Key result
A systematic methods review of 28 observational studies on cardiac rehabilitation found that 86% adjusted for confounding, but only 46% reported variable selection methods.
Why the study?
Guidance on building multivariable regression models to control confounding is often unknown to researchers, warranting an investigation of explanatory regression modeling practices in non-randomized cardiac rehabilitation studies.
Systematic Review (n=28)
Most observational studies on cardiac rehabilitation fail to meet common criteria for appropriate statistical model building and lack precise reporting.
Answers are grounded in this paper and cite their sources.
Most cardiac rehabilitation studies inadequately report statistical methods; challenges evidence reliability and underscores need for rigorous modeling in future research.
Randomization is an effective design option to prevent bias from confounding in the evaluation of the causal effect of interventions on outcomes. However, in some cases, randomization is not possible, making subsequent adjustment for confounders essential to obtain valid results. Several methods exist to adjust for confounding, with multivariable modeling being among the most widely used. The main challenge is to determine which variables should be included in the causal model and to specify appropriate functional relations for continuous variables in the model. While the statistical literature gives a variety of recommendations on how to build multivariable regression models in practice, this guidance is often unknown to applied researchers. We set out to investigate the current practice of explanatory regression modeling to control confounding in the field of cardiac rehabilitation, for which mainly non-randomized observational studies are available. In particular, we conducted a systematic methods review to identify and compare statistical methodology with respect to statistical model building in the context of the existing recent systematic review CROS-II, which evaluated the prognostic effect of cardiac rehabilitation. CROS-II identified 28 observational studies, which were published between 2004 and 2018. Our methods review revealed that 24 (86%) of the included studies used methods to adjust for confounding. Of these, 11 (46%) mentioned how the variables were selected and two studies (8%) considered functional forms for continuous variables. The use of background knowledge for variable selection was barely reported and data-driven variable selection methods were applied frequently. We conclude that in the majority of studies, the methods used to develop models to investigate the effect of cardiac rehabilitation on outcomes do not meet common criteria for appropriate statistical model building and that reporting often lacks precision.
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Akbari et al. (2023) conducted a systematic review in Cardiac rehabilitation (n=28). Statistical model building methods was evaluated on Use of methods to adjust for confounding. A systematic methods review of 28 observational studies on cardiac rehabilitation found that 86% adjusted for confounding, but only 46% reported variable selection methods.
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