Modeling manifest variables in longitudinal designs – a two-stage approach

Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data.Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl.Contact:peter@biomatters.com

To introduce growth curve modeling for longitudinal headache research. Longitudinal data play an important role in the study of headache-related outcomes by allowing researchers to test hypotheses about change over time. However, headache researchers are often unfamiliar with the flexibility and power that growth curves can offer in analyzing longitudinal data. The goals of this paper are to introduce growth curve models within the multilevel modeling framework for analyzing longitudinal headache-related data and to show how these models can be applied in practice. Longitudinal data for the empirical example came from publicly available data from Wave I to Wave IV of the National Longitudinal Study of Adolescent to Adult Health. In total, 5608 individuals were included in the study and multilevel models were fit to examine, for individuals with and without adolescent migraine, longitudinal changes in depression from age 13 to 27 years old. Findings showed that individuals varied in their longitudinal depression trajectories. A cubic time trend best approximated the data with depression increasing through adolescence, decreasing during young adulthood, and then beginning to increase again in adulthood. Further, results also indicated that individuals with adolescent migraine had higher levels of depression throughout the age span compared those without adolescent migraine, but the shape of change did not differ across the groups. Growth curve models offer a flexible alternative to traditional statistical methods and can rigorously evaluate a wide array of headache-related hypotheses.

Public management scholars are looking to longitudinal research designs and data to help overcome the many limitations associated with cross-sectional research. However, far less attention has been given to time itself as a research lens for scholars to consider. This article seeks to integrate time as a construct of theoretical importance into a discussion of longitudinal design, data, and public management research. First, I discuss the relative advantages of longitudinal design and data, but also the challenges, limitations, and issues researchers need to consider. Second, I consider the importance of time as a theoretical construct of interest in the pursuit of longitudinal public management research. Third, I offer a brief look at the use of longitudinal design and panel data analyses in the current public management literature. The overview demonstrates a notable absence of public management research considering the attitudes, motives, perceptions, and experiences of individual public employees and managers. Finally, I consider why there are so few longitudinal studies of public employees and point out the issues public management researchers interested in individual employee-level phenomena need to consider when advancing their own longitudinal research designs.

Conducting longitudinal research has been encouraged among family scholars as a way to better understand family process and to measure changes in family structure and relationships over time. When doing longitudinal research, a common problem is the loss of sample members between the first wave of data collection and subsequent waves. Some subjects move between data points and cannot be located, whereas others refuse to continue their participation. Some members of the original sample, especially if it includes older adults, will have died or have become too disabled to participate further in the study. These losses represent an attrition of the original sample over time. These various forms of attrition of a sample result in a potential threat of bias if those who drop out have unique characteristics such that the remaining sample ceases to be representative of the original sample. This bias, known as attrition bias, is assumed to exist when there are significant differences between the initial and later samples. Because the loss of sample members between the first wave of data collection and subsequent waves is relatively common, researchers in longitudinal studies should check for attrition bias. Unchecked bias may be the major threat to longitudinal research (Markides, Dickson, & Pappas, 1982; Norris, 1985). Larzelere and Klein (1987) listed sample attrition as one of the three major problems that researchers face in analyzing longitudinal data. Despite the threat of attrition bias, however, it has received little attention in longitudinal family research. Indeed, few family researchers address this issue when reporting the results of their analyses of longitudinal data. There are two ways attrition can bias a sample. Firs, it may alter the characteristics of the sample, making it no longer representative of the original sample. Because the results of later waves of data are not generalizable to the original population that was sampled, the external validity of the study is threatened. For example, a longitudinal study examining the effects of divorce on the mental health of former spouses may fail to retain those subjects who have become too depressed to respond to the questionnaire. The nonparticipation of this subsample could bias the findings towards a minimization of depression as an outcome of divorce. The second way that selective attrition can bias longitudinal data is by altering the covariance of variables (Goudy, 1985; Norris, 1987). This problem occurs when the underrepresentation of some groups in the longitudinal sample leads to correlations between variables that are different than the true correlations in the original sample. Referring back to the previous example, the underrepresentation of depressed people in the second wave of the study may distort the statistical relation between length of marriage and depression. This poses a potentially severe threat to theory building because of the effect of these distortions on internal validity. TESTING FOR ATTRITION BIAS The most common method for detecting attrition bias in the characteristics of the sample is to use t tests to compare those subjects who responded to all waves of the study with those who dropped out after only one wave. The means of important variables from the first wave, such as socioeconomic status (SES), age, marital satisfaction, and depression, are compared between the two samples to determine if the differences are statistically significantly different. Significant differences in the means of one or more variables indicates the existence of attrition bias. Differences in categorical variables, such as race and marital status, can be determined by using the chi-square statistic. Logit analysis is a second method for detecting differences in characteristics of the sample. A logit equation is developed to estimate the probability that each first wave respondent will participate in later waves. …

With the improvement of -omics and next-generation sequencing (NGS) methodologies, along with the lowered cost of generating these types of data, the analysis of high-throughput biological data has become standard both for forming and testing biomedical hypotheses. Our knowledge of how to normalize datasets to remove latent undesirable variances has grown extensively, making for standardized data that are easily compared between studies. Here we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper (https://github.com/ELELAB/CAncer-bioMarker-Prediction-Pipeline -CAMPP) intended to aid bioinformatic software-users with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. To avoid issues relating to R-package updates, a renv .lock file is provided to ensure R-package stability. Data-management includes missing value imputation, data normalization, and distributional checks. CAMPP performs (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis, and (VI) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist in streamlining bioinformatic analysis of quantitative biological data, whilst ensuring an appropriate bio-statistical framework.

This article examines quantitative panel analysis techniques appropriate for situations in which the researcher models determinants of change in continuous outcomes. One set of techniques, based on the analysis of pooled time-series data sets, has received little attention in the family literature, although there are a number of situations where these techniques would be appropriate, such as the analysis of multiple-wave panel data. The article explores the fixed effect (change-scare) and random effects models based on pooled time-series data and discusses their advantages and disadvantages for the analysis of survey panel data compared with other available approaches. An empirical example of these methods is presented in which the effect of marital duration and number of children on spousal interaction in intact marriages was examined in a four-wave panel sample. A significant development in the field of family research has been the increased availability of large panel survey data sets containing variables of interest to family researchers (e.g., National Survey of Families and Households Panel, Panel Study of Income Dynamics, National Longitudinal Survey of Youth, Marital Instability Over the Life Course Panel). The potential posed by these panel studies is often unrealized because of the greater complexity of panel analyses and the shortage of clear and accessible guidelines for selecting the appropriate analysis models and statistical software. Several guides to panel methods are available (e.g., Campbell, Mutran, & Parker, 1986; Collins & Horn, 1991; Finkel, 1995; Johnson, 1988; Kessler & Greenberg, 1981; Markus, 1979; Menard, 1991). While these guides can be of value, they have the following drawbacks: Some recent developments are not covered, the focus has been largely on methods for two-wave panels, and situations commonly encountered by family researchers in the analysis of survey data, such as missing waves for some respondents, have been neglected. One type of model commonly found in family research involves the analysis of change over time in a continuous dependent variable. For example, a researcher interested in explaining change in marital interaction over the course of a marriage may model the effects of increased duration of the marriage, changes in the number and ages of the children, spells of employment of both spouses, and changes in family income. This situation involves a continuous dependent variable (degree of spousal interaction) with continuous variables (marital duration, income) and events (addition and subtraction of children, spells of employment and unemployment) as explanatory variables. Panel studies of changes in psychological distress brought about by marital dissolution, changes in frequency of sexual intercourse over the duration of the marriage, and the effect of retirement on marital happiness are research problems with similar analytic needs. With two or more waves of panel data and a continuous dependent variable, the researcher has a choice among five basic panel analysis models. These are (a) regression with lagged dependent variables, (b) structural equation models with reciprocal and lagged effects (e.g., LISREL), (c) repeated measures analysis of variance, (d) growth curve and hierarchical effects models, and (e) fixed (change-score) and random effects regression estimators for pooled time-series data sets. Event history, or hazard, models (Teachman, 1982) and panel models for qualitative variables (Clogg, Eliason, & Grego, 1990) are excluded from this list because they are not designed for use with continuous dependent variables. The first three techniques have been widely used with panel data in the family literature, and are generally accessible to the researcher. Many of the regression techniques have been superseded by the structural equation approaches, which are capable of estimating reciprocal effects and control for biases introduced by measurement errors and autocorrelated errors. …

Linear mixed‐effects (LME) models have become a popular tool for analyzing longitudinal data that arise in areas as diverse as clinical trials, epidemiology, agriculture, economics, and geophysics. The increasing popularity of these models is explained by the flexibility they offer in modeling the within‐subject correlation often present in longitudinal data, by the handling of both balanced and unbalanced data, and by the availability of reliable and efficient software for fitting them. This paper describes a general LME model for longitudinal Gaussian data, presenting the model definition and its underlying assumptions. Maximum likelihood and restricted maximum likelihood estimation methods are discussed, as well as asymptotic inference methods. Available software for fitting and analyzing LME models are briefly reviewed. The model and methods are illustrated through an example from a longitudinal growth study.

We estimate growth curve of yam in a longitudinal study with 60 plants comprising of three groups, each group having twenty plants corresponding to 500, 650 and 800 g of seed weight. The study is relevant for appropriate choice of initial weight for plantation and harvest time of yam. Longitudinal growths of Elephant-foot-yam are studied by taking yam from the ground with care in the middle of a production season, then measure underground growth by Archimedean principle and replant the structure for further growth in remaining lifetime. Yam growth curve has a spike and takes a sharp upturn towards the end. Harvest of the crop at mature stage of plant lifetime increases yam yield substantially, as seen in sharp increase of growth curve towards end. Growth slopes before and after intervention while taking interim observation and difference of these two slopes for each plant are considered. These yam characteristics are linear combinations of yield observations, and are seen to follow normal distribution in quantile–quantile plot; the finding has implications in testing of hypothesis concerning slopes. Experimental results obtained in the present study from 60 plants supplement and reconfirm earlier findings of Dasgupta (Growth curve and structural equation modeling, Chap. 1, 1st edn. Springer proceedings in mathematics & statistics. Springer, New York, 2015) on yam growth based on six plants, two plants per above-mentioned seed weights. Growth curve corresponding to seed weight 650 g indicates superior yield when crops are harvested at the end of season. Growth curve variation in each group of seed weight in parametric and nonparametric set-up is also studied. Seed weight 650 g corresponds to growth curve with minimal variation. Above ground biomass of yam, in the middle of crop season, is higher for seed weight 800 g than that for 500 g. Prediction problem of underground yam weight on the basis of observable above ground biomass and plant lifetime is discussed. Accuracy of prediction is high in logarithmic scale, suggesting that the relationship is nonlinear. Growth of plant lifetime that has applications in crop harvest rate and forecasting market supply of yam is approximated by a Weibull model. For some specific choice of parameters, hazard rate of Weibull distribution is shown to be the limiting form of that for folded normal variable, characterising the limiting distribution. An error bound of hazard rate approximation is obtained. Discrete versions of the folded normal distribution and Weibull distribution are also discussed.KeywordsAmorphophallus paeoniifoliusElephant foot yamLongitudinal studyCross sectional studyArchimedean principleQuantileWilcoxon 2-sample U statisticsOrnstein–Uhlenbeck processBurrHazard rateWeibull distributionFolded normal distributionMSC classification 2010:Primary 62P10Secondary 62J02

This study presents growth charts of preschool based on cross-sectional data of the population in Riyadh, Saudi Arabia. Charts on weights and heights per age were designed separately for boys and girls. Boys consistently weighed more than girls for this age group at every age. Also, the boys were taller than the girls after the age of one year. Since the growth deficits in children from Saudi Arabia compared with internationally recommended reference populations may be attributed to genetic and environmental factors, the charts presented will suffice as a standard in monitoring the growth of Saudi children in particular and those in the Gulf region in general.

Fetal renal growth as assessed through renal parenchymal area derived from prenatal and perinatal ultrasonography.

1. Growth models for body mass and length were fitted to data collected from 1842 sea otters Enhydra lutris shot or live-captured throughout south-west Alaska between 1967 and 2004. Growth curves were constructed for each of two main year groups: 1967-71 when the population was at or near carrying capacity and 1992-97 when the population was in steep decline. Analyses of data collected from animals caught during 2004, when the population density was very low, were precluded by a small sample size and consequently only examined incidentally to the main growth curves. 2. Growth curves demonstrated a significant increase in body mass and body length at age in the 1990s. Asymptotic values of body mass were 12-18% higher in the 1990s than in the 1960s/70s, and asymptotic values for body length were 10-11% higher between the same periods. Data collected in 2004 suggest a continued increase in body size, with nearly all data points for mass and length falling significantly above the 1990s growth curves. 3. In addition to larger asymptotic values for mass and length, the rate of growth towards asymptotic values was more rapid in the 1990s than in the 1960s/70s: sea otters reached 95% of asymptotic body mass and body length 1-2 years earlier in the 1990s. 4. Body condition (as measured by the log mass/log length ratio) was significantly greater in males than in females. There was also an increasing trend from the 1960s/70s through 2004 despite much year-to-year variation. 5. Population age structures differed significantly between the 1960s/70s and the 1990s with the latter distribution skewed toward younger age classes (indicating an altered lx function) suggesting almost complete relaxation of age-dependent mortality patterns (i.e. those typical of food-limited populations). 6. This study spanned a period of time over which the population status of sea otters in the Aleutian archipelago declined precipitously from levels at or near equilibrium densities at some islands in the 1960s/70s to < 5% of estimated carrying capacity by the late 1990s. The results of this study indicate an improved overall health of sea otters over the period of decline and suggest that limited nutritional resources were not the cause of the observed reduced population abundance. Our findings are consistent with the hypothesis that the decline was caused by increased killer whale predation.

The rising field of integrative bioinformatics provides the vital methods to integrate, manage and also to analyze the diverse data and allows gaining new and deeper insights and a clear understanding of the intricate biological systems. The difficulty is not only to facilitate the study of heterogeneous data within the biological context, but it also more fundamental, how to represent and make the available knowledge accessible. Moreover, adding valuable information and functions that persuade the user to discover the interesting relations hidden within the data is, in itself, a great challenge. Also, the cumulative information can provide greater biological insight than is possible with individual information sources. Furthermore, the rapidly growing number of databases and data types poses the challenge of integrating the heterogeneous data types, especially in biology. This rapid increase in the volume and number of data resources drive for providing polymorphic views of the same data and often overlap in multiple resources.In this thesis a multi-pronged approach is proposed that deals with various methods for the analysis and representation of the diverse biological data which are present in different data sources. This is an effort to explain and emphasize on different concepts which are developed for the analysis of molecular data and also to explain its biological significance. The hypotheses proposed are in context with various other results and findings published in the past. The approach demonstrated also explains different ways to integrate the molecular data from various sources along with the need for a comprehensive understanding and clear projection of the concept or the algorithm and its results, but with simple means and methods. The multifarious approach proposed in this work comprises of different tools or methods spanning significant areas of bioinformatics research such as data integration, data visualization, biological network construction / reconstruction and alignment of biological pathways. Each tool deals with a unique approach to utilize the molecular data for different areas of biological research and is built based on the kernel of the thesis. Furthermore these methods are combined with graphical representation that make things simple and comprehensible and also helps to understand with ease the underlying biological complexity. Moreover the human eye is often used to and it is more comfortable with the visual representation of the facts.

BackgroundNew technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity.ResultsWe illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types.ConclusionsThis work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights.

Many areas of psychological, social, and health research are characterised by hierarchically structured data. Growth curves are usually represented by means of a two-level hierarchical structure in which observations are the first-level units nested within subjects, the second-level units. With data such as these, the best option for analysis is the general linear mixed model, which can be used even with longitudinal data series in which intervals are not constant or for which over the passage of time there is loss of data. In this paper an overview is given of the general linear mixed model approach to the analysis of longitudinal data in developmental research. The advantages of this model in comparison with the traditional approaches for analysing longitudinal data are shown, emphasising the usefulness of modelling the covariance structure properly to achieve a precise estimation of the parameters of the model.

Growth curve mixture models with unknown covariance structures