Related Covariates Research Articles

Assessment of actual significance levels for covariate effects in NONMEM.

The objectives of this study were to assess the difference between actual and nominal significance levels, as judged by the likelihood ratio test, for hypothesis tests regarding covariate effects using NONMEM, and to study what factors influence these levels. Also, a strategy for obtaining closer agreement between nominal and actual significance levels was investigated. Pharmacokinetic (PK) data without covariate relationships were simulated from a one compartment i.v. bolus model for 50 individuals. Models with and without covariate relationships were then fitted to the data, and differences in the objective function values were calculated. Alterations were made to the simulation settings; the structural and error models, the number of individuals, the number of samples per individual and the covariate distribution. Different estimation methods in NONMEM were also tried. In addition, a strategy for estimating the actual significance levels for a specific data set, model and parameter was investigated using covariate randomization and a real data set. Under most conditions when the first-order (FO) method was used, the actual significance level for including a covariate relationship in a model was higher than the nominal significance level. Among factors with high impact were frequency of sampling and residual error magnitude. The use of the first-order conditional estimation method with interaction (FOCE-INTER) resulted in close agreement between actual and nominal significance levels. The results from the covariate randomization procedure of the real data set were in agreement with the results from the simulation study. With the FO method the actual significance levels were higher than the nominal, independent of the covariate type, but depending on the parameter influenced. When using FOCE-INTER the actual and nominal levels were similar. The most important factors influencing the actual significance levels for the FO method are the approximation of the influence of the random effects in a nonlinear model, a heteroscedastic error structure in which an existing interaction between interindividual and residual variability is not accounted for in the model, and a lognormal distribution of the residual error which is approximated by a symmetric distribution. Estimation with FOCE-INTER and the covariate randomization procedure provide means to achieve agreement between nominal and actual significance levels.

Evaluation of navigation errors based on ergodic processes

The problems of estimation and test of navigation errors are discussed in this paper. Based on Poor's estimation algorithm, the error model of a navigation system is studied from the stochastic process point of view, and the ergodicity of a stochastic process is extended to the ergodicity of a multivariate stochastic process. It is pointed out that Poor's estimation algorithm should be based on the ergodicity of a multivariate stochastic process for practical use and a sufficient condition is given for its application. In addition, Poor's estimation algorithm is extended to the case of any n navigation systems. The hypothesis test problem of a navigation system accuracy is also discussed in this paper. By using the multivariate statistical analysis theory, the hypothesis test problem of the error covariance matrix of every navigation system can be solved by testing the hypothesis of the relative error covariance matrix between two different navigation systems.

ESTIMATING DORMANCY AND SURVIVAL OF A RARE HERBACEOUS PERENNIAL USING MARK–RECAPTURE MODELS

Mark-recapture statistics have rarely been applied to plants, yet they can be useful for estimating plant demographic traits where individuals may be missed or unob- servable. We applied mark-recapture statistics based on an information theory approach to estimate annual probabilities of dormancy in a five-year study of a threatened, perennial plant, the small yellow lady's slipper orchid ( Cypripedium calceolusssp. parviflorum (Sal- isb.) Fernald). The aboveground states of 548 genets in eight patches were monitored over five years. Apparent survival probabilities (f), corrected for dormancy, were calculated. The best-fit model suggested that apparent survival was constant throughout the study for all patches, while dormancy varied additively with time among patches. The mean prob- ability of dormancy was 0.320 6 0.024, with a mean maximum overestimation of 0.067 as calculated using an estimate of the probability of detection. Dormancy typically lasted for no longer than two consecutive years, although dormancy as long as four years was observed. Dormancy displayed a strong covariate relationship with spring frost days, al- though effects of precipitation and mean spring temperature were almost equally strong. Mean apparent survival probabilities were high in each patch ( f5 0.878), but dormancy probabilities varied considerably among patches (d 5 0.188-0.672). Conventional re- sprouting probabilities underestimated apparent survival by a mean difference of 0.288 (range: 0.150-0.589). This novel application of mark-recapture statistics to plant demog- raphy allowed robust survival estimates that accounted for uncertainty due to an unob- servable, dormant life stage.

Analyzing data with clumping at zero: An example demonstration

This article demonstrates the use of two approaches to analyzing the relationship of multiple covariates to an outcome which has a high proportion of zero values. One approach is to categorize the continuous outcome (including the zero category) and then fit a proportional odds model. Another approach is to use logistic regression to model the probability of a zero response and ordinary least squares linear regression to model the non-zero continuous responses. The use of these two approaches was demonstrated using outcomes data on hours of care received from the Springfield Elder Project. A crude linear model including both zero and non-zero values was also used for comparison. We conclude that the choice of approaches for analysis depends on the data. If the proportional odds assumption is valid, then it appears to be the method of choice; otherwise, the combination of logistic regression and a linear model is preferable.

Shear stress statistics and wall similarity analysis in turbulent boundary layers using a high-resolution 3-D ADVP

Three-dimensional (3-D) quasi-instantaneous acoustic Doppler velocity profiles at the center of uniform, turbulent open-channel flow over smooth and rough beds have been analyzed for the dynamics of coherent structures. The qualitative aspects of simultaneously measured Eulerian velocity and shear stress signatures identify coherent structures in the water column. A cumulant discard method is applied to describe the statistical properties of the covariance terms u'w' along the mean flow and v'w' across the mean flow relative to their time means. Conditional statistics and conditional sampling are used to compare the theoretical and experimental relative covariance contributions from the four quadrants in the longitudinal and transverse planes. The results in the (u', w') plane show the dominance of ejections (quadrant 2; u' 0) and sweeps (quadrant 4; u'>0, w'<0). In contrast, the distribution of fractional v'w' events in the transverse plane is quasi-uniform over the four quadrants. Based on these experimentally determined statistical properties of the covariance terms in different flow conditions, a simplified form of the vertical turbulent energy flux in the intermediate flow region is given and the concept of wall similarity in turbulent boundary layers is validated. Since the validity of the wall similarity concept over a wide range of bed roughness has been shown, it is proposed to determine the mean bed friction velocity from the evaluation of the vertical turbulent energy flux.

Body weight characteristics of subjects on asthma medication.

Weight gain is a frequently documented side effect after long-term anti-inflammatory treatment with systemic corticosteroid drugs in patients with asthma. In recent years new types of inhaled corticosteroids have been introduced, which act locally and are more rapidly bio-transformed. Even such corticosteroids may have a detectable, clinically relevant systemic side effect on weight. The aim of this study is to investigate if there is any relationship between body weight and asthma medication. The relationship between asthma medication and body weight was analysed in two combined randomized samples of the adult Swedish population 16-60 y of age (n = 17,912). Multivariate logistic regression analyses were carried out to obtain estimates for (1) body mass index (BMI) indicating 'obesity' (BMI > 29.9 kg/m2) in men and women controlling for self-reported asthma medication, and (2) self-reported asthma medication controlling for BMI. In both cases we furthermore controlled for interview period, age, Swedish region, smoking habits, physical activities and level of education. We found no significantly higher odds for obesity in men (OR = 1.21 (0.55-2.64) or women (OR = 1.97 (0.89-4.38) on asthma medication compared to men and women with pharmacologically untreated asthma even after adjustment for smoking habits, physical activities, level of education and other related co-variables. However, we found significant positive associations between obesity and interview period, age and former smoking and inverse significant relationships with the degree of physical activity. We also found significantly higher adjusted odds for asthma, indicated by self-reported asthma medication, in women (OR = 2.74 (1.91-3.91)) but not in men (OR = 1.57 (0.96-2.56)) with BMI indicating 'obesity'. There is no strong evidence to suggest that modern pharmacological asthma treatment may contribute much to the development of obesity in either men or women on asthma medication. Adjustment for smoking habits, physical activities, level of education and other related co-variables have minor effects on these relationships. Obesity may still be an independent risk factor for asthma since we observed significantly higher odds for self-reported asthma medication in women and an almost significant relationship in men even after control for BMI and other related co-variables.

A Nonparametric Approach to Identifying Latent Relationships in Hierarchical Models

This paper provides a method for nonparametrically modeling the relationship between consumer preference for product features, such as reliability or durability, and covariates that describe consumers and how they use the product. This relationship is of interest to firms designing and delivering products to a market because the extent to which consumers are sensitive to particular features determines the potential profitability of product offerings, and affects decisions relating to appropriate distribution outlets and advertising strategies. The successful identification of these relationships also aids in efficiently targeting marketing activities to specific segments of the consumer population. The relationship between consumer preference for product features and observable covariates is important but is typically unknown. In addition, these relationships are often deeply embedded in a model hierarchy and are not observed directly. For example, in models of household choice, the observed outcomes are multinomial with probabilities driven by latent utilities or values that consumers place on the choice alternatives. These utilities are in turn a function of characteristics, such as price and product features, which are differentially valued. Of primary interest is the relationship between consumer sensitivity to product characteristics and readily observed covariates such as household demographics or aspects of product usage. Because the relationships of interest are not directly observed, it is difficult to draw inferences about them without formal statistical models. This paper presents a three-level hierarchical Bayes model for modeling binary consumer preferences as a function of observable covariates. The hierarchical model nonparametrically estimates the relationships between consumer preferences for product features and the covariates without assuming a specific functional form. A nonparametric model is particularly useful in the exploratory analysis of consumer data in which the primary purpose of the analysis is to generate further questions rather than provide specific answers to well-posed questions. This type of analysis is frequently encountered in marketing where a series of studies are commissioned to better understand the nature of demand. The first level of the hierarchy in the Bayesian model relates the binary consumer choice to the sensitivities of the consumer to product attributes such as brand name, price, reliability, and durability. The second level of the hierarchy models the heterogeneity across consumers using functions that relate attribute sensitivities to observable covariates. This level of the hierarchy also allows each respondent to have unique demand coefficients by introducing random effect components. The third level of the hierarchy specifies a smoothness prior for each of the unknown functions used in the second level. The approach is flexible and works well both when the unknown function can be closely approximated by a linear function and when it cannot be. A Bayesian model selection technique is used to determine which functions can be modeled using a linear function and which ones should be modeled nonparametrically to provide the necessary flexibility to estimate the function accurately. The proposed methodology is illustrated using data from a survey of consumer preferences for features of marine outboard engines that was collected as part of a consulting project. Our analysis focuses on measuring consumer preferences for engine features and their relationships to two variables related to boat length and engine size. Consumer preferences for engine features were obtained through a national survey conducted over the telephone. Preferences were elicited by means of a pairwise evaluation in which respondents chose between two engines that were identical in every respect except for two engine features. The methodology can be modified to allow for more complex comparisons such as conjoint data collected in full profiles. The application of a Bayesian model selection procedure indicates that 4 of the 28 covariate relationships in the model are nonlinear, while the other 24 are linear. The preferences associated with these four functions are involved in 56% of the pairwise comparisons in the study. Therefore, in practice, if the nonlinear functions are not properly estimated there is the potential to draw misleading inferences regarding 56% of the pairwise choices. Firms can use the estimates of the functions relating preferences to covariates in a number of ways. First, they can use the covariates to determine the total number of consumers who have high demand for a particular product feature, and then they can target communication efforts to those individuals. Alternatively, the empirical results can be used as a basis of subsequent analysis to obtain a more complete characterization of a market segment.

The Connection Between Reaction Time and Variation in Reading Ability: Unravelling Covariance Relationships With Cognitive Ability and Phonological Sensitivity

Eighty-one adult participants varying in reading ability completed two choice reaction time (RT) tasks (one auditory and one visual) in conjunction with measures of phonological awareness, general cognitive ability, and word recognition ability. Replicating previous work, a significant correlation between RT and reading ability was obtained. However, several different methods of examining overlapping variance (hierarchical regression, path analysis, commonality analysis) indicated that the zero-order correlation between RT and word recognition ability was largely due to variance shared with phonological awareness and general cognitive ability. RT explained little variance in reading ability after phonological sensitivity had been partialed out and almost no unique variance after phonological sensitivity and general cognitive ability had been partialed out. In addition, the overlap in the variance of RT and phonological processing was almost entirely due to variance shared with intelligence.

Measurement and structural models for children's problem behaviors.

This article considers an analytic strategy for measuring and modeling child and adolescent problem behaviors. The strategy embeds an item response model within a hierarchical model to define an interval scale for the outcomes, to assess dimensionality, and to study how individual and contextual factors relate to multiple dimensions of problem behaviors. To illustrate, the authors analyze data from the primary caregiver ratings of 2,177 children aged 9-15 in 79 urban neighborhoods on externalizing behavior problems using the Child Behavior Checklist 4-18 (T. M. Achenbach, 1991a). Two subscales, Aggression and Delinquency, are highly correlated, and yet unidimensionality must be rejected because these subscales have different associations with key theoretically related covariates.

A General Conditional-Logistic Model for Affected-Relative-Pair Linkage Studies

Model-free LOD-score methods are often employed to detect linkage between marker loci and common diseases, with samples of affected sib pairs. Although extensions of the basic one-disease-locus model have been proposed that allow separate inclusion of other types of affected relative pairs, discordant relative pairs, covariates, or additional disease loci, a unified framework that can handle all of these features has been lacking. In this report, I propose a conditional-logistic parameterization that generalizes easily to include all of these features. Two data examples, one using simulated data and one using type 1 diabetes, illustrate applications of the models.

Patient Knowledge, Awareness, and Delay in Seeking Medical Attention for Malignant Melanoma

We investigated the relationship between patient knowledge, awareness, and delay in seeking medical attention for melanoma. The study population was comprised of 255 cases with cutaneous melanoma newly diagnosed during January 15, 1987 to May 15, 1989, who were part of a population-based case control study. Personal interviews were conducted to obtain information on patient's knowledge of melanoma signs and symptoms, skin awareness, delay in seeking medical attention, and related covariates. The adjusted odds ratio for the association between skin awareness and delay was 0.30 (95% confidence interval 0.12–0.71). Odds ratios ranged from 0.43 to 0.81 for knowledge and delay. Awareness of skin changes was associated with a reduced Breslow depth for stage I melanomas. Individuals who are aware of skin changes and abnormalities appear to be less likely to delay seeking medical attention for melanoma. Knowledge of melanoma signs and symptoms may also contribute to a decreased delay in melanoma diagnosis.

Square-root information filtering and fixed-interval smoothing with singularities

The square-root information filter algorithm and the related covariance smoother algorithm have been generalized to handle singular state-transition matrices and perfect measurements. This allows the use of SRIF techniques for problems with delays and state constraints. The generalized algorithms use complete QR factorization to isolate deterministically known parts of the state and nonsingular parts of the state transition and disturbance influence matrices. These factorizations and the corresponding changes of coordinates are used to solve the recursive least-squares problems that are basic to the SRIF technique. Numerical stability, computation time, and storage requirements are comparable to the traditional SRIF algorithms.

Genes as gerontological variables: genetically heterogeneous stocks and complex systems☆

In gerontological research utilizing animal models, a major general strategy has been the use of uniform genotypes of inbred strains or their F 1 hybrids. These animal models provide standard reference groups that are of major importance in establishing a reliable data base on aging phenomena. There are limitations to their usage, however, particularly in respect to descriptions or evaluations of variances or of covariance relationships. For these purposes, genetically heterogeneous stocks have the advantage that phenotypic variance (and covariance) has a genetic as well as an environmental component. The advantages of genetic heterogeneity are best realized when the stock has been systematically derived (usually by intercrossing of inbred strains) and maintained by a mating scheme of sufficient size to minimize inbreeding. Genetically heterogeneous stocks are of particularly high potential value in the study of complex systems. Some examples of their use in a gerontological context are provided.

Interpreting Within-Field Relationships Between Crop Yields and Soil and Plant Variables Using Factor Analysis

Precision farming technologies allow for collection of large amounts of data from producers' fields. This study used grid-sampling techniques and factor analysis to investigate relationships between several site variables and corn (Zea mays L.) yields on five producer's fields. Sampling positions (112 to 258) were at the intersecting points of grid lines spaced 15 m. Variables measured were soil organic matter, pH, P, K, and NO3-N; residue cover; broadleaf and grass weed control; corn height at two dates, plant population, and grain yield. Correlation and multiple regression analyses showed that some variables were related to corn yields but the variables involved in significant relationships varied among fields. Moreover, the site variables often were highly correlated and the correlations varied among fields. In these conditions multiple regression would be an unreliable analysis tool. Study of covariance relationships among the variables using factor analysis showed that some of the variables measured could be grouped to indicate a number of underlying common factors influencing corn yields. These common factors were soil fertility, weed control, and conditions for early plant growth. Their importance in explaining the yield variability differed greatly among fields. Application of factor analysis to data generated by precision-farming technologies has potential for describing and understanding relationships between measured variables.

Blood Pressure at Age 3–24 Years in a Rural Community in Anhui, China

PURPOSE: This community-based study in Anhui, China examined the sex-specific pattern of blood pressure (BP) and its major determinants between age 3 and 24 years. METHODS: BP, height, weight, and related covariates were obtained from all eligible subjects by consistent methods. A generalized additive model was used to explore independent relations between BP and covariates. The sample for analyses included a total of 14,277 subjects (7244 males and 7033 females). RESULTS: Systolic blood pressure (SBP)increased linearly with age to an apparent plateau around the age of 15 years in girls and 20 years in boys. Diastolic blood pressure (DBP) essentially paralleled SBP. Until the age of 10, both SBP and DBP were similar in boys and girls; thereafter, values for boys surpassed those for girls with an average difference of 9 mmHg and 4 mmHg for SBP and DBP, respectively, at 20 years of age. This sex difference appeared to persist into early adulthood. The independent relation between BP and each of the three major determinants (age, height, and weight) was nonlinear over the age range studied. Sex-specific regression models with linear terms for age, height and weight were developed for each of four age groups (3–9, 10–14, 15–19, 20–24 years). The 5th, 10th, 90th, and 95th percentiles based on the percentage of predicted values can serve as the cutoffs for low, normal-low, normal-high, and high BP. The predicted values were derived from the above regression models that take into account an individual’s sex, age, height, and weight. CONCLUSIONS: This analysis provided important basis for classification of hypertension status and subsequent investigation of environmental risk factors in this population.

Paleohydrology of the Upper Miocene Bicorb Lake (eastern Spain) as inferred from stable isotopic data from inorganic carbonates

The hydrological evolution of the Upper Miocene Bicorb lake (eastern Spain) has been established based on sedimentology, carbonate mineralogy and stable isotopic data from inorganic carbonates from the upper lacustrine unit. Three main subunits have been recognized. In subunit A, the sedimentological features suggest a hydrologically closed lake although the isotopic data are not conclusive. The isotopic compositions of primary carbonates from subunit B display a distribution typical for a hydrologically closed lake, and allow the differentiation of four developmental stages. The first stage was defined by the deposition of gypsum, LMC, dolomite and aragonite, indicating large variations in the water Mg/Ca ratio. During the second and third stages only LMC was deposited suggesting only minor Mg/Ca fluctuations. The δ 13C and δ 18O pattern of the second stage shows a well-defined covariance ( r=0.8), while in the third stage the isotopic data display a negative covariant relationship ( r=−0.8). Changes in dissolved inorganic carbon composition are considered the main factor to explain the variations from positive to negative covariance. In the fourth stage the lake waters underwent large Mg/Ca fluctuations, as indicated by the deposition of LMC, HMC and aragonite. The isotopic trend is also covariant. Finally, subunit C is characterized by LMC and minor amounts of HMC, suggesting minor changes in Mg/Ca ratio. The isotopic features of this subunit are more congruent with a hydrologically open lake. The lowest δ 13C and δ 18O values are very similar in the three subunits, reflecting the most dilute conditions of the lake water, and a uniform isotopic composition of the local meteoric water during these periods. The changes in water balance and solute inputs recorded in the mineralogy and isotopic signatures of carbonates were controlled by the diapiric evolution and changes in the E/P balance.

Product experience and consumer product attribute inference accuracy

Consumers’ product evaluations are often influenced by information contained in their memories. Prior to product evaluations, consumers are often exposed to data that permits them to judge the covariation relationships among different product attributes. However, these attribute covariance perceptions may lead to biased product evaluations. Using an experimental design, this study examines the accuracy of consumers’ product attribute covariance beliefs as a function of their product experience and the relevancy of product information to which they are exposed prior to evaluating product performances. The results indicate that even limited product information affects consumers’ beliefs about product performances on attributes for which no information is available. In other words, specific product information may serve as a cue or indicator for other product characteristics via attribute covariance inferences. The accuracy of these inferences appears to be, at least partly, the function of the consumers’ product experience. Consumers with high levels of product experience are more effective at encoding and retrieving product attribute performance information. Implications of the findings are discussed and suggestions for future research are provided.

Levels of representationality

. The dominant assumptions throughout contemporary philosophy, psychology, cognitive science, and artificial intelligence about the ontology underlying intentionality, and its core of representationality, are those of encodings-some sort of informational or correspondence or covariation relationship between the represented and its representation that constitutes that representational relationship. There are many disagreements concerning details and implementations, and even some suggestions about claimed alternative ontologies, such as connectionism (though none that escape what is argued is the fundamentalflaw in these dominant approaches). One assumption that seems to be held by all, however, usually without explication or defence, is that there is one singular underlying ontology to representationality. In this paper, it is argued that there are in fact quite a number of ontologies that manifest representationality-levels of representationality-and that none of them are the standard ‘manipulations of encoded symbols’ ontology, nor any other variation on the informational approach to representation. Collectively, these multiple representational ontologies constitute a framework for cognition, whether natural or artificial.

Random Effects Linear Models for Process Mean and Variance

We consider random effects models for both the mean and variance of a process where some variations in both the mean and variance can be explained by their related covariate effects. Empirical Bayes procedures are employed to estimate covariate effects. Approaches proposed are applied to evaluate the performance of the Phase V sensors of the tactical remote sensor system of the U.S. Marine Corps in terms of their mean and variance of detection distance with respect to target types and sensitivity levels of a sensor.

New Algorithm for Attitude Determination Using Global Positioning System Signals

A novel technique for Ž nding a point-by-point (deterministic) attitude solution of a vehicle using Global Positioning System phase difference measurements is presented. This technique transforms a general cost function into a more numerically efŽ cient form by determining three-dimensional vectors in either the body or reference coordinate system. Covariance relationships for the new algorithm, as well as methods that minimize the general cost function, are also derived. The equivalence of the general cost functionand transformed cost function is shown for the case of orthogonal baselines or sight lines. Simulation results are shown that demonstrate the usefulness of the new algorithm and covariance expressions.