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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↗
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↗
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↗
PubMed1977

Sampling in social gerontology: a method of locating specialized populations.

This paper describes a two-stage sampling design for obtaining probability samples of the elderly and other specialized populations. The first stage enumerates a sample of elderly individuals residing in a probability, sample of households; the second stage involves the mailing of a questionnaire to the eligible respondents located in the first stage. The results of this method for a study of noninstitutionalized elderly in Washington State are reported. These results indicate that (1) elderly individuals will respond in the two-stage sampling design does not appear to increase sample bias above that expected in normal mailed-questionnaire studies, and (3) the method is extremely inexpensive. Use of the two-stage design by social gerontologists is recommended.

Aged↗
PubMed2000

[Prevalence of tobacco use in Switzerland in the 1990's--estimation of consumption trends based on 2 methods].

Smoking prevalence rates in Switzerland in the 1990s++ have been estimated from Perma data, which have been available quarterly since 1991, as well as from the data of the first and second Swiss Health Surveys, conducted in 1992/93 and 1997. Both sources--each providing data on more than 10,000 respondents--have been large-scale surveys that have used different but complementary survey designs. The probabilistic sampling design of the Health Surveys assures representative findings; the Perma data, although obtained through a non-probabilistic sampling design, permits trend analysis as Perma uses multiple measurement points and therefore time-series methodology can be applied. Both Perma and the Health Surveys yielded approximately the same prevalence of 37% male smokers in 1992/93 and 39% in 1997. For females Perma gave 4% higher prevalence rates than the Health Surveys (Surveys 1992/93: 24%; 1997: 31%). For both sexes the increase in total smoking prevalence was accounted for mainly by adolescents and young adults. Whereas the Surveys showed an increase from 29% to 41% (18% to 39%) in males (females) aged 15 to 19 years, the corresponding increase derived from Perma was 50% less. Except for this youngest age-group, differences between the methods remained within standard statistical norms. There is no doubt, however, that smoking in adolescents increased between 1992/93 and 1997.

Adolescent↗
PubMed2003

Optimal sampling schedule design for populations of patients.

Generation of pharmacodynamic relationships in the clinical arena requires estimation of pharmacokinetic parameter values for individual patients. When the target population is severely ill, the ability to obtain traditional intensive blood sampling schedules is curtailed. Population modeling guided by optimal sampling theory has provided robust estimates of individual patient pharmacokinetic parameter values. Because of the wide range of parameter values seen in this circumstance, it is important to know how the range of parameter values in the population affects the timing of the optimal samples. We describe a new, simple technique to obtain optimal samples for a population of patients. This technique uses the nonparametric distribution associated with a nonparametric adaptive grid population pharmacokinetic analysis. We used the distribution from an analysis of 58 patients receiving levofloxacin for nosocomial pneumonia at a dose of 750 mg. The collection of parameter vectors and their associated probabilities were entered into a D-optimal design evaluation by using ADAPT II. The sampling times, weighted for their probabilities, were displayed in a frequency histogram (an expression of how system information varies with time for the population). Such an explicit expression of the time distribution of information allows rational sampling design that is robust not only for the population mean vector, as in traditional D-optimal design theory, but also for large portions of the total population. For levofloxacin, one reasonable six-sample design would be 1.5, 2, 2.25, 4, 4.75, and 24 h after starting a 90-min infusion. Such sampling designs allow informative population pharmacokinetic analysis with precise and unbiased estimates after the maximal a posteriori probability Bayesian step. This allows the highest probability of delineating a pharmacodynamic relationship.

Chromatography, High Pressure Liquid↗
PubMed2001

A general algorithm for optimal sampling schedule design in nuclear medicine imaging.

Optimal sampling schedule (OSS) is of great interest in biomedical experiment design, as it can improve the physiological parameter estimation precision and significantly reduce the samples required. A number of well designed algorithms and software packages have been developed, which deal with the instantaneous measurements at discrete times. However, in nuclear medicine tracer kinetic studies, the imaging systems, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT), take measurements (images) based on continuous accumulation over time intervals. In this case, the existing algorithms cannot be used to design OSS so as to reduce the image frame numbers. In this paper, a general OSS design algorithm for the accumulative measurement is proposed. The potential usefulness of the algorithm is demonstrated by its designing OSS in [18F] fluoro-2-deoxy-D-glucose (FDG) studies with PET to estimate the local cerebral metabolic rate of glucose. The robustness of parameter estimation using the OSS with respect to intra-subject and inter-subject parameter variations is also presented.

Algorithms↗
PubMed1990

Incorporating prior parameter uncertainty in the design of sampling schedules for pharmacokinetic parameter estimation experiments.

An experiment design procedure is proposed for nonlinear parameter estimation studies that formally incorporates prior parameter uncertainty. The design criterion derives from information theory considerations and involves an asymptotic interpretation of the expected posterior information provided by an experiment. A pharmacokinetic sample schedule design problem is used to illustrate and evaluate this information theoretic design strategy. The model considered is commonly used to describe the plasma concentration of a drug following its oral administration. The limitations and advantages of the proposed design procedure are discussed in relation to other previously reported design techniques for incorporating parameter uncertainty.

Administration, Oral↗
PubMed1997

Using aspects of study design in sample size estimation.

The basis of sample size calculations is usually needed in protocols for clinical trials and when publishing results in respected journals. Although a large amount of research has been undertaken on sample size estimation for different trial designs, in practice the methods are rarely used. This paper describes some useful theory that has practical relevance.

Clinical Trials as Topic↗
PubMedDate not supplied

A simple method of sample size calculation for unequal-sample-size designs that use the logrank or t-test.

This paper presents a simple method of calculating sample sizes for unequal-sample-size designs with use of published tables applicable to equal-sample-size design. The method applies to both the logrank test and the t-test. For the power of logrank test, this paper compares the proposed method with existing methods and with the Monte Carlo simulation.

Clinical Trials as Topic↗