Guidelines for the design of clinical trials with longitudinal outcomes

Galbraith, Sally; Stat., M.; Marschner, Ian C.

doi:10.1016/s0197-2456(02)00205-2

Cited by 53 publications

(75 citation statements)

References 12 publications

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“…Third, the efficiency of optimal CD designs will be influenced by unplanned missingness incurred during data collection. Galbraith and Marschner (2002) found that for linear models, the loss of efficiency due to attrition was within 10 % if the attrition rate was no more than 30 %. OrtegaAzurbuy, Tan, and found that the efficiency loss of D-optimal designs identified using complete data was within 15 % with a 70 % attrition rate at the last time point.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, equally spaced designs tend to be more practical and theoretically relevant than unequally spaced repeated measures (Berger & Wong, 2009;Galbraith & Marschner, 2002). Third, the efficiency of optimal CD designs will be influenced by unplanned missingness incurred during data collection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…PM designs, however, have several potential advantages (Graham, Taylor, & Cumsille, 2001;Mistler & Enders, 2012). First, given a fixed budget, certain PM designs can show higher efficiency than CD designs that include the same number of total observed data points, because they can offer a better balance between the number of repeated measures per participant/pattern and sample size 2 (Galbraith, Stat, & Marschner, 2002;Mistler & Enders, 2012). Second, given a fixed sample size, PM designs require fewer repeated measures from each individual to achieve the same number of measurement occasions in the data as a CD design, at substantially reduced cost (Hogue, Pornprasertmanit, Fry, Rhemtulla, & Little, 2013).…”

Section: Complete and Planned Missing Designsmentioning

confidence: 99%

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Search for efficient complete and planned missing data designs for analysis of change

Fan

Rhemtulla

et al. 2015

Behav Res

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The design of longitudinal data collection is an essential component of any study of change. A well-designed study will maximize the efficiency of statistical tests and minimize the cost of available resources (e.g., budget). Two families of designs have been used to collect longitudinal data: complete data (CD) and planned missing (PM) designs. This article proposes a systematic and flexible procedure named SEEDMC (SEarch for Efficient Designs using Monte Carlo Simulation) to search for efficient CD and PM designs for growth-curve modeling under budget constraints. This procedure allows researchers to identify efficient designs for multiple effects separately and simultaneously, and designs that are robust to MCAR attrition. SEEDMC is applied to identify efficient designs for key change parameters in linear and quadratic growth models. The identified efficient designs are summarized and the strengths and possible extensions of SEEDMC are discussed.Keywords Planned missing data designs . Growth curve modeling . Efficiency . Longitudinal data collectionThe design of longitudinal data collection is an essential component of any study of change. Factors that researchers must consider when planning such designs include but are not limited to the number and allocation of repeated measures, sample size, and minimizing unwanted factors such as carry-over effects and attrition. In addition to these considerations, researchers are typically constrained by a finite budget and limited resources which should also be taken into account at the design stage. A well-designed study will lead to statistical tests that are maximally efficient, that is, precise and powerful (Berger & Wong, 2009), given available resources.Two families of designs can be used to collect longitudinal data: complete data (CD) and planned missing (PM) designs. Each of these designs represents a unique combination of design factors (e.g., number of repeated measures per participant and allocation of the repeated measures to time points), leading to the differential efficiency of target statistical procedures. Given two designs, the more efficient one produces parameter estimates that have smaller sampling variability, leading to smaller standard errors, tighter confidence intervals, and thus higher power to detect non-zero effects. Thus, more efficient designs are preferred.Although past research has studied CD designs and specific PM designs, no study so far has searched for the most efficient design among all possible CD and PM designs. Furthermore, the effect of unplanned missingness (missingness that is not due to design, e.g., attrition) has been studied for CD designs but not for PM designs. Given that unplanned missingness is ubiquitous in longitudinal studies, this is an important issue to address. Identifying efficient designs is not a trivial matter. As shown in the examples below, using an efficient design may save up to 75 % of the research budget while achieving the same efficiency to detect target effects when compared to commonly ...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Complete and Planned Missing Designsmentioning

confidence: 99%

See 1 more Smart Citation

Search for efficient complete and planned missing data designs for analysis of change

Fan

Rhemtulla

et al. 2015

Behav Res

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“…The Weibull survival function is thus given by S(t) = e −λt τ and the corresponding baseline hazard function by h(t) = λτ t τ −1 . The scale parameter λ can be replaced by − log(1 − ω) with ω ∈ [0, 1] as the overall proportion of the subjects in the baseline group who experience the event by time t = 1 (Galbraith and Marschner, 2002). The shape parameter τ ∈ [0, ∞) determines the shape of the hazard function.…”

Section: Weibull Survival Functionmentioning

confidence: 99%

“…Galbraith and Marschner (2002), Passos et al (2011) and Moerbeek (2008) report the optimal design for growth models with continuous outcomes and Tekle et al (2008) study the optimal design for trials with dichotomous outcomes. Moreover, Jóźwiak and Moerbeek (2012) examine the optimum combination of a number of subjects and number of time periods for studies with discrete-time survival responses.…”

Section: Introductionmentioning

confidence: 99%

The influence of a covariate on optimal designs in longitudinal studies with discrete-time survival endpoints

Safarkhani

Moerbeek

2014

Computational Statistics & Data Analysis

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Optimal design Treatment effect estimator Covariate effect and prevalence a b s t r a c tLongitudinal intervention studies on event occurrence can measure the timing of an event at discrete points in time. To design studies of this kind as inexpensively and efficiently as possible, researchers need to decide on the number of subjects and the number of measurements for each subject. Different combinations of these design factors may produce the same level of power, but each combination can have different costs. When applying a cost function, the optimal design gives the optimal number of subjects and measurements, thus maximizing the power for a given budget and achieving sufficient power at minimal costs. Only very limited research has been conducted on the effect of a predictive covariate on optimal designs for a treatment effect estimator. Here, we go one step further than previous studies on optimal designs and demonstrate the extent to which a binary covariate influences the optimal design. An examination of various covariate effects and prevalences shows how substantially the covariate affects the optimal design and this effect is partly associated with the cost ratio between sampling subjects and measurements, and the survival pattern. So since the optimal design is sensitive to misspecification of these factors, we advise researchers to carefully specify the covariate effect and prevalence.

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Clinical Trials

Ryan

2013

Sample Size Determination and Power

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Guidelines for the design of clinical trials with longitudinal outcomes

Cited by 53 publications

References 12 publications

Search for efficient complete and planned missing data designs for analysis of change

Search for efficient complete and planned missing data designs for analysis of change

The influence of a covariate on optimal designs in longitudinal studies with discrete-time survival endpoints

Clinical Trials

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