Incorporating sources of correlation between outcomes: An introduction to mixed models.

Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample t-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and p-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.

A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases.

BACKGROUND: The natural history of many chronic diseases is characterized by periods of increased disease activity, commonly referred to as flare-ups or exacerbations. Accurate characterization of the burden of these exacerbations is an important research objective. METHODS: The purpose of this work was to develop a statistical framework for nuanced characterization of the three main features of exacerbations: their rate, duration, and severity, with interrelationships among these features being a particular focus. We jointly specified a zero-inflated accelerated failure time regression model for the rate, an accelerated failure time regression model for the duration, and a logistic regression model for the severity of exacerbations. Random effects were incorporated into each component to capture heterogeneity beyond the variability attributable to observed characteristics, and to describe the interrelationships among these components. RESULTS: We used pooled data from two clinical trials in asthma as an exemplary application to illustrate the utility of the joint modeling approach. The model fit clearly indicated the presence of heterogeneity in all three components. A novel finding was that the new therapy reduced not just the rate but also the duration of exacerbations, but did not have a significant impact on their severity. After controlling for covariates, exacerbations among more frequent exacerbators tended to be shorter and less likely to be severe. CONCLUSIONS: We conclude that a joint modeling framework, programmable in available software, can provide novel insights about how the rate, duration, and severity of episodic events interrelate, and enables consistent inference on the effect of treatments on different disease outcomes. Trial registration Ethics approval was obtained from the University of British Columbia Human Ethics Board (H17-00938).

Contextualizing the standard maternal continuum of care in Pakistan: an application of revised recommendation of the World Health Organization.

Objective: This study utilizes recent nationally representative data to contextualize the standard maternal continuum of care (SMCoC) in Pakistan. The revised SMCoC framework encompasses at least eight antenatal care visits, skilled birth attendants during delivery, and postnatal care within 48 h of childbirth. Methods: The study used a sample of 3,887 ever-married women aged 15-49 from the latest Pakistan Demographic and Health Survey (PDHS) conducted in 2017-18. Several statistical methods were employed: descriptive statistics, bivariate, multilevel logistic regression models, and Fairlie decomposition analysis. Results: Only 12% of women had accessed full SMCoC services in Pakistan. Education and the wealth quintile emerged as pivotal factors influencing the utilization of SMCoC. The likelihood of full SMCC utilization was more likely among higher educated women (OR: 3.37; 95% CI: 2.16-5.25) and those belonging to the wealthiest household wealth quintile (OR: 4.95; 95% CI: 2.33-5.51). Media exposure, autonomy, healthcare accessibility, residence, and region were also identified as significant predictors of SMCoC utilization among women. Conclusion: In conclusion, while most women did not utilize full SMCoC services in Pakistan, the pattern is substantially varied by background characteristics. Education, wealth quintile, mass media exposure, and autonomy were significant factors, along with geographical aspects such as healthcare accessibility and region. The study underscores the need for a multifaceted approach to ensure equitable access to full SMCoC services for women in Pakistan, addressing individual, socioeconomic, and geographical factors.

Accurate estimation for extra-Poisson variability assuming random effect models.

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.

Evidence of reduced individual heterogeneity in adult survival of long-lived species.

The canalization hypothesis postulates that the rate at which trait variation generates variation in the average individual fitness in a population determines how buffered traits are against environmental and genetic factors. The ranking of a species on the slow-fast continuum - the covariation among life-history traits describing species-specific life cycles along a gradient going from a long life, slow maturity, and low annual reproductive output, to a short life, fast maturity, and high annual reproductive output - strongly correlates with the relative fitness impact of a given amount of variation in adult survival. Under the canalization hypothesis, long-lived species are thus expected to display less individual heterogeneity in survival at the onset of adulthood, when reproductive values peak, than short-lived species. We tested this life-history prediction by analysing long-term time series of individual-based data in nine species of birds and mammals using capture-recapture models. We found that individual heterogeneity in survival was higher in species with short-generation time (< 3 years) than in species with long generation time (> 4 years). Our findings provide the first piece of empirical evidence for the canalization hypothesis at the individual level from the wild.

Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: Development and validation.

With the emergence of rich information on biomarkers after treatments, new types of prognostic tools are being developed: dynamic prognostic tools that can be updated at each new biomarker measurement. Such predictions are of interest in oncology where after an initial treatment, patients are monitored with repeated biomarker data. However, in such setting, patients may receive second treatments to slow down the progression of the disease. This paper aims to develop and validate dynamic individual predictions that allow the possibility of a new treatment in order to help understand the benefit of initiating new treatments during the monitoring period. The prediction of the event in the next x years is done under two scenarios: (1) the patient initiates immediately a second treatment, (2) the patient does not initiate any treatment in the next x years. Predictions are derived from shared random-effect models. Applied to prostate cancer data, different specifications for the dependence between the prostate-specific antigen repeated measures, the initiation of a second treatment (hormonal therapy), and the risk of clinical recurrence are investigated and compared. The predictive accuracy of the dynamic predictions is evaluated with two measures (Brier score and prognostic cross-entropy) for which approximated cross-validated estimators are proposed.

Is healthcare a 'Necessity' or 'Luxury'? an empirical evidence from public and private sector analyses of South-East Asian countries?

South-East Asian Regional (SEAR) countries range from low- to middle-income countries and have considerable differences in mix of public and private sector expenditure on health. This study intends to estimate the income-elasticities of healthcare expenditure in public and private sectors separately for investigating whether healthcare is a 'necessity' or 'luxury' for citizens of these countries. Panel data from 9 SEAR countries over 16 years (1995-2010) were employed. Fixed- and random-effect models were fitted to estimate income-elasticity of public, private and total healthcare expenditure. Results showed that one percent point increase in GDP per capita increased private expenditure on healthcare by 1.128%, while public expenditure increased by only 0.412%. Inclusion of three-year lagged variables of GDP per capita in the models did not have remarkable influence on the findings. The citizens of SEAR countries consider healthcare as a necessity while provided through public sector and a luxury when delivered by private sector. By increasing the public provisions of healthcare, more redistribution of healthcare resources can be ensured, which can accelerate the journey of SEAR countries towards universal health coverage.

Warped functional analysis of variance.

This article presents an Analysis of Variance model for functional data that explicitly incorporates phase variability through a time-warping component, allowing for a unified approach to estimation and inference in presence of amplitude and time variability. The focus is on single-random-factor models but the approach can be easily generalized to more complex ANOVA models. The behavior of the estimators is studied by simulation, and an application to the analysis of growth curves of flour beetles is presented. Although the model assumes a smooth latent process behind the observed trajectories, smootheness of the observed data is not required; the method can be applied to irregular time grids, which are common in longitudinal studies.

Integrative analysis of gene expression data including an assessment of pathway enrichment for predicting prostate cancer.

BACKGROUND: Microarray technology has been previously used to identify genes that are differentially expressed between tumour and normal samples in a single study, as well as in syntheses involving multiple studies. When integrating results from several Affymetrix microarray datasets, previous studies summarized probeset-level data, which may potentially lead to a loss of information available at the probe-level. In this paper, we present an approach for integrating results across studies while taking probe-level data into account. Additionally, we follow a new direction in the analysis of microarray expression data, namely to focus on the variation of expression phenotypes in predefined gene sets, such as pathways. This targeted approach can be helpful for revealing information that is not easily visible from the changes in the individual genes. RESULTS: We used a recently developed method to integrate Affymetrix expression data across studies. The idea is based on a probe-level based test statistic developed for testing for differentially expressed genes in individual studies. We incorporated this test statistic into a classic random-effects model for integrating data across studies. Subsequently, we used a gene set enrichment test to evaluate the significance of enriched biological pathways in the differentially expressed genes identified from the integrative analysis. We compared statistical and biological significance of the prognostic gene expression signatures and pathways identified in the probe-level model (PLM) with those in the probeset-level model (PSLM). Our integrative analysis of Affymetrix microarray data from 110 prostate cancer samples obtained from three studies reveals thousands of genes significantly correlated with tumour cell differentiation. The bioinformatics analysis, mapping these genes to the publicly available KEGG database, reveals evidence that tumour cell differentiation is significantly associated with many biological pathways. In particular, we observed that by integrating information from the insulin signalling pathway into our prediction model, we achieved better prediction of prostate cancer. CONCLUSIONS: Our data integration methodology provides an efficient way to identify biologically sound and statistically significant pathways from gene expression data. The significant gene expression phenotypes identified in our study have the potential to characterize complex genetic alterations in prostate cancer.