Cross-Sectional but Not Prospective Association of Accelerometry-Derived Physical Activity With Quality of Life in Children and Adolescents

Objectives: This study aims to quantify the cross-sectional and prospective associations between quality of life (QoL) and moderate-to-vigorous physical activity (MVPA). Methods: This study was based on the Swiss children's Objectively measured PHYsical Activity cohort. The primary endpoint is the overall QoL score and its six dimensions. The main predictor is the average time spent in MVPA per day. Linear mixed effects and linear regression models respectively were used to investigate the cross-sectional and prospective associations between MVPA and QoL. Results: There were 352 participants in the study with complete data from baseline (2013-2015) and follow-up (2019). MVPA was positively associated with overall QoL and physical wellbeing (p = 0.023 and 0.002 respectively). The between-subject MVPA was positively associated with the overall QoL, physical wellbeing, and social wellbeing (p = 0.030, 0.017, and 0.028 respectively). Within-subject MVPA was positively associated with physical wellbeing and functioning at school (p = 0.039 and 0.013 respectively). Baseline MVPA was not associated with QoL 5 years later. Conclusion: Future longitudinal studies should employ shorter follow-up times and repeat measurements to assess the PA and QoL association.

Permutation Tests for Assessing Potential Non-Linear Associations between Treatment Use and Multivariate Clinical Outcomes

In many psychometric applications, the relationship between the mean of an outcome and a quantitative covariate is too complex to be described by simple parametric functions; instead, flexible nonlinear relationships can be incorporated using penalized splines. Penalized splines can be conveniently represented as a linear mixed effects model (LMM), where the coefficients of the spline basis functions are random effects. The LMM representation of penalized splines makes the extension to multivariate outcomes relatively straightforward. In the LMM, no effect of the quantitative covariate on the outcome corresponds to the null hypothesis that a fixed effect and a variance component are both zero. Under the null, the usual asymptotic chi-square distribution of the likelihood ratio test for the variance component does not hold. Therefore, we propose three permutation tests for the likelihood ratio test statistic: one based on permuting the quantitative covariate, the other two based on permuting residuals. We compare via simulation the Type I error rate and power of the three permutation tests obtained from joint models for multiple outcomes, as well as a commonly used parametric test. The tests are illustrated using data from a stimulant use disorder psychosocial clinical trial.

Limitations of clinical trial sample size estimate by subtraction of two measurements

In planning randomized clinical trials (RCTs) for diseases such as Alzheimer's disease (AD), researchers frequently rely on the use of existing data obtained from only two time points to estimate sample size via the subtraction of baseline from follow-up measurements in each subject. However, the inadequacy of this method has not been reported. The aim of this study is to discuss the limitation of sample size estimation based on the subtraction of available data from only two time points for RCTs. Mathematical equations are derived to demonstrate the condition under which the obtained data pairs with variable time intervals could be used to adequately estimate sample size. The MRI-based hippocampal volume measurements from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Monte Carlo simulations (MCS) were used to illustrate the existing bias and variability of estimates. MCS results support the theoretically derived condition under which the subtraction approach may work. MCS also show the systematically under- or over-estimated sample sizes by up to 32.27 % bias. Not used properly, such subtraction approach outputs the same sample size regardless of trial durations partly due to the way measurement errors are handled. Estimating sample size by subtracting two measurements should be treated with caution. Such estimates can be biased, the magnitude of which depends on the planned RCT duration. To estimate sample sizes, we recommend using more than two measurements and more comprehensive approaches such as linear mixed effect models.

The Use of Putative Dialysis Initiation Time in Comparative Outcomes of Patients with Advanced Chronic Kidney Disease: Methodological Aspects

The latest data from the United States Renal Data Systems show over 134,000 individuals with end-stage kidney disease (ESKD) starting dialysis in the year 2019. ESKD patients on dialysis, the default treatment strategy, have high mortality and hospitalization, especially in the first year of dialysis. An alternative treatment strategy is (non-dialysis) conservative management (CM). The relative effectiveness of CM with respect to various patient outcomes, including survival, hospitalization, and health-related quality of life among others, especially in elderly ESKD or advanced chronic kidney disease patients with serious comorbidities, is an active area of research. A technical challenge inherent in comparing patient outcomes between CM and dialysis patient groups is that the start of follow-up time is "not defined" for patients on CM because they do not initiate dialysis. One solution is the use of putative dialysis initiation (PDI) time. In this work, we examine the validity of the use of PDI time to determine the start of follow-up for longitudinal retrospective and prospective cohort studies involving CM. We propose and assess the efficacy of estimating PDI time using linear mixed effects model of kidney function decline over time via simulation studies. We also illustrate how the estimated PDI time can be used to effectively estimate the survival distribution.

School-Level Factors within Comprehensive School Health Associated with the Trajectory of Moderate-to-Vigorous Physical Activity over Time: A Longitudinal, Multilevel Analysis in a Large Sample of Grade 9 and 10 Students in Canada

(1) The majority of Canadian youth are insufficiently active, and moderate-to-vigorous physical activity (MVPA) decreases substantially during secondary school. School factors within the comprehensive school health (CSH) framework may help attenuate this decline. This study aimed to examine how youth MVPA changes over a three-year period and evaluate the school characteristics associated with preventing the decline in MVPA over time, guided by the CSH framework. (2) This study uses COMPASS survey data from 78 secondary schools in Ontario and Alberta that participated in Year 2 (2013/14), Year 3 (2014/15), and Year 4 (2015/16), and 17,661 students attending these schools. Multilevel (linear mixed effects) models were used to determine the association between school-level factors and student MVPA (weekly minutes) over time, stratified by gender. (3) Both male and female students had a significant decline in MVPA across the 3 years, with a greater decrease observed among female students. Within the CSH framework, the school's social environment, partnerships, and policies were associated with student MVPA over time, however the specific school factors and directions of associations varied by gender. (4) School-based interventions (e.g., public health partnerships) may help avoid the decline in MVPA observed in this critical period and support student health.

[Heterogeneity of growth and maturity of Larimichthys polyactis in the offshore waters of southern Zhejiang, China]

The variations of life history traits have been observed for many fish species, which gains much concerns in the study of aquatic biology and ecology. In this study, the biological characteristics were explored for yellow croaker (Larimichthys polyactis) in the offshore waters of southern Zhejiang, based on 4920 individuals collected from 13 fishery-independent seasonal surveys from autumn 2015 to autumn 2018. Linear mixed effects models were used to estimate the growth, maturity characteristics, and their heterogeneity. The body length of yellow croaker samples ranged from 13 to 215 mm with the dominant body of 110 to 154 mm. The body weight ranged from 0.5 to 182.2 g, with the dominant body weight from 20 to 55 g. The results showed that the linear mixed effects models with random effects from season, gender, and year performed best for length-weight relationship, with the lowest AIC and RMSE values. The effects of season were much larger than those of genders and years. When the length exceeded 160 mm, the weight gain rate of yellow croaker was faster in spring and summer, lower in autumn and winter, while the male individuals gained more weight than the females with the same body length. Among 4841 individuals of specimens with gonadal data, the individuals at maturity Ⅱ stage occupied 50.4%, and the individuals at maturity stage contributed to 19.6%. The results from the best linear mixed effects model showed that season had the most significant influence on the maturity of yellow croaker. The 50% maturity length (L_50%) was much lower in winter (124.6 mm) with no much difference between other seasons, indicating that yellow croaker matures earlier in winter. Our results indicated that linear mixed effect model could reflect the biological heterogeneity of yellow croaker conveniently and that the growth and maturity of yellow croaker had significantly sexual and temporal variations, which should be considered in the stock assessment and fishery management for yellow croaker.

A tractable method to account for high-dimensional nonignorable missing data in intensive longitudinal data

Despite the need for sensitivity analysis to nonignorable missingness in intensive longitudinal data (ILD), such analysis is greatly hindered by novel ILD features, such as large data volume and complex nonmonotonic missing-data patterns. Likelihood of alternative models permitting nonignorable missingness often involves very high-dimensional integrals, causing curse of dimensionality and rendering solutions computationally prohibitive to obtain. We aim to overcome this challenge by developing a computationally feasible method, nonlinear indexes of local sensitivity to nonignorability (NISNI). We use linear mixed effects models for the incomplete outcome and covariates. We use Markov multinomial models to describe complex missing-data patterns and mechanisms in ILD, thereby permitting missingness probabilities to depend directly on missing data. Using a second-order Taylor series to approximate likelihood under nonignorability, we develop formulas and closed-form expressions for NISNI. Our approach permits the outcome and covariates to be missing simultaneously, as is often the case in ILD, and can capture U-shaped impact of nonignorability in the neighborhood of the missing at random model without fitting alternative models or evaluating integrals. We evaluate performance of this method using simulated data and real ILD collected by the ecological momentary assessment method.

Longitudinal Analysis of Pulmonary Function in Survivors of Congenital Diaphragmatic Hernia

OBJECTIVE

To analyze longitudinal trends of pulmonary function testing in patients with congenital diaphragmatic hernia (CDH) followed in our multidisciplinary clinic.

STUDY DESIGN

This was a retrospective cohort study of CDH patients born between 1991 and 2013. A linear mixed effects model was fitted to estimate the trends of percent predicted forced expiratory volume in 1 second (FEV₁pp), percent predicted forced vital capacity (FVCpp), and FEV₁/FVC over time.

RESULTS

Of 268 patients with CDH who survived to discharge, 119 had at least 1 pulmonary function test study. The FEV₁pp (P < .001), FVCpp (P = .017), and FEV₁/FVC (P = .001) decreased with age. Compared with defect size A/B, those with defect size C/D had lower FEV₁pp by an average of 11.5% (95% CI, 2.9%-20.1%; P = .010). A history of oxygen use at initial hospital discharge also correlated with decreased FEV₁pp by an average of 8.0% (95% CI, 1.2%-15.0%; P = .023).

CONCLUSIONS

In a select cohort of CDH survivors, average pulmonary function declines with age relative to expected population normative values. Those with severe CDH represent a population at risk for worsening pulmonary function test measurements who may benefit from recognition and monitoring for complications.

Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study

BACKGROUND

Diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA), allow examining the structural integrity of the brain. However, the true value of these parameters may be confounded by variability in MR hardware, acquisition parameters, and image quality.

PURPOSE

To examine the effects of confounding factors on FA and to evaluate the feasibility of statistical methods to model and reduce multicenter variability.

STUDY TYPE

Longitudinal multicenter study.

PHANTOM

DTI single strand phantom (HQ imaging).

FIELD STRENGTH/SEQUENCE

3T diffusion tensor imaging.

ASSESSMENTS

Thirteen European imaging centers participated. DTI scans were acquired every 6 months and whenever maintenance or upgrades to the system were performed. A total of 64 scans were acquired in 2 years, obtained by three scanner vendors, using six individual head coils, and 12 software versions.

STATISTICAL TESTS

The variability in FA was assessed by the coefficients of variation (CoV). Several linear mixed effects models (LMEM) were developed and compared by means of the Akaike Information Criterion (AIC).

RESULTS

The CoV was 2.22% for mean FA and 18.40% for standard deviation of FA. The variables "site" (P = 9.26 × 10^-5 ), "vendor" (P = 2.18 × 10^-5 ), "head coil" (P = 9.00 × 10^-4 ), "scanner drift," "bandwidth" (P = 0.033), "TE" (P = 8.20 × 10^-6 ), "SNR" (P = 0.029) and "mean residuals" (P = 6.50 × 10^-4 ) had a significant effect on the variability in mean FA. The variables "site" (P = 4.00 × 10^-4 ), "head coil" (P = 2.00 × 10^-4 ), "software" (P = 0.014), and "mean voxel outlier intensity count" (P = 1.10 × 10^-4 ) had a significant effect on the variability in standard deviation of FA. The mean FA was best predicted by an LMEM that included "vendor" and the interaction term of "SNR" and "head coil" as model factors (AIC -347.98). In contrast, the standard deviation of FA was best predicted by an LMEM that included "vendor," "bandwidth," "TE," and the interaction term between "SNR" and "head coil" (AIC -399.81).

DATA CONCLUSION

Our findings suggest that perhaps statistical models seem promising to model the variability in quantitative DTI biomarkers for clinical routine and multicenter studies.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:955-965.

Development of short-range white matter in healthy children and adolescents

Neural communication is facilitated by intricate networks of white matter (WM) comprised of both long and short range connections. The maturation of long range WM connections has been extensively characterized, with projection, commissural, and association tracts showing unique trajectories with age. There, however, remains a limited understanding of age-related changes occurring within short range WM connections, or U-fibers. These connections are important for local connectivity within lobes and facilitate regional cortical function and greater network economy. Recent studies have explored the maturation of U-fibers primarily using cross-sectional study designs. Here, we analyzed diffusion tensor imaging (DTI) data for healthy children and adolescents in both a cross-sectional (n = 78; mean age = 13.04 ± 3.27 years) and a primarily longitudinal (n = 26; mean age = 10.78 ± 2.69 years) cohort. We found significant age-related differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) across the frontal, parietal, and temporal lobes of participants within the cross-sectional cohort. By contrast, we report significant age-related differences in only FA for participants within the longitudinal cohort. Specifically, larger FA values were observed with age in frontal, parietal, and temporal lobes of the left hemisphere. Our results extend previous findings restricted to long range WM to demonstrate regional changes in the microstructure of short range WM during childhood and adolescence. These changes possibly reflect continued myelination and axonal organization of short range WM with increasing age in more anterior regions of the left hemisphere. Hum Brain Mapp 39:204-217, 2018. © 2017 Wiley Periodicals, Inc.

Evaluation of Approaches to Analyzing Continuous Correlated Eye Data When Sample Size Is Small

PURPOSE

To evaluate the performance of commonly used statistical methods for analyzing continuous correlated eye data when sample size is small.

METHODS

We simulated correlated continuous data from two designs: (1) two eyes of a subject in two comparison groups; (2) two eyes of a subject in the same comparison group, under various sample size (5-50), inter-eye correlation (0-0.75) and effect size (0-0.8). Simulated data were analyzed using paired t-test, two sample t-test, Wald test and score test using the generalized estimating equations (GEE) and F-test using linear mixed effects model (LMM). We compared type I error rates and statistical powers, and demonstrated analysis approaches through analyzing two real datasets.

RESULTS

In design 1, paired t-test and LMM perform better than GEE, with nominal type 1 error rate and higher statistical power. In design 2, no test performs uniformly well: two sample t-test (average of two eyes or a random eye) achieves better control of type I error but yields lower statistical power. In both designs, the GEE Wald test inflates type I error rate and GEE score test has lower power.

CONCLUSION

When sample size is small, some commonly used statistical methods do not perform well. Paired t-test and LMM perform best when two eyes of a subject are in two different comparison groups, and t-test using the average of two eyes performs best when the two eyes are in the same comparison group. When selecting the appropriate analysis approach the study design should be considered.

A Practical Guide to Visualization and Statistical Analysis of R. solanacearum Infection Data Using R

This paper describes and summarizes approaches for visualization and statistical analysis using data from Ralstonia solanacearum infection experiments based on methods and concepts that are broadly applicable. Members of the R. solanacearum species complex cause bacterial wilt disease. Bacterial wilt is a lethal plant disease and has been studied for over 100 years. During this time various methods to quantify disease and different ways to analyze the generated data have been employed. Here, I aim to provide a general background on three distinct and commonly used measures of disease: the area under the disease progression curve, longitudinal recordings of disease severity and host survival. I will discuss how one can proceed with visualization, statistical analysis, and interpretation using different datasets while revisiting the general concepts of statistical analysis. Datasets and R code to perform all analyses discussed here are included in the supplement.

Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia

Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness. Here we address this question using a partitioning-based heritability analysis of genome-wide single-nucleotide polymorphism (SNP) and neuroimaging data from 1750 healthy individuals. We find that schizophrenia-associated genetic variants explain a significantly enriched proportion of trait heritability in eight brain phenotypes (false discovery rate=10%). In particular, intracranial volume and left superior frontal gyrus thickness exhibit significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross-disorder comparison suggests that the neurogenetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders. Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder.

Comparing Analytic Methods for Longitudinal GWAS and a Case-Study Evaluating Chemotherapy Course Length in Pediatric AML. A Report from the Children's Oncology Group

Regression analysis is commonly used in genome-wide association studies (GWAS) to test genotype-phenotype associations but restricts the phenotype to a single observation for each individual. There is an increasing need for analytic methods for longitudinally collected phenotype data. Several methods have been proposed to perform longitudinal GWAS for family-based studies but few methods are described for unrelated populations. We compared the performance of three statistical approaches for longitudinal GWAS in unrelated subjectes: (1) principal component-based generalized estimating equations (PC-GEE); (2) principal component-based linear mixed effects model (PC-LMEM); (3) kinship coefficient matrix-based linear mixed effects model (KIN-LMEM), in a study of single-nucleotide polymorphisms (SNPs) on the duration of 4 courses of chemotherapy in 624 unrelated children with de novo acute myeloid leukemia (AML) genotyped on the Illumina 2.5 M OmniQuad from the COG studies AAML0531 and AAML1031. In this study we observed an exaggerated type I error with PC-GEE in SNPs with minor allele frequencies < 0.05, wheras KIN-LMEM produces more than expected type II errors. PC-MEM showed balanced type I and type II errors for the observed vs. expected P-values in comparison to competing approaches. In general, a strong concordance was observed between the P-values with the different approaches, in particular among P < 0.01 where the between-method AUCs exceed 99%. PC-LMEM accounts for genetic relatedness and correlations among repeated phenotype measures, shows minimal genome-wide inflation of type I errors, and yields high power. We therefore recommend PC-LMEM as a robust analytic approach for GWAS of longitudinal data in unrelated populations.

Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity

Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.

General Framework for Meta-Analysis of Haplotype Association Tests

For complex traits, most associated single nucleotide variants (SNV) discovered to date have a small effect, and detection of association is only possible with large sample sizes. Because of patient confidentiality concerns, it is often not possible to pool genetic data from multiple cohorts, and meta‐analysis has emerged as the method of choice to combine results from multiple studies. Many meta‐analysis methods are available for single SNV analyses. As new approaches allow the capture of low frequency and rare genetic variation, it is of interest to jointly consider multiple variants to improve power. However, for the analysis of haplotypes formed by multiple SNVs, meta‐analysis remains a challenge, because different haplotypes may be observed across studies. We propose a two‐stage meta‐analysis approach to combine haplotype analysis results. In the first stage, each cohort estimate haplotype effect sizes in a regression framework, accounting for relatedness among observations if appropriate. For the second stage, we use a multivariate generalized least square meta‐analysis approach to combine haplotype effect estimates from multiple cohorts. Haplotype‐specific association tests and a global test of independence between haplotypes and traits are obtained within our framework. We demonstrate through simulation studies that we control the type‐I error rate, and our approach is more powerful than inverse variance weighted meta‐analysis of single SNV analysis when haplotype effects are present. We replicate a published haplotype association between fasting glucose‐associated locus (G6PC2) and fasting glucose in seven studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and we provide more precise haplotype effect estimates.

Optimal composite scores for longitudinal clinical trials under the linear mixed effects model

Clinical trials of chronic, progressive conditions use rate of change on continuous measures as the primary outcome measure, with slowing of progression on the measure as evidence of clinical efficacy. For clinical trials with a single prespecified primary endpoint, it is important to choose an endpoint with the best signal‐to‐noise properties to optimize statistical power to detect a treatment effect. Composite endpoints composed of a linear weighted average of candidate outcome measures have also been proposed. Composites constructed as simple sums or averages of component tests, as well as composites constructed using weights derived from more sophisticated approaches, can be suboptimal, in some cases performing worse than individual outcome measures. We extend recent research on the construction of efficient linearly weighted composites by establishing the often overlooked connection between trial design and composite performance under linear mixed effects model assumptions and derive a formula for calculating composites that are optimal for longitudinal clinical trials of known, arbitrary design. Using data from a completed trial, we provide example calculations showing that the optimally weighted linear combination of scales can improve the efficiency of trials by almost 20% compared with the most efficient of the individual component scales. Additional simulations and analytical results demonstrate the potential losses in efficiency that can result from alternative published approaches to composite construction and explore the impact of weight estimation on composite performance. Copyright © 2016. The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.

An actigraphy study of sleep and pain in midlife women: the Study of Women's Health Across the Nation Sleep Study

OBJECTIVE

We examined whether women reporting nighttime pain would have more actigraphy-measured evidence for disturbed sleep and would report feeling less rested compared with women without nighttime pain.

METHODS

Up to 27 consecutive nights of actigraphy and sleep diary data from each participant were analyzed in this community-based study of 314 African-American (n = 118), white (n = 141), and Chinese (n = 55) women, aged 48 to 58 years, who were premenopausal, perimenopausal, or postmenopausal and were participating in the Study of Women's Health Across the Nation Sleep Study. Dependent variables were actigraphy-measured movement and fragmentation index, total sleep time, sleep efficiency, and diary self-report of "feeling rested" after waking up. All outcomes were fitted using linear mixed-effects models to examine covariate-adjusted associations between the independent variable (nighttime pain severity) and sleep outcomes.

RESULTS

Higher pain severity scores were associated with longer sleep duration but reduced sleep efficiency and less restful sleep. Women reporting nocturnal vasomotor symptoms had more sleep-related movement and sleep fragmentation, had reduced sleep efficiency, and were less likely to feel rested after wakening whether or not they reported pain.

CONCLUSIONS

Midlife women who report higher nighttime pain levels have more objective evidence for less efficient sleep, consistent with self-reported less restful sleep. Nocturnal vasomotor symptoms also can contribute to restlessness and wakefulness in midlife women.

TAILOR THE LONGITUDINAL ANAYSIS FOR NIH LONGITUDINAL NORMAL BRAIN DEVELOPMENTAL STUDY

There are imminent needs for longitudinal analysis to make physiological inferences on NIH MRI study of normal brain development. But up to date, two critical aspects for longitudinal analysis, namely the selections of mean and covariance structures have not been addressed by the neuroimaging community. For the mean structure, we employed a linear free-knot B-spline regression in combination with quasi-least square estimating equations to approximate a nonlinear growth trajectory with piecewise linear segments for a friendly physiological interpretation. For covariance structure selection, we have proposed a novel time varying correlation structure considering not only the time separation between the repeated measures but also when these acquisitions occurred. We have demonstrated that the proposed covariance structure has a lower Akaike information criterion value than the commonly used Markov correlation structure.

Models for the analysis of repeated continuous outcome measures in clinical trials

Repeated continuous outcome measures are common in clinical trials. In this tutorial style paper, using data collected from a trial evaluating an intervention for managing asthma and chronic obstructive pulmonary disease, we demonstrate ways of statistically analysing such data to answer frequently encountered clinical research questions. We illustrate the use of linear mixed effects modelling in doing so and discuss its advantages over several other commonly used approaches. The methods described in this paper can easily be carried out using standard statistical software.

Identifying multiple change points in a linear mixed effects model

Although change-point analysis methods for longitudinal data have been developed, it is often of interest to detect multiple change points in longitudinal data. In this paper, we propose a linear mixed effects modeling framework for identifying multiple change points in longitudinal Gaussian data. Specifically, we develop a novel statistical and computational framework that integrates the expectation-maximization and the dynamic programming algorithms. We conduct a comprehensive simulation study to demonstrate the performance of our method. We illustrate our method with an analysis of data from a trial evaluating a behavioral intervention for the control of type I diabetes in adolescents with HbA1c as the longitudinal response variable.

Profiling post-centrifugation delay of serum and plasma with antibody bead arrays

Several biobanking initiatives have emerged to create extensive collections of specimen for biomedical studies and various analytical platforms. An affinity proteomic analysis with antibody suspension bead arrays was conducted to investigate the influence of the pre-analytical time and temperature conditions on blood derived samples. Serum and EDTA plasma prepared from 16 individuals was centrifuged and aliquots were kept either at 4°C or in ambient temperature for 1h and up to 36h prior to first storage. Multiplexed protein profiles of post-centrifugation delay were generated in 384 biotinylated samples using 373 antibodies that targeted 343 unique proteins. Very few profiles were observed as significantly altered by the studied temperature and time intervals. Single binder and sandwich assays revealed decreasing levels of caldesmon 1 (CALD1) related to EDTA standard tubes and prolonged post-centrifugation delay of 36h. Indications from changes in CALD1 levels require further confirmation in independent material, but the current data suggests that samples should preferentially be frozen during the day of collection when to be profiled with antibody arrays selected for this study.

BIOLOGICAL SIGNIFICANCE

Affinity-based profiling of serum and plasma by microarray assays can provide unique opportunities for the discovery of biomarkers. It is though often not known how differences in sample handling after collection influence the downstream analysis. By profiling three types of blood preparations for alterations in protein profiles with respect to time and temperature post centrifugation, we addressed an important component in the analysis and of such specimen. We believe that this analysis adds valuable information to be considered when biobanking blood derived samples. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.

Power and sample size calculations for evaluating mediation effects in longitudinal studies

Current methods of power and sample size calculations for the design of longitudinal studies to evaluate mediation effects are mostly based on simulation studies and do not provide closed-form formulae. A further challenge due to the longitudinal study design is the consideration of missing data, which almost always occur in longitudinal studies due to staggered entry or drop out. In this article, we consider the product of coefficients as a measure for the longitudinal mediation effect and evaluate three methods for testing the hypothesis on the longitudinal mediation effect: the joint significant test, the normal approximation and the test of b methods. Formulae for power and sample size calculations are provided under each method while taking into account missing data. Performance of the three methods under limited sample size are examined using simulation studies. An example from the Einstein aging study is provided to illustrate the methods.

A comparison of power analysis methods for evaluating effects of a predictor on slopes in longitudinal designs with missing data

In many longitudinal studies, evaluating the effect of a binary or continuous predictor variable on the rate of change of the outcome, i.e. slope, is often of primary interest. Sample size determination of these studies, however, is complicated by the expectation that missing data will occur due to missed visits, early drop out, and staggered entry. Despite the availability of methods for assessing power in longitudinal studies with missing data, the impact on power of the magnitude and distribution of missing data in the study population remain poorly understood. As a result, simple but erroneous alterations of the sample size formulae for complete/balanced data are commonly applied. These 'naive' approaches include the average sum of squares and average number of subjects methods. The goal of this article is to explore in greater detail the effect of missing data on study power and compare the performance of naive sample size methods to a correct maximum likelihood-based method using both mathematical and simulation-based approaches. Two different longitudinal aging studies are used to illustrate the methods.

Effects of mRNA amplification on gene expression ratios in cDNA experiments estimated by analysis of variance

BACKGROUND

A limiting factor of cDNA microarray technology is the need for a substantial amount of RNA per labeling reaction. Thus, 20-200 micro-grams total RNA or 0.5-2 micro-grams poly (A) RNA is typically required for monitoring gene expression. In addition, gene expression profiles from large, heterogeneous cell populations provide complex patterns from which biological data for the target cells may be difficult to extract. In this study, we chose to investigate a widely used mRNA amplification protocol that allows gene expression studies to be performed on samples with limited starting material. We present a quantitative study of the variation and noise present in our data set obtained from experiments with either amplified or non-amplified material.

RESULTS

Using analysis of variance (ANOVA) and multiple hypothesis testing, we estimated the impact of amplification on the preservation of gene expression ratios. Both methods showed that the gene expression ratios were not completely preserved between amplified and non-amplified material. We also compared the expression ratios between the two cell lines for the amplified material with expression ratios between the two cell lines for the non-amplified material for each gene. With the aid of multiple t-testing with a false discovery rate of 5%, we found that 10% of the genes investigated showed significantly different expression ratios.

CONCLUSION

Although the ratios were not fully preserved, amplification may prove to be extremely useful with respect to characterizing low expressing genes.