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Jos W. R. Twisk - One of the best experts on this subject based on the ideXlab platform.
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Applied Multilevel Analysis: Software for Multilevel Analysis
Applied Multilevel Analysis, 2006Co-Authors: Jos W. R. TwiskAbstract:Introduction In the foregoing chapters, all examples of Multilevel Analysis were analysed in MLwiN. Although this software package is specially developed for performing Multilevel Analysis, there are also other software packages that can be used for Multilevel Analysis. In this chapter the example dataset(s) will be reanalysed with other software packages, and any differences in the results will be compared and discussed. For continuous outcome variables the research question concerned the relationship between total cholesterol and age (see Sections 2.2, 2.5 and 2.6.1), for dichotomous outcome variables it was the relationship between hypercholesterolemia and age (see Section 4.2), and for ‘count’ outcome variables the relationship between ‘the number of risk factors’ and age (see Section 4.4). For multinomial logistic Multilevel Analysis the population was divided into three groups, i.e. a group with relatively ‘low’ cholesterol values, a group with relatively ‘moderate’ cholesterol values, and a group with relatively ‘high’ cholesterol values (see Section 4.3). For linear Multilevel Analysis (i.e. Multilevel Analysis with a continuous outcome variable) both a two-level structure (i.e. patients clustered within medical doctors) and a three-level structure (patients clustered within medical doctors and medical doctors are clustered within institutions) will be used in the comparison. Only a two-level structure will be used for logistic Multilevel Analysis (i.e. Multilevel Analysis with a dichotomous outcome variable), for Poisson Multilevel Analysis (i.e. Multilevel Analysis with a ‘count’ outcome variable), and for multinomial logistic Multilevel Analysis (i.e. Multilevel Analysis with a categorical outcome variable).
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Applied Multilevel Analysis: Multilevel Analysis in longitudinal studies
Applied Multilevel Analysis, 2006Co-Authors: Jos W. R. TwiskAbstract:Introduction In the earlier chapters it has been explained that Multilevel Analysis is suitable for the Analysis of correlated data. We have seen examples in which observations of patients were correlated because they ‘belong’ to the same medical doctor, i.e. the observations of patients were clustered within medical doctors. The fact that observations are correlated is probably most pronounced in longitudinal studies in which repeated observations are made within one subject or patient. It is obvious that these observations are (usually) highly correlated. Therefore, the whole theory of Multilevel Analysis, as described in the earlier chapters, can also be applied to longitudinal data. With longitudinal data, the repeated observations are clustered within the subject or patient (see Figure 6.1). Figure 6.1 illustrates a two-level structure, i.e. the observations are the lower level, while the patient is the higher level. This is different from all the examples that have been described before, in which the patients were the lower level. It is of course also possible that the patients are clustered within medical doctors, as was also the situation in the earlier chapters. This is referred to as a three-level structure, i.e. the observations are clustered within the patients and the patients are clustered within the medical doctors (see Figure 6.2).
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Applied Multilevel Analysis: A Practical Guide - Applied Multilevel Analysis : a practical guide
2006Co-Authors: Jos W. R. TwiskAbstract:Preface 1. Introduction 2. Basic principles behind Multilevel Analysis 3. What do we gain by applying Multilevel Analysis? 4. Multilevel Analysis with different outcome variables 5. Multilevel modelling 6. Multilevel Analysis in longitudinal studies 7. Multivariate Multilevel Analysis 8. Sample size calculations in Multilevel studies 9. Software for Multilevel Analysis References Index.
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applied Multilevel Analysis a practical guide
Applied multilevel analysis: a practical guide., 2006Co-Authors: Jos W. R. TwiskAbstract:Preface 1. Introduction 2. Basic principles behind Multilevel Analysis 3. What do we gain by applying Multilevel Analysis? 4. Multilevel Analysis with different outcome variables 5. Multilevel modelling 6. Multilevel Analysis in longitudinal studies 7. Multivariate Multilevel Analysis 8. Sample size calculations in Multilevel studies 9. Software for Multilevel Analysis References Index.
Minot Cleveland - One of the best experts on this subject based on the ideXlab platform.
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neighborhood level influences on physical activity among older adults a Multilevel Analysis
Journal of Aging and Physical Activity, 2004Co-Authors: John K Fisher, Fuzhong Li, Yvonne L Michael, Minot ClevelandAbstract:There is a need for greater understanding of setting-specific influences on physical activity to complement the predominant research paradigm of individual-centered influences on physical activity. In this study, the authors used a cross-sectional Multilevel Analysis to examine a range of neighborhood-level characteristics and the extent to which they were associated with variation in self-reported physical activity among older adults. The sample consisted of 582 community-dwelling residents age 65 years and older (M = 73.99 years, SD = 6.25) recruited from 56 neighborhoods in Portland, OR. Information collected from participants and neighborhood data from objective sources formed a two-level data structure. These hierarchical data (i.e., individuals nested within neighborhoods) were subjected to Multilevel structural-equation-modeling analyses. Results showed that neighborhood social cohesion, in conjunction with other neighborhood-level factors, was significantly associated with increased levels of neig...
Anneke L. Francke - One of the best experts on this subject based on the ideXlab platform.
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A Multilevel Analysis of three randomised controlled trials of the Australian Medical Sheepskin in the prevention of sacral pressure ulcers.
The Medical journal of Australia, 2020Co-Authors: Patriek Mistiaen, Damien Jolley, Sunita Mcgowan, Peter Spreeuwenberg, Mark B Hickey, Anneke L. FranckeAbstract:To assess the effectiveness of the Australian Medical Sheepskin in preventing sacral pressure ulcers (PUs), based on combined data from existing published trials. Data from two randomised controlled trials (RCTs) among Australian hospital patients and one RCT among Dutch nursing home patients were pooled, comprising a total population of 1281 patients from 45 nursing wards in 11 institutions. These data were analysed in two ways: with conventional meta-Analysis based on the published effect sizes; and with Multilevel binary logistic regression based on the combined individual patient data. In the Multilevel Analysis, patient, nursing ward and institution were used as levels and we controlled for sex, age, PU risk and number of days of observation. Incidence of sacral PUs. Overall, the incidence of sacral PUs was 12.2% in the control group versus 5.4% in the intervention group with an Australian Medical Sheepskin. Conventional meta-Analysis showed significantly reduced odds of developing a PU while using the sheepskin (odds ratio [OR], 0.37 [95% CI, 0.17-0.77]). Multilevel Analysis gave an OR of 0.35 and narrowed the confidence interval by almost 50% (95% CI, 0.23-0.55). These analyses of pooled data confirm that the Australian Medical Sheepskin is effective in preventing sacral PUs. Multilevel Analysis of individual patient data gives a more precise effect estimate than conventional meta-Analysis.
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A Multilevel Analysis of three randomised controlled trials of the Australian Medical Sheepskin in the prevention of sacral pressure ulcers.
The Medical Journal of Australia, 2010Co-Authors: Patriek Mistiaen, Damien Jolley, Sunita Mcgowan, Mark Hickey, Peter Spreeuwenberg, Anneke L. FranckeAbstract:Objective: To assess the effectiveness of the Australian Medical Sheepskin in preventing sacral pressure ulcers (PUs), based on combined data from existing published trials. Design and setting: Data from two randomised controlled trials (RCTs) among Australian hospital patients and one RCT among Dutch nursing home patients were pooled, comprising a total population of 1281 patients from 45 nursing wards in 11 institutions. These data were analysed in two ways: with conventional meta-Analysis based on the published effect sizes; and with Multilevel binary logistic regression based on the combined individual patient data. In the Multilevel Analysis, patient, nursing ward and institution were used as levels and we controlled for sex, age, PU risk and number of days of observation. Main outcome measure: Incidence of sacral PUs. Results: Overall, the incidence of sacral PUs was 12.2% in the control group versus 5.4% in the intervention group with an Australian Medical Sheepskin. Conventional metaAnalysis showed significantly reduced odds of developing a PU while using the sheepskin (odds ratio [OR], 0.37 [95% CI, 0.17–0.77]). Multilevel Analysis gave an OR of 0.35 and narrowed the confidence interval by almost 50% (95% CI, 0.23–0.55). Conclusions: These analyses of pooled data confirm that the Australian Medical Sheepskin is effective in preventing sacral PUs. Multilevel Analysis of individual patient
Fuzhong Li - One of the best experts on this subject based on the ideXlab platform.
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A Multilevel Analysis of Change in Neighborhood Walking Activity in Older Adults
Journal of Aging and Physical Activity, 2005Co-Authors: Fuzhong Li, K. John Fisher, Ross C. BrownsonAbstract:The article reports on a Multilevel Analysis conducted to examine change in neighborhood walking activity over a 12-month period in a community-based sample of 28 neighborhoods of 303 older adults age 65 and over. The study employed a Multilevel (residents nested within neighborhoods) and longitudinal (4 repeated measures over 1 year) design and a Multilevel Analysis of change and predictors of change in neighborhood walking activity. Results indicated a significant neighborhood effect, with neighborhood-level walking characterized by a downward trajectory over time. Inclusion of baseline variables using selected perceived neighborhood-level social- and physical-environment measures indicated that neighborhoods with safe walking environments and access to physical activity facilities had lower rates of decline in walking activity. The findings provide preliminary evidence of neighborhood-level change and predictors of change in walking activity in older adults. They also suggest the importance of analyzin...
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neighborhood level influences on physical activity among older adults a Multilevel Analysis
Journal of Aging and Physical Activity, 2004Co-Authors: John K Fisher, Fuzhong Li, Yvonne L Michael, Minot ClevelandAbstract:There is a need for greater understanding of setting-specific influences on physical activity to complement the predominant research paradigm of individual-centered influences on physical activity. In this study, the authors used a cross-sectional Multilevel Analysis to examine a range of neighborhood-level characteristics and the extent to which they were associated with variation in self-reported physical activity among older adults. The sample consisted of 582 community-dwelling residents age 65 years and older (M = 73.99 years, SD = 6.25) recruited from 56 neighborhoods in Portland, OR. Information collected from participants and neighborhood data from objective sources formed a two-level data structure. These hierarchical data (i.e., individuals nested within neighborhoods) were subjected to Multilevel structural-equation-modeling analyses. Results showed that neighborhood social cohesion, in conjunction with other neighborhood-level factors, was significantly associated with increased levels of neig...
John K Fisher - One of the best experts on this subject based on the ideXlab platform.
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neighborhood level influences on physical activity among older adults a Multilevel Analysis
Journal of Aging and Physical Activity, 2004Co-Authors: John K Fisher, Fuzhong Li, Yvonne L Michael, Minot ClevelandAbstract:There is a need for greater understanding of setting-specific influences on physical activity to complement the predominant research paradigm of individual-centered influences on physical activity. In this study, the authors used a cross-sectional Multilevel Analysis to examine a range of neighborhood-level characteristics and the extent to which they were associated with variation in self-reported physical activity among older adults. The sample consisted of 582 community-dwelling residents age 65 years and older (M = 73.99 years, SD = 6.25) recruited from 56 neighborhoods in Portland, OR. Information collected from participants and neighborhood data from objective sources formed a two-level data structure. These hierarchical data (i.e., individuals nested within neighborhoods) were subjected to Multilevel structural-equation-modeling analyses. Results showed that neighborhood social cohesion, in conjunction with other neighborhood-level factors, was significantly associated with increased levels of neig...
