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Harrall KK, Sauder KA, Glueck DH, Shenkman EA, Muller KE. Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024; 25:433-445. [PMID: 38767783 PMCID: PMC11239604 DOI: 10.1007/s11121-024-01673-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2024] [Indexed: 05/22/2024]
Abstract
We give examples of three features in the design of randomized controlled clinical trials which can increase power and thus decrease sample size and costs. We consider an example multilevel trial with several levels of clustering. For a fixed number of independent sampling units, we show that power can vary widely with the choice of the level of randomization. We demonstrate that power and interpretability can improve by testing a multivariate outcome rather than an unweighted composite outcome. Finally, we show that using a pooled analytic approach, which analyzes data for all subgroups in a single model, improves power for testing the intervention effect compared to a stratified analysis, which analyzes data for each subgroup in a separate model. The power results are computed for a proposed prevention research study. The trial plans to randomize adults to either telehealth (intervention) or in-person treatment (control) to reduce cardiovascular risk factors. The trial outcomes will be measures of the Essential Eight, a set of scores for cardiovascular health developed by the American Heart Association which can be combined into a single composite score. The proposed trial is a multilevel study, with outcomes measured on participants, participants treated by the same provider, providers nested within clinics, and clinics nested within hospitals. Investigators suspect that the intervention effect will be greater in rural participants, who live farther from clinics than urban participants. The results use published, exact analytic methods for power calculations with continuous outcomes. We provide example code for power analyses using validated software.
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Affiliation(s)
- Kylie K Harrall
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Gainesville, 32606, FL, USA.
| | - Katherine A Sauder
- Department of Implementation Science, Wake Forest University School of Medicine, 475 Vine Street, Winston-Salem, 27101, NC, USA
| | - Deborah H Glueck
- Department of Pediatrics, University of Colorado School of Medicine, 13123 E. 16th Ave., Aurora, 80045, CO, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Gainesville, 32606, FL, USA
| | - Keith E Muller
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Gainesville, 32606, FL, USA
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Liu S, Lai X, Xie Q, Wang Z, Pan Y, Wang Q, Zhang Z. Holo-omics analysis reveals the influence of gut microbiota on obesity indicators in Jinhua pigs. BMC Microbiol 2023; 23:322. [PMID: 37923989 PMCID: PMC10623862 DOI: 10.1186/s12866-023-03011-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 09/11/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND The mechanisms behind obesity are complex and multi-faceted, involving the interplay of both host genomics and gut microbiome. In recent years, research has largely focused on these factors separately, but rarely from the viewpoint of holo-omics, which considers the host and microbiome as an integrated entity. To address this gap in knowledge, the present study aimed to investigate the holo-omics basis of obesity in Jinhua pigs, a Chinese indigenous breed known for its high degree of fat deposition and superior meat quality. METHODS Six pigs with extreme obesity phenotype were selected from a larger cohort of eighteen Jinhua pigs, and the contents of the jejunum, cecum, and colon regions were collected after slaughter at 240 days of age. The data obtained was processed, denoised, and annotated using QIIME2, with expression differences being analyzed using edgeR software. RESULTS The results showed significant differences in jejunal microbial diversity and composition between the two groups, with gut transcriptomics also indicating that differentially expressed genes in the jejunum were enriched in lipid metabolism pathways. These findings provide further evidence of the influence of the gut microbiome and host gene expression on fat deposition in Jinhua pigs. CONCLUSIONS This study provides valuable insights into the mechanisms of fat deposition in Jinhua pigs from the viewpoint of holo-omics. The integration of host transcriptomics and microbiome data helps shed light on the complex interactions between the host and gut microbiome, and highlights the importance of considering both factors in our understanding of obesity.
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Affiliation(s)
- Shuang Liu
- College of Animal Sciences, Zhejiang University, Hangzhou, 310030, China
| | - Xueshuang Lai
- School of Agriculture and Biology, Department of Animal Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qinqin Xie
- College of Animal Sciences, Zhejiang University, Hangzhou, 310030, China
| | - Zhen Wang
- College of Animal Sciences, Zhejiang University, Hangzhou, 310030, China
| | - Yuchun Pan
- College of Animal Sciences, Zhejiang University, Hangzhou, 310030, China
| | - Qishan Wang
- College of Animal Sciences, Zhejiang University, Hangzhou, 310030, China.
| | - Zhe Zhang
- College of Animal Sciences, Zhejiang University, Hangzhou, 310030, China.
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Harrall KK, Muller KE, Starling AP, Dabelea D, Barton KE, Adgate JL, Glueck DH. Power and sample size analysis for longitudinal mixed models of health in populations exposed to environmental contaminants: a tutorial. BMC Med Res Methodol 2023; 23:12. [PMID: 36635621 PMCID: PMC9835314 DOI: 10.1186/s12874-022-01819-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/13/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND When evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different trajectories of the environmental exposure over time. Power analyses for longitudinal mixed models require multiple inputs, including clinically significant differences, standard deviations, and correlations of measurements. Further, methods for power analyses of longitudinal mixed models are complex and often challenging for the non-statistician. We discuss methods for extracting clinically relevant inputs from literature, and explain how to conduct a power analysis that appropriately accounts for longitudinal repeated measures. Finally, we provide careful recommendations for describing complex power analyses in a concise and clear manner. METHODS For longitudinal studies of health outcomes from environmental exposures, we show how to [1] conduct a power analysis that aligns with the planned mixed model data analysis, [2] gather the inputs required for the power analysis, and [3] conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists. RESULTS As an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis. CONCLUSIONS This paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications.
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Affiliation(s)
- Kylie K Harrall
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado - Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA.
| | - Keith E Muller
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Anne P Starling
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado - Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado - Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kelsey E Barton
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - John L Adgate
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Deborah H Glueck
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado - Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
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Josey KP, Ringham BM, Barón AE, Schenkman M, Sauder KA, Muller KE, Dabelea D, Glueck DH. Power for balanced linear mixed models with complex missing data processes. COMMUN STAT-THEOR M 2021; 52:46-64. [PMID: 36743328 PMCID: PMC9897326 DOI: 10.1080/03610926.2021.1909732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 03/21/2021] [Indexed: 02/07/2023]
Abstract
When designing repeated measures studies, both the amount and the pattern of missing outcome data can affect power. The chance that an observation is missing may vary across measurements, and missingness may be correlated across measurements. For example, in a physiotherapy study of patients with Parkinson's disease, increasing intermittent dropout over time yielded missing measurements of physical function. In this example, we assume data are missing completely at random, since the chance that a data point was missing appears to be unrelated to either outcomes or covariates. For data missing completely at random, we propose noncentral F power approximations for the Wald test for balanced linear mixed models with Gaussian responses. The power approximations are based on moments of missing data summary statistics. The moments were derived assuming a conditional linear missingness process. The approach provides approximate power for both complete-case analyses, which include independent sampling units where all measurements are present, and observed-case analyses, which include all independent sampling units with at least one measurement. Monte Carlo simulations demonstrate the accuracy of the method in small samples. We illustrate the utility of the method by computing power for proposed replications of the Parkinson's study.
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Affiliation(s)
- Kevin P. Josey
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA
| | - Brandy M. Ringham
- Lifecourse Epidemiology of Adiposity and Disease (LEAD) Center, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA
| | - Anna E. Barón
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA
| | - Margaret Schenkman
- Physical Therapy Program, School of Medicine, University of Colorado Denver, Denver, Colorado, USA
| | - Katherine A. Sauder
- Department of Pediatrics, School of Medicine, University of Colorado Denver, Denver, Colorado, USA
| | - Keith E. Muller
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Dana Dabelea
- Department of Epidemiology and the Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA
| | - Deborah H. Glueck
- Department of Pediatrics, School of Medicine, University of Colorado Denver, Denver, Colorado, USA
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Cardel MI, Pavela G, Janicke D, Huo T, Miller D, Lee AM, Gurka MJ, Dhurandhar E, Peters JC, Caldwell AE, Krause E, Fernandez A, Allison DB. Experimentally Manipulated Low Social Status and Food Insecurity Alter Eating Behavior Among Adolescents: A Randomized Controlled Trial. Obesity (Silver Spring) 2020; 28:2010-2019. [PMID: 33150744 PMCID: PMC7653825 DOI: 10.1002/oby.23002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 05/05/2020] [Accepted: 05/27/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE This randomized trial experimentally manipulated social status to assess effects on acute eating behavior and 24-hour energy balance. METHODS Participants (n = 133 Hispanics; age 15-21 years; 60.2% females) were randomized to low social status ("LOW") or high social status ("HIGH") conditions in a rigged game of Monopoly (Hasbro, Inc.). Acute energy intake in a lunchtime meal was measured by food scales. Twenty-four-hour energy balance was assessed via summation of resting metabolic rate (metabolic cart), physical activity energy expenditure (accelerometer), thermic effect of food, and subtraction of twenty-four-hour energy intake (food diary). RESULTS In the total sample, no significant differences were observed by study condition at lunchtime. LOW females consumed a greater percent of lunchtime daily energy needs (37.5%) relative to HIGH females (34.3%); however, this difference was not statistically significant (P = 0.291). In males, however, LOW consumed significantly less (36.5%) of their daily energy needs relative to HIGH males (45.8%; P = 0.001). For 24-hour energy balance, sex differences were nearly significant (P = 0.057; LOW females: surplus +200 kcal; HIGH males: surplus +445 kcal). Food-insecure individuals consumed a nearly significant greater lunchtime percent daily energy than those with food security (40.7% vs. 36.3%; P = 0.0797). CONCLUSIONS The data demonstrate differential acute and 24-hour eating behavior responses between Hispanic male and female adolescents in experimentally manipulated conditions of low social status.
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Affiliation(s)
- Michelle I. Cardel
- Department of Health Outcomes and Biomedical Informatics, University of Florida, 2004 Mowry Road, PO Box 100177, Gainesville, FL, 32610
| | - Greg Pavela
- Department of Health Behavior, University of Alabama at Birmingham, 227K Ryals Public Health Building, 1665 University Boulevard, Birmingham, AL, 35294
| | - David Janicke
- Department of Clinical and Health Psychology, University of Florida, PO Box 100165, Gainesville, FL, 32610
| | - Tianyao Huo
- Department of Health Outcomes and Biomedical Informatics, University of Florida, 2004 Mowry Road, PO Box 100177, Gainesville, FL, 32610
| | - Darci Miller
- Department of Health Outcomes and Biomedical Informatics, University of Florida, 2004 Mowry Road, PO Box 100177, Gainesville, FL, 32610
| | - Alexandra M. Lee
- Department of Health Outcomes and Biomedical Informatics, University of Florida, 2004 Mowry Road, PO Box 100177, Gainesville, FL, 32610
| | - Matthew J. Gurka
- Department of Health Outcomes and Biomedical Informatics, University of Florida, 2004 Mowry Road, PO Box 100177, Gainesville, FL, 32610
| | - Emily Dhurandhar
- Department of Kinesiology and Sport Management, Texas Tech University, Box 43011, 2500 Broadway, Lubbock, TX, 79409
| | - John C. Peters
- Division of Endocrinology, Metabolism, and Diabetes, University of Colorado Denver, Anschutz Health and Wellness Center, PO Box 6511-MS 8106, 12801 East 17 Ave, RC1 South RM 7103, Aurora, CO, 80045
| | - Ann E. Caldwell
- Division of Endocrinology, Metabolism, and Diabetes, University of Colorado Denver, Anschutz Health and Wellness Center, PO Box 6511-MS 8106, 12801 East 17 Ave, RC1 South RM 7103, Aurora, CO, 80045
| | - Eric Krause
- Department of Pharmacodynamics, University of Florida, PO Box 100487, Gainesville, FL, 32610
| | - Alicia Fernandez
- Department of Medicine, University of California San Francisco, 1001 Potrero Ave, Rm. 1307, UCSF Box 1364, San Francisco, CA, 94110
| | - David B. Allison
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University-Bloomington, 1025 East 7 Street, Suite 111, Bloomington, IN, 47405
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Xu Z, Xie C, Xia L, Yuan Y, Zhu H, Huang X, Li C, Tao Y, Qu X, Zhang F, Zhang Z. Targeted exome sequencing identifies five novel loci at genome-wide significance for modulating antidepressant response in patients with major depressive disorder. Transl Psychiatry 2020; 10:30. [PMID: 32066657 PMCID: PMC7026085 DOI: 10.1038/s41398-020-0689-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 10/23/2019] [Accepted: 11/06/2019] [Indexed: 02/08/2023] Open
Abstract
In order to determine the role of single nucleotide variants (SNVs) in modulating antidepressant response, we conducted a study, consisting of 929 major depressive disorder (MDD) patients, who were treated with antidepressant drugs (drug-only) or in combination with a repetitive transcranial magnetic stimulation (plus-rTMS), followed by targeted exome sequencing analysis. We found that the "plus-rTMS" patients presented a more effective response to the treatment when compared to the 'drug-only' group. Our data firstly demonstrated that the SNV burden had a significant impact on the antidepressant response presented in the "drug-only" group, but was limited in the "plus-rTMS" group. Further, after controlling for overall SNV burden, seven single nucleotide polymorphisms (SNPs) at five loci, IL1A, GNA15, PPP2CB, PLA2G4C, and GBA, were identified as affecting the antidepressant response at genome-wide significance (P < 5 × 10-08). Additional multiple variants achieved a level of correction for multiple testing, including GNA11, also shown as a strong signal for MDD risk. Our study showed some promising evidence on genetic variants that could be used as individualized therapeutic guides for MDD patients.
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Affiliation(s)
- Zhi Xu
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Chunming Xie
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Lu Xia
- Global Clinical and Translational Research Institute, Bethesda, MD 20814 USA
| | - Yonggui Yuan
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Hong Zhu
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Xiaofa Huang
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Caihua Li
- Center for Genetics and Genomics Analysis, Genesky Biotechnologies, Inc, 201203 Shanghai, China
| | - Yu Tao
- Center for Genetics and Genomics Analysis, Genesky Biotechnologies, Inc, 201203 Shanghai, China
| | - Xiaoxiao Qu
- Genesky Diagnostics, Inc., BioBay, SIP, 215123 Jiangsu, China
| | - Fengyu Zhang
- Global Clinical and Translational Research Institute, Bethesda, MD, 20814, USA.
| | - Zhijun Zhang
- The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009, Nanjing, Jiangsu, China. .,Global Clinical and Translational Research Institute, Bethesda, MD, 20814, USA. .,The Institute of Neuropsychiatry, the Key Laboratory of Development Genes and Human Diseases, the Ministry of Education and Institute of Life Sciences of Southeast University, 210096, Nanjing, Jiangsu, China.
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Baghfalaki T. Bayesian sample size determination for longitudinal studies with continuous response based on different scientific questions of interest. J Biopharm Stat 2018; 29:244-270. [PMID: 30359549 DOI: 10.1080/10543406.2018.1535501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Longitudinal study designs are commonly applied in much scientific research, especially in the medical, social, and economic sciences. Longitudinal studies allow researchers to measure changes in each individual's responses over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. In longitudinal studies, because of the complexity of their design, including the selection of the number of individuals and the number of repeated measurements, sample size determination is less studied. This paper uses a simulation-based method to determine the sample size from a Bayesian perspective. For this purpose, several Bayesian criteria for sample size determination are used, of which the most important one is the Bayesian power criterion. We determine the sample size of a longitudinal study based on the scientific question of interest, by the choice of an appropriate model. Most of the methods of determining sample size are based on the definition of a single hypothesis. In this paper, in addition to using this method, we determine the sample size using multiple hypotheses. Using several examples, the proposed Bayesian methods are illustrated and discussed.
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Affiliation(s)
- Taban Baghfalaki
- a Department of Statistics, Faculty of Mathematical Sciences , Tarbiat Modares University , Tehran , Iran
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Chi YY, Glueck DH, Muller KE. Power and Sample Size for Fixed-Effects Inference in Reversible Linear Mixed Models. AM STAT 2018; 73:350-359. [PMID: 32042203 DOI: 10.1080/00031305.2017.1415972] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Despite the popularity of the general linear mixed model for data analysis, power and sample size methods and software are not generally available for commonly used test statistics and reference distributions. Statisticians resort to simulations with homegrown and uncertified programs or rough approximations which are misaligned with the data analysis. For a wide range of designs with longitudinal and clustering features, we provide accurate power and sample size approximations for inference about fixed effects in linear models we call reversible. We show that under widely applicable conditions, the general linear mixed-model Wald test has non-central distributions equivalent to well-studied multivariate tests. In turn, exact and approximate power and sample size results for the multivariate Hotelling-Lawley test provide exact and approximate power and sample size results for the mixed-model Wald test. The calculations are easily computed with a free, open-source product that requires only a web browser to use. Commercial software can be used for a smaller range of reversible models. Simple approximations allow accounting for modest amounts of missing data. A real-world example illustrates the methods. Sample size results are presented for a multicenter study on pregnancy. The proposed study, an extension of a funded project, has clustering within clinic. Exchangeability among participants allows averaging across them to remove the clustering structure. The resulting simplified design is a single level longitudinal study. Multivariate methods for power provide an approximate sample size. All proofs and inputs for the example are in the Supplementary Materials (available online).
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Affiliation(s)
- Yueh-Yun Chi
- Department of Biostatistics, University of Florida
| | - Deborah H Glueck
- Department of Biostatistics and Informatics, University of Colorado Denver
| | - Keith E Muller
- Department of Health Outcomes and Policy, University of Florida
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Huo T, Guo Y, Shenkman E, Muller K. Assessing the reliability of the short form 12 (SF-12) health survey in adults with mental health conditions: a report from the wellness incentive and navigation (WIN) study. Health Qual Life Outcomes 2018; 16:34. [PMID: 29439718 PMCID: PMC5811954 DOI: 10.1186/s12955-018-0858-2] [Citation(s) in RCA: 125] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 02/05/2018] [Indexed: 12/11/2022] Open
Abstract
Background Although Short Form (SF)-12 × 2® has been extensively studied and used as a valid measure of health-related quality of life in a variety of population groups, no systematic studies have described the reliability of the measure in patients with behavioral conditions or serious mental illness (SMI). Methods and results We assessed the internal consistency, split-half reliability and annual test-retest correlations in a sample of 1587 participants with either a combination of physical and behavioral conditions or SMI. The Mosier’s alpha was 0.70 for the Physical Composite Scale (PCS) and 0.69 for the Mental Health Composite Scale (MCS), indicating good internal consistency. We observed strong correlations between physical functioning, physical role and body pain scales (r = 0.55–0.56), and between social functioning, emotional role, and mental health (r = 0.53–0.58). We calculated split-half reliabilities to be 0.74 for physical functioning, 0.75 for physical role, 0.73 for emotional role and 0.65 for mental health respectively. We assessed the annual test-retest correlation using intraclass correlation (ICC) and found an ICC of 0.61 for PCS and 0.57 for MCS composite scores, adjusting for age, sex, race/ethnicity, and CRG. We found no decline in the correlations between baseline and the following study years until year 3. Conclusions Our results encourage using SF-12v2® to assess health-related quality of life in the Medicaid population with combined physical and behavioral conditions or similar cohorts. Trial registration The WIN study was registered with clinicaltrials.gov on April 22, 2015. Trial registration number: NCT02440906. Retrospectively registered.
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Affiliation(s)
- Tianyao Huo
- Department of Health Outcomes and Biomedical Informatics, University of Florida, P.O. Box 100177, Gainesville, FL, 32610, USA.
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, University of Florida, P.O. Box 100177, Gainesville, FL, 32610, USA
| | - Elizabeth Shenkman
- Department of Health Outcomes and Biomedical Informatics, University of Florida, P.O. Box 100177, Gainesville, FL, 32610, USA
| | - Keith Muller
- Department of Health Outcomes and Biomedical Informatics, University of Florida, P.O. Box 100177, Gainesville, FL, 32610, USA
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Shenkman E, Muller K, Vogel B, Nixon SJ, Wagenaar AC, Case K, Guo Y, Wegman M, Aric J, Stoner D. The wellness incentives and navigation project: design and methods. BMC Health Serv Res 2015; 15:579. [PMID: 26714845 PMCID: PMC4696169 DOI: 10.1186/s12913-015-1245-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 12/19/2015] [Indexed: 12/22/2022] Open
Abstract
Background About 35 % of non-elderly U.S. adult Medicaid enrollees have a behavioral health condition, such as anxiety, mood disorders, substance use disorders, and/or serious mental illness. Individuals with serious mental illness, in particular, have mortality rates that are 2 to 3 times higher as the general population, which are due to multiple factors including inactivity, poor nutrition, and tobacco use. 61 % of Medicaid beneficiaries with behavioral health conditions also have multiple other co-occurring chronic physical health conditions, which further contributes to morbidity and mortality. The Wellness Incentives and Navigation (WIN) project is one of 10 projects under the Centers for Medicare and Medicaid Services “Medicaid Incentives for the Prevention of Chronic Diseases” Initiative, to “test the effectiveness of providing incentives directly to Medicaid beneficiaries of all ages who participate in prevention programs, and change their health risks and outcomes by adopting healthy behaviors.” Methods/Design WIN is a three-year randomized pragmatic clinical trial designed to examine the comparative effectiveness of the combined use of personal navigators, motivational interviewing, and a flexible wellness account on cardiovascular risk reduction among individuals in Medicaid with co-occurring physical and mental health conditions or serious mental illness alone relative to the usual care provided within Medicaid Managed Care. 1250 individuals, identified through Medicaid claims data, were recruited and randomly assigned to an intervention group or control group with outcomes tracked annually. A comparison group was also recruited to help assess the study’s internal validity. Discussion The primary outcomes are physical and mental health related quality-of-life as measured by the SF-12, and BMI, blood pressure, LDL-C, and Hba1c results for those who are diabetic measured clinically. The purpose of this paper is to present the unique design of the WIN trial prior to results becoming available in hopes of assisting other researchers in conducting community-based randomized pragmatic trials. Outcomes will be assessed through the linkage of patient reported outcomes, health care claims, and electronic health record data. Trial Registration NCT02440906
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Affiliation(s)
- Elizabeth Shenkman
- Department of Health Outcomes and Policy, College of Medicine, the Clinical and Translational Science Institute, and the Institute for Child Health Policy, University of Florida, Gainesville, USA.
| | - Keith Muller
- Department of Health Outcomes and Policy, College of Medicine, the Clinical and Translational Science Institute, and the Institute for Child Health Policy, University of Florida, Gainesville, USA.
| | - Bruce Vogel
- Department of Health Outcomes and Policy, College of Medicine, the Clinical and Translational Science Institute, and the Institute for Child Health Policy, University of Florida, Gainesville, USA.
| | - Sara Jo Nixon
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, USA.
| | - Alexander C Wagenaar
- Department of Health Outcomes and Policy, College of Medicine and the Institute for Child Health Policy, University of Florida, Gainesville, USA.
| | - Kimberly Case
- Department of Health Outcomes and Policy, College of Medicine, University of Florida, University of Florida, Gainesville, USA.
| | - Yi Guo
- Department of Health Outcomes and Policy, College of Medicine, University of Florida, University of Florida, Gainesville, USA.
| | - Martin Wegman
- Department of Health Outcomes and Policy, College of Medicine, Department of Epidemiology, College of Public Health and Health Professions, and the Clinical and Translational Science Institute, University of Florida,, Gainesville, USA.
| | - Jessie Aric
- Texas Department of State Health Services, Austin, USA.
| | - Dena Stoner
- Texas Department of State Health Services, Austin, USA.
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Ringham BM, Kreidler SM, Muller KE, Glueck DH. Multivariate test power approximations for balanced linear mixed models in studies with missing data. Stat Med 2015; 35:2921-37. [PMID: 26603500 DOI: 10.1002/sim.6811] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Revised: 09/25/2015] [Accepted: 10/01/2015] [Indexed: 11/10/2022]
Abstract
Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Brandy M Ringham
- Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, CO, U.S.A
| | | | - Keith E Muller
- Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, U.S.A
| | - Deborah H Glueck
- Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, CO, U.S.A
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Guo Y, Pandis N. Sample-size calculation for repeated-measures and longitudinal studies. Am J Orthod Dentofacial Orthop 2015; 147:146-9. [DOI: 10.1016/j.ajodo.2014.10.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Revised: 10/09/2014] [Accepted: 10/09/2014] [Indexed: 11/30/2022]
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13
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Munjal A, Sakhadeo UR, Muller KE, Glueck DH, Kreidler SM. GLIMMPSE Lite: calculating power and sample size on smartphone devices. PLoS One 2014; 9:e102082. [PMID: 25541688 PMCID: PMC4277287 DOI: 10.1371/journal.pone.0102082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2013] [Accepted: 06/14/2014] [Indexed: 11/19/2022] Open
Abstract
Researchers seeking to develop complex statistical applications for mobile devices face a common set of difficult implementation issues. In this work, we discuss general solutions to the design challenges. We demonstrate the utility of the solutions for a free mobile application designed to provide power and sample size calculations for univariate, one-way analysis of variance (ANOVA), GLIMMPSE Lite. Our design decisions provide a guide for other scientists seeking to produce statistical software for mobile platforms.
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Affiliation(s)
- Aarti Munjal
- Dept. of Biostatistics and Informatics, University of Colorado Denver, Aurora, Colorado, United States of America
- * E-mail:
| | - Uttara R. Sakhadeo
- Dept. of Biostatistics and Informatics, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Keith E. Muller
- Health Outcomes and Policy, University of Florida, Gainesville, Florida, United States of America
| | - Deborah H. Glueck
- Dept. of Biostatistics and Informatics, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Sarah M. Kreidler
- Dept. of Biostatistics and Informatics, University of Colorado Denver, Aurora, Colorado, United States of America
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Kreidler SM, Muller KE, Grunwald GK, Ringham BM, Coker-Dukowitz ZT, Sakhadeo UR, Barón AE, Glueck DH. GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate. J Stat Softw 2013; 54:i10. [PMID: 24403868 PMCID: PMC3882200 DOI: 10.18637/jss.v054.i10] [Citation(s) in RCA: 213] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023] Open
Abstract
GLIMMPSE is a free, web-based software tool that calculates power and sample size for the general linear multivariate model with Gaussian errors (http://glimmpse.SampleSizeShop.org/). GLIMMPSE provides a user-friendly interface for the computation of power and sample size. We consider models with fixed predictors, and models with fixed predictors and a single Gaussian covariate. Validation experiments demonstrate that GLIMMPSE matches the accuracy of previously published results, and performs well against simulations. We provide several online tutorials based on research in head and neck cancer. The tutorials demonstrate the use of GLIMMPSE to calculate power and sample size.
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Guo Y, Logan HL, Glueck DH, Muller KE. Selecting a sample size for studies with repeated measures. BMC Med Res Methodol 2013; 13:100. [PMID: 23902644 PMCID: PMC3734029 DOI: 10.1186/1471-2288-13-100] [Citation(s) in RCA: 201] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2013] [Accepted: 07/29/2013] [Indexed: 11/28/2022] Open
Abstract
Many researchers favor repeated measures designs because they allow the detection of within-person change over time and typically have higher statistical power than cross-sectional designs. However, the plethora of inputs needed for repeated measures designs can make sample size selection, a critical step in designing a successful study, difficult. Using a dental pain study as a driving example, we provide guidance for selecting an appropriate sample size for testing a time by treatment interaction for studies with repeated measures. We describe how to (1) gather the required inputs for the sample size calculation, (2) choose appropriate software to perform the calculation, and (3) address practical considerations such as missing data, multiple aims, and continuous covariates.
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Gribbin MJ, Chi YY, Stewart PW, Muller KE. Confidence regions for repeated measures ANOVA power curves based on estimated covariance. BMC Med Res Methodol 2013; 13:57. [PMID: 23586676 PMCID: PMC3738257 DOI: 10.1186/1471-2288-13-57] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2012] [Accepted: 03/15/2013] [Indexed: 11/25/2022] Open
Abstract
Background Using covariance or mean estimates from previous data introduces randomness
into each power value in a power curve. Creating confidence intervals about
the power estimates improves study planning by allowing scientists to
account for the uncertainty in the power estimates. Driving examples arise
in many imaging applications. Methods We use both analytical and Monte Carlo simulation methods. Our analytical
derivations apply to power for tests with the univariate approach to
repeated measures (UNIREP). Approximate confidence intervals and regions for
power based on an estimated covariance matrix and fixed means are described.
Extensive simulations are used to examine the properties of the
approximations. Results Closed-form expressions are given for approximate power and confidence
intervals and regions. Monte Carlo simulations support the accuracy of the
approximations for practical ranges of sample size, rank of the design
matrix, error degrees of freedom, and the amount of deviation from
sphericity. The new methods provide accurate coverage probabilities for all
four UNIREP tests, even for small sample sizes. Accuracy is higher for
higher power values than for lower power values, making the methods
especially useful in practical research conditions. The new techniques allow
the plotting of power confidence regions around an estimated power curve, an
approach that has been well received by researchers. Free software makes the
new methods readily available. Conclusions The new techniques allow a convenient way to account for the uncertainty of
using an estimated covariance matrix in choosing a sample size for a
repeated measures ANOVA design. Medical imaging and many other types of
healthcare research often use repeated measures ANOVA.
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Vossoughi M, Ayatollahi SMT, Towhidi M, Ketabchi F. On summary measure analysis of linear trend repeated measures data: performance comparison with two competing methods. BMC Med Res Methodol 2012; 12:33. [PMID: 22439982 PMCID: PMC3414745 DOI: 10.1186/1471-2288-12-33] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2011] [Accepted: 03/22/2012] [Indexed: 11/29/2022] Open
Abstract
Background The summary measure approach (SMA) is sometimes the only applicable tool for the analysis of repeated measurements in medical research, especially when the number of measurements is relatively large. This study aimed to describe techniques based on summary measures for the analysis of linear trend repeated measures data and then to compare performances of SMA, linear mixed model (LMM), and unstructured multivariate approach (UMA). Methods Practical guidelines based on the least squares regression slope and mean of response over time for each subject were provided to test time, group, and interaction effects. Through Monte Carlo simulation studies, the efficacy of SMA vs. LMM and traditional UMA, under different types of covariance structures, was illustrated. All the methods were also employed to analyze two real data examples. Results Based on the simulation and example results, it was found that the SMA completely dominated the traditional UMA and performed convincingly close to the best-fitting LMM in testing all the effects. However, the LMM was not often robust and led to non-sensible results when the covariance structure for errors was misspecified. The results emphasized discarding the UMA which often yielded extremely conservative inferences as to such data. Conclusions It was shown that summary measure is a simple, safe and powerful approach in which the loss of efficiency compared to the best-fitting LMM was generally negligible. The SMA is recommended as the first choice to reliably analyze the linear trend data with a moderate to large number of measurements and/or small to moderate sample sizes.
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Affiliation(s)
- Mehrdad Vossoughi
- Department of Biostatistics, Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
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Gurka MJ, Edwards LJ, Muller KE. Avoiding bias in mixed model inference for fixed effects. Stat Med 2011; 30:2696-707. [PMID: 21751227 DOI: 10.1002/sim.4293] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2010] [Accepted: 04/13/2011] [Indexed: 12/22/2022]
Abstract
Analysis of a large longitudinal study of children motivated our work. The results illustrate how accurate inference for fixed effects in a general linear mixed model depends on the covariance model selected for the data. Simulation studies have revealed biased inference for the fixed effects with an underspecified covariance structure, at least in small samples. One underspecification common for longitudinal data assumes a simple random intercept and conditional independence of the within-subject errors (i.e., compound symmetry). We prove that the underspecification creates bias in both small and large samples, indicating that recruiting more participants will not alleviate inflation of the Type I error rate associated with fixed effect inference. Enumerations and simulations help quantify the bias and evaluate strategies for avoiding it. When practical, backwards selection of the covariance model, starting with an unstructured pattern, provides the best protection. Tutorial papers can guide the reader in minimizing the chances of falling into the often spurious software trap of nonconvergence. In some cases, the logic of the study design and the scientific context may support a structured pattern, such as an autoregressive structure. The sandwich estimator provides a valid alternative in sufficiently large samples. Authors reporting mixed-model analyses should note possible biases in fixed effects inference because of the following: (i) the covariance model selection process; (ii) the specific covariance model chosen; or (iii) the test approximation.
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Affiliation(s)
- Matthew J Gurka
- Department of Community Medicine, School of Medicine, West Virginia University, PO Box 9190, Morgantown, WV 26506-9190, USA.
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Kairalla JA, Coffey CS, Muller KE. GLUMIP 2.0: SAS/IML Software for Planning Internal Pilots. J Stat Softw 2008; 28. [PMID: 27774042 DOI: 10.18637/jss.v028.i07] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Internal pilot designs involve conducting interim power analysis (without interim data analysis) to modify the final sample size. Recently developed techniques have been described to avoid the type I error rate inflation inherent to unadjusted hypothesis tests, while still providing the advantages of an internal pilot design. We present GLUMIP 2.0, the latest version of our free SAS/IML software for planning internal pilot studies in the general linear univariate model (GLUM) framework. The new analytic forms incorporated into the updated software solve many problems inherent to current internal pilot techniques for linear models with Gaussian errors. Hence, the GLUMIP 2.0 software makes it easy to perform exact power analysis for internal pilots under the GLUM framework with independent Gaussian errors and fixed predictors.
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