Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials

Stull, Donald E.

doi:10.1007/s11136-007-9290-5

Cited by 44 publications

(47 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parallel process latent growth models were used to examine relationships between the different cognitive variables and initial everyday functioning as well as linear trajectories of functioning (B. Muthén, 1997; L. K. Muthén & Muthén, 2008). In these models, latent factors, formed by intercepts (initial or baseline status) and slopes (trajectories or annual rates of change), represent person-specific linear growth processes for observed continuous variables measured repeatedly over time (Stull, 2008). Figure 1 shows the basic model specification.…”

Section: Analysis Planmentioning

confidence: 99%

Cognitive Predictors of Everyday Functioning in Older Adults: Results From the ACTIVE Cognitive Intervention Trial

Gross

Rebok

Unverzagt

et al. 2011

The Journals of Gerontology Series B: Psychological Sciences an

View full text Add to dashboard Cite

Section: Analysis Planmentioning

confidence: 99%

Cognitive Predictors of Everyday Functioning in Older Adults: Results From the ACTIVE Cognitive Intervention Trial

Gross

Rebok

Unverzagt

et al. 2011

The Journals of Gerontology Series B: Psychological Sciences an

View full text Add to dashboard Cite

“…Another advantage is the ability of LCM to model and compare trajectories of change in latent variables across multiple groups. Stull (35) suggests that this feature makes LCM a particularly strong approach to analyzing clinical trial data.…”

Section: Quantitative Advancesmentioning

confidence: 99%

Time Issues in Multilevel Interventions for Cancer Treatment and Prevention

2012

View full text Add to dashboard Cite

The concept of time introduces important complexities in designing and evaluating programs, measuring and analyzing individual changes, and estimating intervention effects in multilevel interventions (MLIs). For example, investigators may focus on time to address whether interventions are more effective in early stages of implementation or whether their effects attenuate over time. Assessing how time is experienced by individual patients may influence both the design of MLIs and the analysis of the effects of such interventions. For example, assessing how interventions interact with individual growth trajectories to affect outcomes of interest may allow investigators to gain a more nuanced understanding of an intervention's impact on individuals. At the environmental and organizational levels, interventions that change structures or processes often play out over extended time intervals and not always in linear fashion.In this chapter, we discuss issues of time related to 1) conceptual relevance of time in multilevel cancer interventions, 2) time as a research and program design issue in MLIs, 3) analytic methods for dealing with time in MLIs, and 4) resource considerations and trade-offs when incorporating time as a component of multilevel research. Conceptual Relevance of Time in Multilevel Cancer InterventionsThe empirical literature is scant regarding the role of time in MLI research. The limited number of published studies have cited the challenges of insufficient time allowed for implementing the intervention (1) and for introducing changes into existing work load or routines. Some work has described different ways in which time may be treated in the context of intervention studies. Time has been conceptualized in four ways: 1) "clock and calendar time," which is structured, and addresses "when," "how often," "how long," and "in what order" activities need to occur (this conceptualization recognizes that time may be linear but its effects as experienced are frequently nonlinear); 2) "temporality," which conveys processes that unfold in time; 3) "timing," which is dependent on the organizational context in which an intervention is applied; and 4) "tempo," which is related to a patient's perception of time (eg, the rate at which a patient perceives his or her disease to have progressed) (2,3).The Symptoms Experience in Time (SET), developed for symptom management research, is another conceptual model for understanding time. It incorporates antecedents that include a precipitating event, such as cancer or a disease process. In addition to clock/calendar time and tempo, three other temporal assumptions describe time as both a concrete (ie, biological/social time) and an abstract concept (ie, sense of timelessness amid intense symptoms) (4).This conceptual work underscores the importance of understanding complex temporal dynamics from both the patient's perspective and the care delivery standpoint when planning or analyzing MLIs. Such notions of time imply a range of dynamic interactions between the intervention...

show abstract

“…Linear models such as ANOVA and ANCOVA, when used in repeatedmeasures studies, traditionally need equal group sizes, consistent time intervals, and no missing data [16]. In contrast, latent growth curve models (LGM) have been used in the analysis of longitudinal data [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Aging Disparity of Initial Dose in Patients with Schizophrenia

Chang¹,

Lung²

2018

Neuropsychiatry

View full text Add to dashboard Cite

show abstract

Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials

Abstract: Analysts are encouraged to consider LGM as an additional and informative tool for analyzing clinical trial or other longitudinal data.

Cited by 44 publications

References 30 publications

Cognitive Predictors of Everyday Functioning in Older Adults: Results From the ACTIVE Cognitive Intervention Trial

Cognitive Predictors of Everyday Functioning in Older Adults: Results From the ACTIVE Cognitive Intervention Trial

Time Issues in Multilevel Interventions for Cancer Treatment and Prevention

Aging Disparity of Initial Dose in Patients with Schizophrenia

Contact Info

Product

Resources

About