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PubMed2000

Chorion villus sampling versus amniocentesis for prenatal diagnosis.

BACKGROUND: Amniocentesis test results are usually available only after 18 weeks gestation. Chorion villus sampling (CVS) may be performed transabdominally or transvaginally, usually between 10 and 12 weeks gestation. OBJECTIVES: The objective of this review was to assess the safety and accuracy of chorion villus sampling compared to amniocentesis. SEARCH STRATEGY: We searched the Cochrane Pregnancy and Childbirth Group trials register. SELECTION CRITERIA: Randomised trials comparing first trimester chorion villus sampling and second trimester amniocentesis. DATA COLLECTION AND ANALYSIS: Trial quality was assessed. MAIN RESULTS: Three studies involving over 9000 women were included. The trials were generally of good quality. Compared to amniocentesis, chorion villus sampling was associated with more sampling and technical failures, and more false positive and false negative results. Pregnancy loss was more common after chorion villus sampling (odds ratio 1.33, 95% confidence interval 1.17 to 1.52). There is a suggestion (though not statistically significant) of an increase in stillbirths and neonatal deaths following chorion villus sampling. Maternal complications were uncommon. REVIEWER'S CONCLUSIONS: The increase in miscarriages after chorion villus sampling compared to amniocentesis appear to be procedure related. Second trimester amniocentesis appears to be safer than chorion villus sampling. The benefits of earlier diagnosis with chorion villus sampling must be set against the greater risk of pregnancy loss.

Chorionic Villi Sampling↗
PubMed1992

Segregation analysis of 159 soft tissue sarcoma kindreds: comparison of fixed and sequential sampling schemes.

In this study we compared parameter estimates and model hypotheses in pedigree data collected by fixed sampling with estimates and hypotheses derived by sequential sampling. Employing a fixed sampling scheme, we previously analyzed data on relatives of 159 childhood sarcoma patients. We have now extracted from that data set individuals who would have been included in a sequentially sampled study. We applied segregation analysis to the truncated data, to determine the mode of inheritance and major locus parameter estimates. With data from both sampling schemes we made a family-by-family comparison to determine each family's contribution to a major gene model. The two sampling schemes yielded similar results: we detected segregation of a dominant major gene and obtained similar major locus parameter estimates. However, the sequential sampling scheme derived these conclusions from data on 982 relatives rather than the 2,451 ascertained in the fixed sampling scheme. The sequential sampling scheme failed to identify only one of the kindreds likely to be segregating the gene. For this data set, the sequential sampling scheme would have provided an efficient mechanism to discriminate genetic hypotheses and would have permitted focus of resources on the specific kindreds likely to segregate a major gene.

Adolescent↗
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↗
PubMed2004

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination.

Breast Neoplasms↗
PubMedDate not supplied

A method for determining the sampling ratio in epidemiologic studies.

This paper presents a new method for determining the optimal sampling ratio and sample size in different types of study designs involving binary exposure and disease variables. The sampling ratio is optimized by maximizing cost efficiency, which is the ratio of the precision in effect estimation to the total sampling cost. One may easily compute the optimal sampling ratio with a hand calculator, and it is independent of the sample sizes of the compared groups. Optimal sample sizes obtain from use of the optimal sampling ratio in the appropriate asymptotic power function for comparing two proportions or rates with unequal sample sizes. We illustrate the method with a case-control design, compare it with other methods for optimizing the sampling strategy, and discuss it in a practical context.

Cost-Benefit Analysis↗
PubMed2004

Optimal sampling of a population to determine QTL location, variance, and allelic number.

In a population intended for breeding and selection, questions of interest relative to a specific segregating QTL are the variance it generates in the population, and the number and effects of its alleles. One approach to address these questions is to extract several inbreds from the population and use them to generate multiple mapping families. Given random sampling of parents, sampling strategy may be an important factor determining the power of the analysis and its accuracy in estimating QTL variance and allelic number. We describe appropriate multiple-family QTL mapping methodology and apply it to simulated data sets to determine optimal sampling strategies in terms of family number versus family size. Genomes were simulated with seven chromosomes, on which 107 markers and six QTL were distributed. The total heritability was 0.60. Two to ten alleles were segregating at each QTL. Sampling strategies ranged from sampling two inbreds and generating a single family of 600 progeny to sampling 40 inbreds and generating 40 families of 15 progeny each. Strategies involving only one to five families were subject to variation due to the sampling of inbred parents. For QTL where more than two alleles were segregating, these strategies did not sample QTL alleles representative of the original population. Conversely, strategies involving 30 or more parents were subject to variation due to sampling of QTL genotypes within the small families obtained. Given these constraints, greatest QTL detection power was obtained for strategies involving five to ten mapping families. The most accurate estimation of the variance generated by the QTL, however, was obtained with strategies involving 20 or more families. Finally, strategies with an intermediate number of families best estimated the number of QTL alleles. We conclude that no overall optimal sampling strategy exists but that the strategy adopted must depend on the objective.

Alleles↗
PubMed2006

Sampling and mapping a soil erosion cover factor by integrating stratification, model updating and cokriging with images.

Cost-efficient sample designs for collection of ground data and accurate mapping of variables are required to monitor natural resources and environmental and ecological systems. In this study, a sample design and mapping method was developed by integrating stratification, model updating, and cokriging with Landsat Thematic Mapper (TM) imagery. This method is based on the spatial autocorrelation of variables and the spatial cross-correlation among them. It can lead to sample designs with variable grid spacing, where sampling distances between plots vary depending on spatial variability of the variables from location to location. This has potential cost-efficiencies in terms of sample design and mapping. This method is also applicable for mapping in the case in which no ground data can be collected in some parts of a study area because of the high cost. The method was validated in a case study in which a ground and vegetation cover factor was sampled and mapped for monitoring soil erosion. The results showed that when the sample obtained with three strata using the developed method was used for sampling and mapping the cover factor, the sampling cost was greatly decreased, although the error of the map was slightly increased compared to that without stratification; that is, the sample cost-efficiency quantified by the product of cost and error was greatly increased. The increase of cost-efficiency was more obvious when the cover factor values of the plots within the no-significant-change stratum were updated by a model developed using the previous observations instead of remeasuring them in the field.

Cost-Benefit Analysis↗
PubMed2005

Effect of sampling frames on response rates in the WHO MONICA risk factor surveys.

Sample surveys are used to investigate occurrence and determinants of diseases in populations. Their reliability is influenced by quality of sampling frame and response rate. We investigated relationship between sampling frame type and response rates and assessed their impact on non-response bias, using data from the WHO MONICA Project, where 37 centres in 20 countries conducted sample surveys, employing the best locally available sampling frame. Sampling frames fell into three categories: Population registers (PR), electoral registers (ER), and health care registers (HR). Response rate (rrs) was factored into components reflecting quality of sampling frame (contact rate cr) and characterizing willingness of sample members to participate (enrolment rate er). The mean quality score for the sampling frames was 92% for PR, 87% for HR and 85% for ER; they contributed on average 23, 20, and 26% to the respective non-response rates. For all frame types and both sexes the lowest quality score occurred in the age group 35 - 44, suggesting a reduced ability to track migration of a highly mobile population group. The patterns in the age/sex distribution of er indicate at least for males in PR and females in HR a potential for non-response bias. Estimation of non-response bias through an abbreviated questionnaire failed because of low item response. We found that contact rate characterizes sampling frame quality. For all frame types it had a major influence on response rate. It is likely that low er and low cr cause different kind of bias, requiring different measures to minimize their effects.

Adult↗
PubMed1983

The relative efficiencies of matched and independent sample designs for case-control studies.

We have studied the asymptotic and small sample efficiencies of dependent (pair-matched or stratified) and independent samples as design techniques for case-control studies, and of matched, stratified, covariance-adjusted, and crude comparisons as methods of analysis. The asymptotic efficiencies of dependent sample designs relative to independent sample designs with adjustment were found to vary with the strengths of the relationships of disease with exposure and potential confounder: as the relationship with exposure increases, dependent samples lose efficiency; as the relationship with confounder increases, dependent samples gain efficiency. The relative efficiency also depends in a complicated manner on such other factors as the distribution of exposure and the strength of the exposure-confounder relationship. In the majority of situations examined, however, dependent samples were found to be somewhat more efficient than independent samples when confounding was present, while the reverse was true when confounding was absent. Results of small sample simulations do not differ importantly from the asymptotic results, except for pair-matching on a non-confounder, where the inefficiency of matching is greater in small samples.

Monte Carlo Method↗
PubMed2004

Respondent-driven sampling to recruit MDMA users: a methodological assessment.

Recruiting samples that are more representative of illicit drug users is an on-going challenge in substance abuse research. Respondent-driven sampling (RDS), a new form of chain-referral sampling, is designed to eliminate the bias caused by the non-random selection of the initial recruits and reduce other sources of bias (e.g. bias due to volunteerism and masking) that are usually associated with regular chain-referral sampling. This study provides a methodological assessment of the application of RDS among young adult MDMA/ecstasy users in Ohio. The results show that the sample compositions converged to equilibrium within a limited number of recruitment waves, independent of the characteristics of the initial recruits (i.e. seeds). The sample compositions approximated the theoretical equilibrium compositions, and were not significantly different from the estimated population compositions-with the exception that White respondents were over-sampled and Black respondents were under-sampled. The effect of volunteerism and masking on the sampling process was found not to be significant. Though identifying productive seeds and improving the referral rate are significant challenges when implementing RDS, the findings demonstrate that RDS is a flexible and robust sampling method. RDS has the potential to be widely employed in studies of illicit drug-using populations.

Epidemiologic Research Design↗
PubMed1996

Chorionic villus sampling safety. Report of World Health Organization/EURO meeting in association with the Seventh International Conference on Early Prenatal Diagnosis of Genetic Diseases, Tel-Aviv, Israel, May 21, 1994.

Accumulated experience of 138,996 cases of chorionic villus sampling shows that chorionic villus sampling is a safe procedure with an associated fetal loss rate comparable to that of amniocentesis. The chorionic villus sampling registry shows that chorionic villus sampling is currently performed primarily between 9 and 12 weeks' gestation and carried no increased risk of limb reduction defects: the overall incidence of limb reduction defects after chorionic villus sampling is 5.2 to 5.7 per 10,000, compared with 4.8 to 5.97 per 10,000 in the general population. Analysis of the pattern distribution of limb defects after chorionic villus sampling revealed no difference from the pattern in the general population. This applies specifically to transverse limb defects. Together with the overall incidence of limb reduction defects, these data provide no evidence for any risk for congenital malformation determined by chorionic villus sampling. Because chorionic villus sampling is currently performed generally after 8 completed weeks of pregnancy, few data are available for analysis of complications related to earlier procedures. Avoiding early chorionic villus sampling also excludes sampling in cases of early fetal death, which can be diagnosed reliably by ultrasonography at 9 weeks of pregnancy.

Chorionic Villi Sampling↗
PubMed2000

Approaches to sampling and case selection in qualitative research: examples in the geography of health.

This paper focuses on the question of sampling (or selection of cases) in qualitative research. Although the literature includes some very useful discussions of qualitative sampling strategies, the question of sampling often seems to receive less attention in methodological discussion than questions of how data is collected or is analysed. Decisions about sampling are likely to be important in many qualitative studies (although it may not be an issue in some research). There are varying accounts of the principles applicable to sampling or case selection. Those who espouse 'theoretical sampling', based on a 'grounded theory' approach, are in some ways opposed to those who promote forms of 'purposive sampling' suitable for research informed by an existing body of social theory. Diversity also results from the many different methods for drawing purposive samples which are applicable to qualitative research. We explore the value of a framework suggested by Miles and Huberman [Miles, M., Huberman,, A., 1994. Qualitative Data Analysis, Sage, London.], to evaluate the sampling strategies employed in three examples of research by the authors. Our examples comprise three studies which respectively involve selection of: 'healing places'; rural places which incorporated national anti-malarial policies; young male interviewees, identified as either chronically ill or disabled. The examples are used to show how in these three studies the (sometimes conflicting) requirements of the different criteria were resolved, as well as the potential and constraints placed on the research by the selection decisions which were made. We also consider how far the criteria Miles and Huberman suggest seem helpful for planning 'sample' selection in qualitative research.

Chronic Disease↗
PubMed2001

Sampling variance and distribution of the D' measure of overall gametic disequilibrium between multiallelic loci.

The development of the theory of estimation of gametic disequilibrium for multiallelic systems is particularly necessary, since a large number of the genetic markers available at present are highly polymorphic multiallelic systems. The D' coefficient is one of the most commonly used measures of the extent of overall disequilibrium between all possible pairs of alleles at two multiallelic loci. Nevertheless, the sampling properties of this measure of overall disequilibrium, are to date, unknown. In this work, we have derived explicit expressions by large-sample theory to compute the approximate sampling variance of Dhat' between pairs of multiallelic loci, when samples of haplotypes are taken from populations. Formulae for calculating the asymptotic sampling variance were checked by Monte Carlo simulation. In addition, the magnitude of the sampling variance of Dhat' was investigated under different scenarios of disequilibrium between multiallelic loci. Extensive simulations were also carried out for describing the sampling distribution of Dhat', conditioned on the sample size, number of alleles and their frequencies, and disequilibrium components. It was found that the sampling distribution of Dhat' generally approaches well the theoretical normal distribution for experimental sample sizes, particularly when loci have many alleles. Disequilibrium data between microsatellite loci of human chromosome 11p are used for illustration. These investigations increase substantially our knowledge about this widely used measure of overall disequilibrium, which is relevant to evaluate disequilibrium between multiallelic loci in populations.

Alleles↗
PubMed1999

Sample size calculations in studies using the EuroQol 5D.

Health-related quality of life (HRQoL) instruments are increasingly used as outcome variables in clinical trials, leading to a requirement for sample size calculations based on these variables. This paper aims to provide a guide to sample size calculations for use with the EuroQol-5D. The paper focuses on sample sizes required for comparative studies, and uses scores from two reference groups of general population and critically ill patients to determine sample sizes using the three parts of the EQ-5D (descriptive system, visual analogue scale (VAS), and EQ-5D index). The effect on sample sizes of different methods of categorising the three variables are compared, and comparisons are also made between sample sizes using parametric and non-parametric methods. Sample sizes required when the EQ-5D descriptive system is used as a binary variable (problems/no problems) are higher than or equal to those required when each dimension is categorised in three levels of severity (no problems, moderate problems, extreme problems). The use of three categories is appropriate in ill populations, though in more healthy populations two categories should be used. Due to the slight skewness of VAS data, and the equality of results using parametric and non-parametric methods, sample size calculations using the VAS should be based on a parametric approach. Sample sizes were considerably higher for the EQ-5D index when predefined intervals, as opposed to a score frequency based categorisation, were used with the general population reference group. Using the EQ-5D index in ill populations, it is recommended that sample size calculations are based on parametric methods, whilst in healthier populations non-parametric methods should be used.

Critical Illness↗
PubMed2001

Is mixed effects modeling or naïve pooled data analysis preferred for the interpretation of single sample per subject toxicokinetic data?

The purpose of this study was to evaluate whether mixed effects modeling (MEM) performs better than either noncompartmental or compartmental naïve pooled data (NPD) analysis for the interpretation of single sample per subject pharmacokinetic (PK) data. Using PK parameters determined during a toxicokinetic study in rats, we simulated data sets that might emerge from similar experiments. Data sets were simulated with varying numbers of animals at each sampling time (4-48) and the number of samples taken (1-3) from each individual. Each data set was replicated 50 times and analyzed using several variations of MEM that differed in the assumptions made regarding intraindividual error, NPD, and a graphical noncompartmental method. These analyses attempted to retrieve the underlying parameter and covariate effect values. We compared these analysis methods with respect to how well the underlying values were retrieved. All analysis methods performed poorly with single sample per subject data but MEM gave less biased estimates under the simulated conditions used here. MEM performance increased when covariate effects were sought in the analysis compared with analyses seeking only PK parameters. Decreasing the number of animals used per sampling time from 48 to 16 did not influence the quality of parameter estimates but further reductions (< 16 animals per sampling time) resulted in a reduced proportion of acceptable estimates. Parameter estimate quality improved and worsened with MEM and NPD, respectively, when additional samples were obtained from each individual. Assumptions made regarding the magnitude of intraindividual error were unimportant with single sample per subject data but influenced parameter estimates if more samples were obtained from each individual. MEM is preferable to both NPD and noncompartmental approaches for the analysis of single sample per subject data but even with MEM estimates of clearance are often biased.

Animals↗
PubMed1996

Characterization of AUCs from sparsely sampled populations in toxicology studies.

PURPOSE: The objective of this work was to develop and validate blood sampling schemes for accurate AUC determination from a few samples (sparse sampling). This will enable AUC determination directly in toxicology studies, without the need to utilize a large number of animals. METHODS: Sparse sampling schemes were developed using plasma concentration-time (Cp-t) data in rats from toxicokinetic (TK) studies with the antiepileptic felbamate (F) and the antihistamine loratadine (L); Cp-t data at 13-16 time-points (N = 4 or 5 rats/time-point) were available for F, L and its active circulating metabolite descarboethoxyloratadine (DCL). AUCs were determined using the full profile and from 5 investigator designated time-points termed "critical" time-points. Using the bootstrap (re-sampling) technique, 1000 AUCs were computed by sampling (N = 2 rats/point, with replacement) from the 4 or 5 rats at each "critical" point. The data were subsequently modeled using PCNONLIN, and the parameters (ka, ke, and Vd) were perturbed by different degrees to simulate pharmacokinetic (PK) changes that may occur during a toxicology study due to enzyme induction/inhibition, etc. Finally Monte Carlo simulations were performed with random noise (10 to 40%) applied to Cp-t and/or PK parameters to examine its impact on AUCs from sparse sampling. RESULTS: The 5 time-points with 2 rats/point accurately and precisely estimated the AUC for F, L and DCL; the deviation from the full profile was approximately 10%, with a precision (%CV) of approximately 15%. Further, altered kinetics and random noise had minimal impact on AUCs from sparse sampling. CONCLUSIONS: Sparse sampling can accurately estimate AUCs and can be implemented in rodent toxicology studies to significantly reduce the number of animals for TK evaluations. The same principle is applicable to sparse sampling designs in other species used in safety assessments.

Animals↗
PubMed2004

Using administrative healthcare data to recruit study subjects: experience with 'camouflaged sampling'.

PURPOSE: To recruit a sample of asthmatics heterogeneous for short-acting (SA) beta-agonist use while protecting their privacy, and to compare participants recruited via 'camouflaged sampling' to those recruited through media advertising. METHODS: Direct and indirect patient contact using camouflaged sampling was used to recruit a stratified random sample of asthmatics, identified based on their receipt of a prescription for a SA beta-agonist. Volunteers were recruited through media advertising. Recruitment rates were determined for both indirect and direct patient contact, and sampled participants were compared to volunteers recruited through media advertising for differences in SA beta-agonist use, demographic socioeconomic factors, and pulmonary function. RESULTS: 109 and 93 participants were recruited through camouflaged sampling and media advertising, respectively. Direct and indirect patient contact resulted in recruitment rates of 5 and 9%, respectively. Sampled participants were more heterogeneous for SA beta-agonist use, older, more likely to smoke or receive social assistance, and of lower socioeconomic status (SES) than volunteers. There was no difference in the association between SES and the magnitude of SA beta-agonist use between recruiting methods. CONCLUSIONS: Although recruitment rates were lower than anticipated, camouflaged sampling facilitated stratified sampling of a targeted population and resulted in a more heterogeneous sample while protecting patient privacy.

Adrenergic beta-Agonists↗
PubMed2001

Evaluation of existing limited sampling models for busulfan kinetics in children with beta thalassaemia major undergoing bone marrow transplantation.

Busulfan pharmacokinetic parameters are useful in predicting the outcome of allogeneic bone marrow transplantation (BMT). Standard pharmacokinetic measurements require multiple blood samples. Various limited sampling models (LSM) have been proposed for reducing the sample number required for these measurements, essentially for patients with malignant disorders undergoing BMT. This study was undertaken to evaluate the existing LSM for busulfan pharmacokinetics to find out the most suitable method for patients with thalassaemia major undergoing BMT. Busulfan levels in plasma samples were analysed by HPLC. The AUC calculated by non-compartmental analysis using the program 'TOPFIT' was compared with previously published LSMs. Our seven sample pharmacokinetic data for AUC calculation was compared with the published LSMs. The three sample models suggested by Chattergoon et al and Schuler et al showed significant agreement with AUC TOPFIT (R(2) = 0.98 and 0.94, respectively) in our clinical context. Other models resulted in significant over or under representation of observed values (Vassal's model R(2) = 0.61; Chattergoon's two sample model R(2) = 0.84; four sample model R(2) = 0.83; Schuler's two sample model R(2) = 0.79). By these data the three sample LSM proposed by Chattergoon et al and Schuler et al are suitable for calculation of the AUC in patients with thalassaemia major undergoing BMT conditioned with oral busulfan.

Adolescent↗