparison of designs that had the same number of observations showed that the indicator of the variance-covariance space was statistically similar across designs, although the optimal design was ranked best. Parameter estimates were similar when using different sampling schedule designs, but some estimates differed by 29%. The optimal design sampling schedule provided sufficient information to estimate parameters without loss of accuracy when compared with other designs.

Animal Feed↗

PubMed2003

Balancing the number and size of sites: an economic approach to the optimal design of cluster samples.

The design of randomized controlled trials entails decisions that have economic as well as statistical implications. In particular, the choice of an individual or cluster randomization design may affect the cost of achieving the desired level of power, other things being equal. Furthermore, if cluster randomization is chosen, the researcher must decide how to balance the number of clusters, or "sites," and the size of each site. This article investigates these interrelated statistical and economic issues. Its principal purpose is to elucidate the statistical and economic trade-offs to assist researchers to employ randomized controlled trials that have desired economic, as well as statistical, properties.

Cluster Analysis↗

PubMed2002

Adaptive sampling in research on risk-related behaviors.

This article introduces adaptive sampling designs to substance use researchers. Adaptive sampling is particularly useful when the population of interest is rare, unevenly distributed, hidden, or hard to reach. Examples of such populations are injection drug users, individuals at high risk for HIV/AIDS, and young adolescents who are nicotine dependent. In conventional sampling, the sampling design is based entirely on a priori information, and is fixed before the study begins. By contrast, in adaptive sampling, the sampling design adapts based on observations made during the survey; for example, drug users may be asked to refer other drug users to the researcher. In the present article several adaptive sampling designs are discussed. Link-tracing designs such as snowball sampling, random walk methods, and network sampling are described, along with adaptive allocation and adaptive cluster sampling. It is stressed that special estimation procedures taking the sampling design into account are needed when adaptive sampling has been used. These procedures yield estimates that are considerably better than conventional estimates. For rare and clustered populations adaptive designs can give substantial gains in efficiency over conventional designs, and for hidden populations link-tracing and other adaptive procedures may provide the only practical way to obtain a sample large enough for the study objectives.

Adolescent↗

PubMed2002

A note on sample size calculation for mean comparisons based on noncentral t-statistics.

One-sample and two-sample t-tests are commonly used in analyzing data from clinical trials in comparing mean responses from two drug products. During the planning stage of a clinical study, a crucial step is the sample size calculation, i.e., the determination of the number of subjects (patients) needed to achieve a desired power (e.g., 80%) for detecting a clinically meaningful difference in the mean drug responses. Based on noncentral t-distributions, we derive some sample size calculation formulas for testing equality, testing therapeutic noninferiority/superiority, and testing therapeutic equivalence, under the popular one-sample design, two-sample parallel design, and two-sample crossover design. Useful tables are constructed and some examples are given for illustration.

Algorithms↗

PubMed2002

Spurious results in therapeutic drug monitoring research.

Maximal correlation between measured blood concentration of a drug and an estimate of the area under the concentration-time curve (AUC) is widely used as criterion for the optimal blood sampling time-point in therapeutic drug monitoring (TDM) research. (More generally, the correlation between an estimate of AUC and a linear combination of several concentration measurements is considered, but the principles are the same.) This particular TDM research methodology is evaluated from a theoretical statistical perspective by considering a general nonspecific study. It is shown that the TDM research methodology produces spurious results because the optimal time-point is determined by irrelevant factors. Particularly, the sampling design is an important determinant. The sampling time-points are of course the only candidates for the optimal time-point, but they may also determine which candidate is optimal. In a special case, it is mathematically proven that any time-point except the first (trough level) can be made optimal by choosing the appropriate sampling design. This is probably true in all practical situations. The theoretical optimum is defined as the optimal time-point in the ideal theoretical sampling design where concentration measurements are made continuously in time. Hence, the theoretical optimum is independent of sampling designs, and the optimal time-point of a study is an approximation to the theoretical optimum. In a homogeneous study population, it can be proven, mathematically and under realistic assumptions, that the theoretical optimum is t(max). Particularly t(max), is the individual theoretical optimum. Heterogeneity of the study population can be an important determinant of the optimal time-point. In significantly heterogeneous study populations, the optimal time-point is usually extreme compared with the distribution of the individual optimal time-points in the population.

Algorithms↗