Aynslie M Hinds1, Tolulope T Sajobi2, Véronique Sebille3, Richard Sawatzky4,5, Lisa M Lix6. 1. Department of Community Health Sciences, University of Manitoba, S113-750 Bannatyne Ave, Winnipeg, MB, R3E 0W3, Canada. 2. Department of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, 3D19 Teaching Research and Wellness Building, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada. 3. Institut de Recherche en Santé, Université de Nantes, Université de Tours, INSERM, SPHERE U1246, 22 Boulevard Bénoni Goullin, 44000, Nantes, France. 4. School of Nursing, Trinity Western University, 7th Floor, 828 West 10th Avenue, Research Pavilion, Vancouver, BC V5Z 1M9, Canada. 5. Centre for Health Evaluation and Outcome Sciences, Providence Health Care, 588-1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada. 6. Department of Community Health Sciences, University of Manitoba, S113-750 Bannatyne Ave, Winnipeg, MB, R3E 0W3, Canada. lisa.lix@umanitoba.ca.
Abstract
PURPOSE: This study describes the characteristics and quality of reporting for published computer simulation studies about statistical methods to analyze complex longitudinal (i.e., repeated measures) patient-reported outcomes (PROs); we included methods for longitudinal latent variable measurement and growth models and response shift. METHODS: Scopus, PsycINFO, PubMed, EMBASE, and Social Science Citation Index were searched for English-language studies published between 1999 and 2016 using selected keywords. Extracted information included characteristics of the study purpose/objectives, simulation design, software, execution, performance, and results. The quality of reporting was evaluated using published best-practice guidelines. SYNTHESIS: A total of 1470 articles were reviewed and 42 articles met the inclusion criteria. The majority of the included studies (73.8%) investigated an existing statistical method, primarily a latent variable model (95.2%). Most studies specified the population model, including variable distributions, mean parameters, and correlation/covariances. The number of time points and sample size(s) were reported by all studies, but justification for the selected values was rarely provided. The majority of the studies (52.4%) did not report on model non-convergence. Bias, accuracy, and model fit were commonly reported performance metrics. All studies reported results descriptively, and 26.2% also used an inferential method. CONCLUSIONS: While methodological research on statistical analyses of complex longitudinal PRO data is informed by computer simulation studies, current reporting practices of these studies have not been consistent with best-practice guidelines. Comprehensive reporting of simulation methods and results ensures that the strengths and limitations of the investigated statistical methods are thoroughly explored.
PURPOSE: This study describes the characteristics and quality of reporting for published computer simulation studies about statistical methods to analyze complex longitudinal (i.e., repeated measures) patient-reported outcomes (PROs); we included methods for longitudinal latent variable measurement and growth models and response shift. METHODS: Scopus, PsycINFO, PubMed, EMBASE, and Social Science Citation Index were searched for English-language studies published between 1999 and 2016 using selected keywords. Extracted information included characteristics of the study purpose/objectives, simulation design, software, execution, performance, and results. The quality of reporting was evaluated using published best-practice guidelines. SYNTHESIS: A total of 1470 articles were reviewed and 42 articles met the inclusion criteria. The majority of the included studies (73.8%) investigated an existing statistical method, primarily a latent variable model (95.2%). Most studies specified the population model, including variable distributions, mean parameters, and correlation/covariances. The number of time points and sample size(s) were reported by all studies, but justification for the selected values was rarely provided. The majority of the studies (52.4%) did not report on model non-convergence. Bias, accuracy, and model fit were commonly reported performance metrics. All studies reported results descriptively, and 26.2% also used an inferential method. CONCLUSIONS: While methodological research on statistical analyses of complex longitudinal PRO data is informed by computer simulation studies, current reporting practices of these studies have not been consistent with best-practice guidelines. Comprehensive reporting of simulation methods and results ensures that the strengths and limitations of the investigated statistical methods are thoroughly explored.
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