Document Type

Journal Article

Publisher

BioMed Central Ltd

Faculty

Faculty of Health, Engineering and Science

School

School of Exercise and Health Sciences/ECU Health and Wellness Institute

RAS ID

16988

Comments

This article was originally published as: Buffart, L., Kalter, J., Chinapaw, M., Heymans, M., Aaronson, N., Courneya, K., Jacobsen, P., Newton, R. , Verdonck-de Leeuw, I., & Brug, J. (2013). Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS): Rationale and design for meta-analyses of individual patient data of randomized controlled trials that evaluate the effect of physical activity and psychosocial interventions on health-related quality of life in cancer survivors. Systematic Reviews, 2(1), Article 75. Original article available here

Abstract

Effective interventions to improve quality of life of cancer survivors are essential. Numerous randomized controlled trials have evaluated the effects of physical activity or psychosocial interventions on health-related quality of life of cancer survivors, with generally small sample sizes and modest effects. Better targeted interventions may result in larger effects. To realize such targeted interventions, we must determine which interventions that are presently available work for which patients, and what the underlying mechanisms are (that is, the moderators and mediators of physical activity and psychosocial interventions). Individual patient data meta-analysis has been described as the ‘gold standard’ of systematic review methodology. Instead of extracting aggregate data from study reports or from authors, the original research data are sought directly from the investigators. Individual patient data meta-analyses allow for adequate statistical analysis of intervention effects and moderators of such effects. Here, we report the rationale and design of the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) Consortium. The primary aim of POLARIS is 1) to conduct meta-analyses based on individual patient data to evaluate the effect of physical activity and psychosocial interventions on the health-related quality of life of cancer survivors; 2) to identify important demographic, clinical, personal, or intervention-related moderators of the effect; and 3) to build and validate clinical prediction models identifying the most relevant predictors of intervention success.

DOI

10.1186/2046-4053-2-75

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 2.0 License.

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