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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↗

PubMed2005

Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions.

A new simulation-optimization methodology is developed for cost-effective sampling network design associated with long-term monitoring of large-scale contaminant plumes. The new methodology is similar in concept to the one presented by Reed et al. (Reed, P.M., Minsker, B.S., Valocchi, A.J., 2000a. Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resour. Res. 36 (12), 3731-3741) in that an optimization model based on a genetic algorithm is coupled with a flow and transport simulator and a global mass estimator to search for optimal sampling strategies. However, this study introduces the first and second moments of a three-dimensional contaminant plume as new constraints in the optimization formulation, and demonstrates the proposed methodology through a real-world application. The new moment constraints significantly increase the accuracy of the plume interpolated from the sampled data relative to the plume simulated by the transport model. The plume interpolation approaches employed in this study are ordinary kriging (OK) and inverse distance weighting (IDW). The proposed methodology is applied to the monitoring of plume evolution during a pump-and-treat operation at a large field site. It is shown that potential cost savings up to 65.6% may be achieved without any significant loss of accuracy in mass and moment estimations. The IDW-based interpolation method is computationally more efficient than the OK-based method and results in more potential cost savings. However, the OK-based method leads to more accurate mass and moment estimations. A comparison of the sampling designs obtained with and without the moment constraints points to their importance in ensuring a robust long-term monitoring design that is both cost-effective and accurate in mass and moment estimations. Additional analysis demonstrates the sensitivity of the optimal sampling design to the various coefficients included in the objective function of the optimization model.

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↗

PubMed2006

Model selection for incomplete and design-based samples.

The Akaike information criterion, AIC, is one of the most frequently used methods to select one or a few good, optimal regression models from a set of candidate models. In case the sample is incomplete, the naive use of this criterion on the so-called complete cases can lead to the selection of poor or inappropriate models. A similar problem occurs when a sample based on a design with unequal selection probabilities, is treated as a simple random sample. In this paper, we consider a modification of AIC, based on reweighing the sample in analogy with the weighted Horvitz-Thompson estimates. It is shown that this weighted AIC-criterion provides better model choices for both incomplete and design-based samples. The use of the weighted AIC-criterion is illustrated on data from the Belgian Health Interview Survey, which motivated this research. Simulations show its performance in a variety of settings.

Adult↗

PubMed2004

Maximum a posteriori Bayesian estimation of epirubicin clearance by limited sampling.

AIMS: To develop a limited sampling strategy for estimation of epirubicin clearance. METHODS: The data set comprised 1051 concentrations measured in 105 patients with advanced or metastatic breast cancer treated with epirubicin alone. Ten limited sampling designs comprising two or three blood samples were proposed, taken at times identified by D-optimality from population pharmacokinetic parameter estimates. The data set was then truncated to include the sampling times for each of the designs. MAP Bayesian estimates of clearance were generated for each design and compared with clearance estimates obtained using all the data. The limited sampling designs were also validated using a separate data set obtained from 18 patients with either breast cancer or hepatocellular carcinoma. The sensitivity of the best limited sampling designs to sample time recording errors of 0-10% or 10-20% was then assessed using a simulated data set including 200 patients. RESULTS: The optimum sampling times were: end of the injection and 18 min, 40 min, 3 h, 10 h and 48 h after the start of the injection. The best three-sample design included samples at 40 min, 3 h and 48 h and gave unbiased estimates of clearance with an imprecision of 9.1% (95% CI 7.3, 10.5). The best two sample design included samples at 3 and 48 h and gave unbiased estimates of clearance with an imprecision of 12.4% (95% CI 9.6, 14.6). Using the validation data set, these two and three sample designs gave unbiased estimates of clearance with an imprecision of 5.6% (95% CI 3.7, 7.0) and 4.2% (95% CI 2.6, 5.3), respectively. Simulations that included 0-10% or 10-20% errors in the recording of the blood sampling times had negligible effects on the bias and imprecision of clearance estimates. CONCLUSIONS: Limited sampling designs have been identified and validated that estimate epirubicin clearance with adequate precision and without bias from two or three blood samples. These designs also allow flexibility in blood sample collection and are robust with regard to sample time recording errors.

Adult↗

PubMed2005

Impact of the media on adolescent sexual attitudes and behaviors.

BACKGROUND: Adolescents in the United States are engaging in sexual activity at early ages and with multiple partners. The mass media have been shown to affect a broad range of adolescent health-related attitudes and behaviors including violence, eating disorders, and tobacco and alcohol use. One largely unexplored factor that may contribute to adolescents' sexual activity is their exposure to mass media. OBJECTIVE: We sought to determine of what is and is not known on a scientific basis of the effects of mass media on adolescent sexual attitudes and behaviors. Method. We performed an extensive, systematic review of the relevant biomedical and social science literature and other sources on the sexual content of various mass media, the exposure of adolescents to that media, the effects of that exposure on the adolescents' sexual attitudes and behaviors, and ways to mitigate those effects. Inclusion criteria were: published in 1983-2004, inclusive; published in English; peer-reviewed (for effects) or otherwise authoritative (for content and exposure); and a study population of American adolescents 11 to 19 years old or comparable groups in other postindustrial English-speaking countries. Excluded from the study were populations drawn from college students. RESULTS: Although television is subject to ongoing tracking of its sexual content, other media are terra incognita. Data regarding adolescent exposure to various media are, for the most part, severely dated. Few studies have examined the effects of mass media on adolescent sexual attitudes and behaviors: only 12 of 2522 research-related documents (<1%) involving media and youth addressed effects, 10 of which were peer reviewed. None can serve as the grounding for evidence-based public policy. These studies are limited in their generalizability by their cross-sectional study designs, limited sampling designs, and small sample sizes. In addition, we do not know the long-term effectiveness of various social-cultural, technologic, and media approaches to minimizing that exposure (eg, V-Chips on television, Internet-filtering-software, parental supervision, rating systems) or minimizing the effects of that exposure (eg, media-literacy programs). CONCLUSIONS: Research needs to include development of well-specified and robust research measures and methodologies; ongoing national surveillance of the sexual content of media and the exposure of various demographic subgroups of adolescents to that content; and longitudinal studies of the effects of that exposure on the sexual decision-making, attitudes, and behaviors of those subgroups. Additional specific research foci involve the success of various types of controls in limiting exposure and the mitigative effects of, for example, parental influence and best-practice media-literacy programs.

Adolescent↗

PubMed2005

[Cluster sampling: consequences of data analysis on drawing conclusions].

BACKGROUND: Cluster sampling is commonly used since it does not require a sampling frame which lists all the individual enumeration units. However, this sampling design is often less precise than simple random sampling due to frequent homogeneity of individuals within clusters. This note illustrates that the precision of parameters such as mean, prevalence and odds ratio can be biased when the data analysis ignores the sampling design, yielding to possibly erroneous conclusions. METHODS: Data from a cluster sampling among clandestine sex workers in Senegal were used. Two analyses were performed and their results were compared. The first analysis took into account the sampling design (design-based analysis) while the second did not (naïve analysis). RESULTS: The range of confidence intervals in design-based analysis differed from -43% to +84% with regard to those of naive analysis, and different conclusions could be drawn. For instance, the human immunodeficiency virus (HIV) infection in clandestine sex workers was associated with condoms use and perceived risk of HIV infection in design-based analysis but not in naive analysis. CONCLUSION: The data analysis must take into account the sampling design, and this is facilitated by the availability of statistical software with survey analysis capabilities.

Adult↗

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↗