Dataset Information

Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD.

ABSTRACT: Repeated measures of various biomarkers provide opportunities for us to enhance understanding of many important clinical aspects of CKD, including patterns of disease progression, rates of kidney function decline under different risk factors, and the degree of heterogeneity in disease manifestations across patients. However, because of unique features, such as correlations across visits and time dependency, these data must be appropriately handled using longitudinal data analysis methods. We provide a general overview of the characteristics of data collected in cohort studies and compare appropriate statistical methods for the analysis of longitudinal exposures and outcomes. We use examples from the Chronic Renal Insufficiency Cohort Study to illustrate these methods. More specifically, we model longitudinal kidney outcomes over annual clinical visits and assess the association with both baseline and longitudinal risk factors.

SUBMITTER: Shou H

PROVIDER: S-EPMC5544518 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD.

Shou Haochang H Hsu Jesse Y JY Xie Dawei D Yang Wei W Roy Jason J Anderson Amanda H AH Landis J Richard JR Feldman Harold I HI Parsa Afshin A Jepson Christopher C

Clinical journal of the American Society of Nephrology : CJASN 20170727 8

Repeated measures of various biomarkers provide opportunities for us to enhance understanding of many important clinical aspects of CKD, including patterns of disease progression, rates of kidney function decline under different risk factors, and the degree of heterogeneity in disease manifestations across patients. However, because of unique features, such as correlations across visits and time dependency, these data must be appropriately handled using longitudinal data analysis methods. We pro ...[more]

PMID: 28751576

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Project description:BackgroundRetaining participants in cohort studies with multiple follow-up waves is difficult. Commonly, researchers are faced with the problem of missing data, which may introduce biased results as well as a loss of statistical power and precision. The STROBE guidelines von Elm et al. (Lancet, 370:1453-1457, 2007); Vandenbroucke et al. (PLoS Med, 4:e297, 2007) and the guidelines proposed by Sterne et al. (BMJ, 338:b2393, 2009) recommend that cohort studies report on the amount of missing data, the reasons for non-participation and non-response, and the method used to handle missing data in the analyses. We have conducted a review of publications from cohort studies in order to document the reporting of missing data for exposure measures and to describe the statistical methods used to account for the missing data.MethodsA systematic search of English language papers published from January 2000 to December 2009 was carried out in PubMed. Prospective cohort studies with a sample size greater than 1,000 that analysed data using repeated measures of exposure were included.ResultsAmong the 82 papers meeting the inclusion criteria, only 35 (43%) reported the amount of missing data according to the suggested guidelines. Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n=54, 66%). Other papers used more sophisticated methods including multiple imputation (n=5) or fully Bayesian modeling (n=1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n=7), the missing indicator method (n=1), and mean value substitution (n=3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated.ConclusionsThis review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies.

Project description:Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes. Author summary Clinical trials are increasingly generating large amounts of complex biological data. Examples can include measuring metabolism or gene expression in tissue or blood sampled repeatedly over the course of a treatment. In such cases, one might wish to compare changes in not one, but hundreds, or thousands of variables simultaneously. In order to effectively analyze such data, both the study design and the multivariate nature of the data should be considered during data analysis. ANOVA simultaneous component analysis+ (ASCA+) is a statistical method which combines general linear models with principal component analysis, and provides a way to separate and visualize the effects of different factors on complex biological data. In this work, we describe how repeated measures linear mixed models, a class of models commonly used when analyzing changes over time and treatment effects in longitudinal studies, can be used together with ASCA+ for analyzing clinical trials in a novel method called repeated measures-ASCA+ (RM-ASCA+).

Project description:Rationale & objectiveSingle measurements of urinary biomarkers reflecting kidney tubule health are associated with chronic kidney disease (CKD) risk in HIV infection, but the prognostic value of repeat measurements over time is unknown.Study designCohort study.Setting & participants647 women living with HIV infection enrolled in the Women's Interagency Health Study.Exposures14 urinary biomarkers of kidney tubule health measured at 2 visits over a 3-year period.OutcomeIncident CKD, defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 at two 6-month visits and an average eGFR decline ≥ 3% per year.Analytical approachWe used multivariable generalized estimating equations adjusting for CKD risk factors to evaluate baseline, time-updated, and change-over-time biomarker associations with incident CKD. We compared CKD discrimination between models with and without a parsimoniously selected set of biomarkers.ResultsDuring a median 7 years of follow-up, 9.7% (63/647) developed CKD. In multivariable-adjusted analyses, 3 of 14 baseline biomarkers associated with incident CKD. In contrast, 10 of 14 time-updated biomarkers and 9 of 14 biomarkers modeled as change over time associated with incident CKD. Urinary epidermal growth factor (EGF), α1-microglobulin (A1M), and albumin were selected using penalized regression methods. In the time-updated model, lower urinary EGF (risk ratio [RR] per 2-fold higher time-updated biomarker levels, 0.69; 95% CI, 0.58-0.81), higher urinary A1M (RR, 1.47; 95% CI, 1.25-1.73), and higher urinary albumin excretion (RR, 1.21; 95% CI, 1.03-1.42) were jointly associated with increased risk for CKD. Compared with a base model (C statistic, 0.75), CKD discrimination improved after adding urinary EGF, A1M, and albumin values across baseline (C = 0.81), time-updated (C = 0.83), and change-over-time (C = 0.83) models (P < 0.01 for all).LimitationsObservational design, incident CKD definition limited to eGFR.ConclusionsRepeat urinary biomarker measurements for kidney tubule health have stronger associations with incident CKD compared with baseline measurements and moderately improve CKD discrimination in women living with HIV infection.

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Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD.

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Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD.

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