Predictors of Chlamydia trachomatis infection among female adolescents: a longitudinal analysis

Screening guidelines recommend testing all sexually active female adolescents for Chlamydia trachomatis during a pelvic examination at each clinic visit. Such criteria have been based on cross-sectional studies; new evaluations should take into account multiple clinic visits and assess whether criteria are appropriate when a prior test is negative and risk factors are absent. Because repeated observations on an individual may be correlated, the authors used the generalized estimating equation method. Little information exists on subsequent risk of infection; as control programs develop, approaches targeting high-risk populations for recurrent infections are needed. Using data on females aged 15-19 years who visited family planning clinics more than once from 1988 to 1992 (n = 26,921) in Region X (Alaska, Idaho, Oregon, and Washington), the authors constructed a retrospective cohort. Teens with chlamydia at their first visit were at high risk for subsequent infection (odds ratio = 1.6, 95% confidence interval 1.4-1.7). Among teens uninfected at the first visit and without risk factors at the second, prevalence at the second visit was 6%. When intervisit correlations using the generalized estimating equation method were taken into account, predictors of chlamydial infection were consistent with those in previous cross sectional studies cervicitis, friable cervix, and multiple, new, or symptomatic sex partner(s). These findings support screening sexually active female adolescents at each visit, even if prior tests results are available.     

When bad isn't all bad: Strategic use of sample information in generalization and stereotyping.

The influence of the reliability and valence of sample information on generalization to in-group and out-group target populations was investigated in 2 studies. In both studies the valence of the information was either favorable (i.e., a positive in-group sample and a negative out-group sample) or unfavorable. The reliability of sample information was manipulated in terms of sample variability (Study 1) and sample size (Study 2). In both studies the perceived central tendency of favorable sample information was directly generalized, but unfavorable and unreliable sample information was not. Moreover, generalizations based on unfavorable and unreliable samples were characterized by enhanced variability estimates for both in-group and out-group populations, thereby undermining unfavorable group differences. These results are discussed in terms of strategic use of sample information in the formation of social stereotypes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measure and statistical test for cross-correlation between paired neuronal spike trains with small sample size

Recent development of multi-unit recording techniques such as optical recording and multi-electrode arrays makes it possible to record neuronal activities from tens or hundreds of neurons simultaneously. To analyze functional connections between these neurons, cross-correlation analysis has been most commonly applied to the hundreds to thousands of pairs of these neurons. However, conventional cross-correlation data needs statistical tests for significance especially when the sample size of recorded spike trains is small. Here, a multiple hypergeometric model based on a transformation of the cross-correlogram data to a 2 x J table has been suggested. The exact p value for significance can be obtained by the generalized Fisher's method with small sample size and a cross-correlation coefficient for the strength of cross-correlation can be obtained based on the R-square analogue for nominal data. For large sample size, chi 2 test can be applied based on the same transformation. Examples of real spike train data set and simulation show that the methods are applicable to the data of multi-unit activity with only tens of spikes. These methods are especially useful when thousands of cross-correlograms need to be screened quickly and automatically.     

Making interpretable forgetting comparisons: Explicit versus hidden assumptions.

G. R. Loftus (see record 1986-05498-001), R. S. Bogartz (see record 1990-09037-001), and others have questioned forgetting comparisons based on tests of interaction between retention and a second variable. A method for comparing forgetting between conditions on the basis of an article by N. H. Anderson (1963) is presented here. The shape method compares the underlying "shapes" of the performance curves. Anderson's shape method is outlined for memory studies, and its inherent assumptions are stated. A statistical test is developed to apply the shape method to realistic situations in which zero retention interval or asymptotic performance data are not available. A power analysis varied the memory curve shape, sample size, and standard deviation and demonstrated the method's ability to detect shape differences. Application to a recent study claiming a forgetting difference yielded the opposite conclusion. Theoretical and practical limitations of Loftus's and Bogartz's methods are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Designing studies to estimate the penetrance of an identified autosomal dominant mutation: cohort, case-control, and genotyped-proband designs

One can obtain population-based estimates of the penetrance of a measurable mutation from cohort studies, from population-based case-control studies, and from genotyped-proband designs (GPD). In a GPD, we assume that representative individuals (probands) agree to be genotyped, and one then obtains information on the phenotypes of first-degree relatives. We also consider an extension of the GPD in which a relative is genotyped (GPDR design). In this paper, we give methods and tables for determining sample sizes needed to achieve desired precision for penetrance estimates from such studies. We emphasize dichotomous phenotypes, but methods for survival data are also given. In an example based on the BRCA1 gene and parameters given by Claus et al. [(1991) Am J Hum Genet 48:232-242], we find that similar large numbers of families need to be studied using the cohort, case-control, and GPD designs if the allele frequency is known, though the GPDR design requires fewer families, and, if one can study mainly probands with disease, the GPD design also requires fewer families. If the allele frequency is not known, somewhat larger sample sizes are required. Surprisingly, studies with mixtures of families of affected and non-affected probands can sometimes be more efficient than studies based exclusively on affected probands when the allele frequency is unknown. We discuss the feasibility and validity of these designs and point out that GPD and GPDR designs are more susceptible to a bias that results when the tendency for an individual to volunteer to be a proband or to be a subject in a cohort or case-control study depends on the phenotypes of his or her relatives.     

Use of missing data methods in longitudinal studies: The persistence of bad practices in developmental psychology.

Developmental science rests on describing, explaining, and optimizing intraindividual changes and, hence, empirically requires longitudinal research. Problems of missing data arise in most longitudinal studies, thus creating challenges for interpreting the substance and structure of intraindividual change. Using a sample of reports of longitudinal studies obtained from three flagship developmental journals—Child Development, Developmental Psychology, and Journal of Research on Adolescence—we examined the number of longitudinal studies reporting missing data and the missing data techniques used. Of the 100 longitudinal studies sampled, 57 either reported having missing data or had discrepancies in sample sizes reported for different analyses. The majority of these studies (82%) used missing data techniques that are statistically problematic (either listwise deletion or pairwise deletion) and not among the methods recommended by statisticians (i.e., the direct maximum likelihood method and the multiple imputation method). Implications of these results for developmental theory and application, and the need for understanding the consequences of using statistically inappropriate missing data techniques with actual longitudinal data sets, are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A control-conditional test for assessing HIV vaccine efficacy in small sample preclinical studies

In this paper, we discuss the analysis of data from small sample animal studies intended to evaluate HIV vaccine efficacy. The focus is on the chimpanzee model with HIV infection, a binary outcome, of primary interest. The problem becomes that of testing for a difference in independent binomial proportions, but aspects of the study design call into question the use of standard approaches. As sample sizes may be as small as one or two per group in this context, it is tempting to utilize previous data; such usage, however, carries a high price in terms of additional assumptions. We present a test, referred to as the control-conditional test, which conditions on the control data and assumes (in a manner of Bayesian estimation) only vague prior information. Comparisons are made with Fisher's exact test and an exact unconditional test. The control-conditional test is also generalized to allow the analysis of data from a differential dose design.     

Modeling multivariate effect sizes.

In this article, we present a flexible approach to the modeling of multiple effect sizes in meta-analysis. The method uses generalized least squares regression to account for interdependence among multiple outcomes within studies and to allow for different numbers of effect sizes across studies. Furthermore, the approach allows great flexibility in modeling linear equations for multivariate outcomes by means of the inclusion of different sets of predictors for each outcome. We use data from studies of the effectiveness of coaching on performance on the Scholastic Aptitude Test to illustrate application of the method. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prevalence of minor psychiatric disorders in an adult African rural community in South Africa

BACKGROUND: This paper reports on a two-stage community-based epidemiological study of selected minor psychiatric disorders conducted on an adult African population in South Africa. METHODS: Using a modified random cluster sampling method, 354 adults were identified as the first-stage sample, with the SRQ-20 being used as a first-stage screen. Clinical interviews based on DSM-IV checklists for generalized anxiety disorder, major depression and dysthymia were administered as the second-stage criterion to 81 subjects from the sample. RESULTS: The weighted prevalence for generalised anxiety and depressive disorders was 23.9% (95% CI 15.1%-32.7%), comprising: generalized anxiety 3.7%, major depression 4.8%, dysthymia 7.3%, and major depression and dysthymia 8.2%. Statistically significant associations were found between caseness and age, marital status, employment, income and educational level. CONCLUSIONS: The results are discussed in relation to comparative local and international data as well as in the context of the current restructuring of the mental-health care system in South Africa from tertiary curative care to integrated primary mental-health care.     

Analysis of clustered data in receiver operating characteristic studies

Clustered data is not simply correlated data, but has its own unique aspects. In this paper, various methods for correlated receiver operating characteristic (ROC) curve data that have been extended specifically to clustered data are reviewed. For those methods that have not yet been extended, suggestions for their application to clustered ROC studies are provided. Various methods with respect to their ability to meet either of two objectives of the analysis of clustered ROC data are compared to consider a variety of ROC indices and their accessibility to researchers. The available statistical methods for clustered data vary in the range of indices that can be considered and in their accessibility to researchers. Parametric models permit all indices to be considered but, owing to computational complexity, are the least accessible of available methods. Nonparametric methods are much more accessible, but only permit estimation and inference about ROC curve area. The jackknife method is the most accessible and permits any index to be considered. Future development of methods for clustered ROC studies should consider the continuation ratio model, which will permit the application of widely available software for the analysis of mixed generalized linear models. Another area of development should be in the adoption of bootstrapping methods to clustered ROC data.     

Design of a study to improve accuracy in reading mammograms

This paper is concerned with the design and analysis of mammography reading studies. In particular we consider studies aimed at evaluating interventions to improve the accuracy with which mammograms are read. A simple randomized design is suggested in which a relatively large group of readers read sets of mammograms before and after an intervention phase. We propose solutions to three difficult statistical issues that arise in the context of such studies: (i) the choice of primary outcome measure; (ii) the data analysis technique to be employed; and (iii) the methodology for calculating sample sizes for readers and images to be read. First, we argue in favor of using sensitivity and specificity as the primary outcome measures rather than receiver operating characteristic (ROC) curves in mammography studies, although the latter are considered state of the art for many types of radiology reading studies. We argue that sensitivity and specificity are more clinically relevant and conceptually more straightforward than ROC curves. Second, we suggest a bivariate approach to data analysis for evaluating intervention effects on sensitivity and specificity. This accommodates the correlations inherent between these measures and allows for estimation of joint effects on them. Finally we propose a method for power calculations that uses computer simulation techniques. Simple formulas for sample size calculations are not available in part because variability in accuracy amongst readers and variation in difficulty among images introduce complexity into power calculations. The simulation method that we propose accommodates such complexity and is easy to implement. The methodology was motivated by a study funded by the Department of Defense to evaluate the potential efficacy of an educational intervention. In the context of this study we illustrate the steps involved in power calculations and apply the data analytic techniques to the sort of data expected to result from this study. Though the proposed methods were motivated by this particular study, the statistical considerations are relevant more broadly in mammography and indeed in other types of radiologic imaging studies. Standards for the conduct of radiologic reading studies are not yet well developed, as they are for randomized clinical trials and for case-control studies. We hope that the discussion in this paper will add to the dialogue necessary for development of such standards.     

Micromethod for analysis for chloride in physiological fluids

A micromethod for the analysis for chloride, based on the chemical precipitation of silver chloride by radial diffusion through agar gel containing silver nitrate, is described. The method is simple to run, requires little or no instrumentation, and requires only 10 mul of sample. Results by coulometric titration (Buchler Cotlove Chloridometer) correlated well for serum (r = 0.961), urine (r = 0.997), cerebrospinal fluid (r = 0.991), and sweat (r = 0.998). Other halide ions or protein do not interfere. Precision studies gave a within-day reproducibility (CV) of 1.3% and a day-to-day variability of 2.1% for a serum sample averaging 115 mmol/liter.     

"The influence of sample size, estimation methods, and model specification on goodness-of-fit assessments in structural equation models": Correction to la Du and Tanaka (1989).

Reports an error in "Influence of sample size, estimation method, and model specification on goodness-of-fit assessments in structural equation models" by Terence J. la Du and J. S. Tanaka (Journal of Applied Psychology, 1989[Aug], Vol 74[4], 625-635). Figure 2 (p. 631) summarizes Katzell's work motivation model and indicates where the trivial misspecification (dashed line) and nontrivial misspecification (starred line) occurred in our model specification condition. The error is in the latter. The starred line should be from Operations and Resources to Extrinsic Rewards and not from Rewards for Performance to Fruity. Our findings are not changed by this error, because we were using Katzell's model and accompanying data base to conduct a sampling study on goodness-of-fit indices and not testing his model. Hence, any of the paths were candidates for the nontrivial misspecification condition. (The following abstract of the original article appeared in record 1989-38703-001.) The problem of assessing fit of structural equation models is reviewed, and two sampling studies are reported that examine the effects of sample size, estimation method, and model misspecification on fit indices. In the first study, the behavior of indices in a known-population confirmatory factor analysis model is considered. In the second study, the same problem in an empirical data set is examined by looking at antecedents and consequences of work motivation. The findings across the two studies suggest that (a) as might be expected, sample size is an important determinant in assessing model fit; (b) estimator-specific, as opposed to estimator-general, fit indices provide more accurate indications of model fit; and (c) the studied fit indices are differentially sensitive to model misspecification. Some recommendations for the use of structural equation model fit indices are given. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Psychological distress and its correlates in secondary school students in Pavia, Italy

Adolescence is a time of social as well as biological transition; nevertheless, there are very few epidemiological studies in this field in Italy. Therefore, we felt it would be useful to conduct a cross-sectional study on a sample of 1346 adolescents aged 14-19 years attending high schools in the Health Authority Area of Pavia (northern Italy) through a multi-dimensional approach, taking into consideration physical and psychological health, life habits, family environment and social life of teen-agers. We used a structured self-administered questionnaire consisting of 264 question items to achieve the study aim, which was to find the variables (among personal data, scholastic, family, relational characteristics and habits) correlated with psychological distress. The results showed that in this sample psychological distress (evaluated by GHQ-30) was significatively (p < 0.005) associated with female sex, problems with school friends and teachers, having at least one immigrant parent (from a region different from that of residence), little love for parents and poor parental psycho-physical health status, staying at home on the weekend, smoking and using psychoactive medicines.     

Prediction of deformation resistance during hot rolling process based on generalized additive model

A deformation resistance model with generalized additive form for hot rolled strip was established based on the measured data of hot strip rolling process. Firstly, a deformation resistance modeling method for hot rolled strip was proposed under the framework of generalized additive models, including pre- analysis of variables, model setting, model estimation and result analysis; the back- fitting algorithm for the estimation of the univariate function of each independent variable was given. Then, the modeling experiment was carried out for Baosteel 1880 finishing mill, the generalized additive model for deformation resistance was established based on large number of strip sample data which covering many kinds of steel grades, the influence of strain temperature, strain and strain rate on deformation resistance was obtained. Practical calculation results show that the accuracy of new model is higher than that of the Baosteel online model. The new model has the advantages of high calculation precision and applicable to wide range of steel grades, and can be used in online process control of hot continuous rolling production.     

Does precontemplation represent a homogeneous stage category? A latent class analysis on German smokers.

The authors examined the subtype structure of smokers classified in the precontemplation stage of change within the transtheoretical model. From a general practice-based sample of 1,499 daily smoking patients from Germany (participation rate 80%), they used a subgroup of 929 smokers who were classified in the precontemplation stage and applied latent class analysis, using the pros and cons of nonsmoking and smoking cessation self-efficacy as the defining variables. Cross-sectional validation of the emerging classes was based on smoking behavior and processes of change variables. For longitudinal validation, generalized estimation equation analyses were used on motivational and abstinence criteria from 6-, 12-, 18-, and 24-month follow-ups. A 4-class model best represented the data. Three subtypes (labeled progressive, immotive, and disengaged pessimistic) were similar to clusters identified in U.S. studies. The 4th (disengaged optimistic), by contrast, was reminiscent of a type that had previously only emerged in a Dutch study. Cross-sectional and longitudinal validation results confirmed the distinctiveness and predictive power of the classes. The findings highlight the importance of tailoring interventions for smoking behavior change to the needs of different subgroups of precontemplating smokers. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Accurate confidence limits for quantiles under random censoring

In survival analysis, estimates of median survival times in homogeneous samples are often based on the Kaplan-Meier estimator of the survivor function. Confidence intervals for quantiles, such as median survival, are typically constructed via large sample theory or the bootstrap. The former has suspect accuracy for small sample sizes under moderate censoring and the latter is computationally intensive. In this paper, improvements on so-called test-based intervals and reflected intervals (cf., Slud, Byar, and Green, 1984, Biometrics 40, 587-600) are sought. Using the Edgeworth expansion for the distribution of the studentized Nelson-Aalen estimator derived in Strawderman and Wells (1997, Journal of the American Statistical Association 92), we propose a method for producing more accurate confidence intervals for quantiles with randomly censored data. The intervals are very simple to compute, and numerical results using simulated data show that our new test-based interval outperforms commonly used methods for computing confidence intervals for small sample sizes and/or heavy censoring, especially with regard to maintaining specified coverage.     

Experimental charge density distribution and its correlation to structural and optical properties of Sm~(3+) doped Nd_2O_3 nanophosphors

Pure and Sm~(3+) doped Nd_2 O_3 nanophosphors were synthesized using modified Pechini method. The phase formation with symmetry of the sample is confirmed by the Rietveld refinement of the powder Xray diffraction(PXRD) data. The surface morphology and the crystallite size were examined using scanning electron microscopy(SEM) and transmission electron microscopy(TEM) and the results confirmed that the synthesized particles are in nanosize. The energy-dispersive X-ray(EDX) analysis was done to confirm the purity of the sample. The optical properties of the sample were studied using ultraviolet-visible range(UV-Vis) spectroscopic analysis and photoluminescence studies. The calculated band gap of the synthesized Nd_2 O_3 was found to be higher than that of bulk Nd_2 O_3. The photoluminescence(PL) of the prepared samples reveals that doping with Sm3+ ion has influenced the optical properties. Quantitative investigation on charge density distribution was done by analysing the 3-dimensional and 2-dimensional charge density maps drawn along the bonding directions. The maximum entropy method(MEM)/Rietveld analysis was used for the first time to analyse the charge density in the chosen system. Charge density arrangement in the unit cell is correlated to the analysed photoluminescent(PL) properties. The spectral behaviour of the samples has been explained through charge ordering which are verified using experimental data obtained. The studies on these materials have shown that these nanophosphors will provide promising application for near-ultraviolet lightemitting diodes(n-UV-LEDs).     

A generalized estimating equations approach for testing ordered group effects with repeated measurements

In repeated measures studies, we are often interested in comparing group effects in which groups are associated with a certain order relation. We propose testing procedures for ordered group effects using the generalized estimating equations (GEE) approach of Liang and Zeger (1986, Biometrika 73, 13-22). The order-constrained GEE estimators of group effects are approximated by the isotonic regression of the unconstrained GEE estimators. Based on these constrained estimators, we construct test statistics for detecting ordered group effects. The limiting distributions of the test statistics are mixtures of chi-square distributions. A Monte Carlo experiment shows improved performances of the proposed tests over the usual chi-square tests in detecting ordered group effects. The proposed test procedures are illustrated by familial polyposis supplementation trial data.     

Comparison of methods for analyzing binary data arising from two-sample twin studies

The study of twins is widely used for research into genetic and environmental influences on human outcome measurements. For the study design in which independent samples of monozygotic and dizygotic twins are compared with respect to their similarity on a binary trait, several statistical methods have been proposed. Using a Monte Carlo simulation, we compare the five following procedures: 1) goodness-of-fit method based on the common correlation model, 2) normal approximation of the maximum likelihood estimators of the common correlation coefficients, 3) Ramakrishnan et al. [(1992) Genet Epidemiol 9:273-282] method of odds ratio comparison, 4) generalized estimating equations method of odds ratio estimation, and 5) tetrachoric correlation method. The results show that the goodness-of-fit approach has similar or better performance in both type-one error rates and power than the other methods in all parameter settings. Its advantage with respect to type-one error rates is particularly clear under conditions of small sample sizes, extreme prevalences, or high values of the intraclass correlation coefficients. Therefore, the goodness-of-fit method is recommended for the two-sample twin study design.