Network meta-analysis of longitudinal data using fractional polynomials

2015 ◽  
Vol 34 (15) ◽  
pp. 2294-2311 ◽  
Author(s):  
J. P. Jansen ◽  
M. C. Vieira ◽  
S. Cope
Keyword(s):  
Longitudinal Data ◽  
Meta Analysis ◽  
Fractional Polynomials

Lag as moderator meta-analysis: A methodological approach for synthesizing longitudinal data

2018 ◽  
Vol 43 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Noel A. Card
Keyword(s):  
Longitudinal Data ◽  
Human Development ◽  
Longitudinal Research ◽  
Meta Analysis ◽  
Methodological Approach ◽  
Mixed Effects ◽  
Effect Sizes ◽  
Time Lags ◽  
Research Findings ◽  
The Impact

Longitudinal data are common and essential to understanding human development. This paper introduces an approach to synthesizing longitudinal research findings called lag as moderator meta-analysis (LAMMA). This approach capitalizes on between-study variability in time lags studied in order to identify the impact of lag on estimates of stability and longitudinal prediction. The paper introduces linear, nonlinear, and mixed-effects approaches to LAMMA, and presents an illustrative example (with syntax and annotated output available as online Supplementary Materials). Several extensions of the basic LAMMA are considered, including artifact correction, multiple effect sizes from studies, and incorporating age as a predictor. It is hoped that LAMMA provides a framework for synthesizing longitudinal data to promote greater accumulation of knowledge in developmental science.

scholarly journals Bayesian splines versus fractional polynomials in network meta-analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Andreas Heinecke ◽  
Marta Tallarita ◽  
Maria De Iorio
Keyword(s):  
Meta Analysis ◽  
Indirect Comparison ◽  
Real Data ◽  
Multiple Time ◽  
Computationally Efficient ◽  
Fractional Polynomials ◽  
Time Points ◽  
Model Based ◽  
Multiple Treatments ◽  
Indirect Comparisons

Abstract Background Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. Methods In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. Results We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. Conclusions The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

Growth factors in Anorexia Nervosa: a systematic review and meta-analysis of cross-sectional and longitudinal data

2021 ◽  
pp. 1-42
Author(s):  
Johanna Louise Keeler ◽  
Lauren Robinson ◽  
Rosemarie Keeler-Schäffeler ◽  
Bethan Dalton ◽  
Janet Treasure ◽  
...  
Keyword(s):  
Systematic Review ◽  
Anorexia Nervosa ◽  
Growth Factors ◽  
Longitudinal Data ◽  
Meta Analysis ◽  
Cross Sectional
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