Rectus femoris transfer in children with cerebral palsy: comparing a propensity score‐matched observational study to a randomized controlled trial

Schwartz, Michael H.; Ries, Andrew J.

doi:10.1111/dmcn.14709

Cited by 9 publications

(7 citation statements)

References 35 publications

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“…The replication crisis in science is real. There are vanishingly few examples of important clinical gait studies being replicated or refuted [ 43 ]. To encourage open and transparent scientific practice, we are providing a detailed Rmarkdown file and a sample dataset.…”

Section: Discussionmentioning

confidence: 99%

A model for understanding the causes and consequences of walking impairments

et al. 2022

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Walking is an important skill with positive impacts on health, function, and well-being. Many disorders impair walking and its positive impacts through a variety of complex and interrelated mechanisms. Any attempt to understand walking impairments, or the effects of interventions intended to treat these impairments, must respect this complexity. Therefore, our main objectives in conducting this study were to (1) propose a comprehensive model for quantifying the causes and consequences of walking impairments and (2) demonstrate the potential utility of the model for supporting clinical care and addressing basic scientific questions related to walking. To achieve these goals, we introduced a model, described by a directed acyclic graph, consisting of 10 nodes and 23 primary causal paths. We gave detailed descriptions of each node and path based on domain knowledge. We then demonstrated the model’s utility using a large sample of gait data (N = 9504) acquired as part of routine care at a regional referral center. We analyzed five relevant examples that involved many of the model’s nodes and paths. We computed causal effect magnitudes as Shapley values and displayed the overall importance of variables (mean absolute Shapley value), the variation of Shapley values with respect to underlying variables, and Shapley values for individual observations (case studies). We showed that the model was plausible, captured some well-known cause-effect relationships, provided new insights into others, and generated novel hypotheses requiring further testing through simulation or experiment. To aid in transparency, reproducibility, and future enhancements we have included an extensively commented Rmarkdown file and a deidentified data set.

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Section: Discussionmentioning

confidence: 99%

A model for understanding the causes and consequences of walking impairments

et al. 2022

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“…The replication crisis in science is real. There are vanishingly few examples of important clinical gait studies being replicated or refuted (42). To encourage open and transparent scientific practice, we are providing a detailed Rmarkdown file and a sample dataset.…”

Section: Future Directionsmentioning

confidence: 99%

A Model for Understanding the Causes and Consequences of Walking Impairments

Schwartz

Steele

Ries

et al. 2022

Preprint

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Walking is an important skill with positive impacts on health, function, and well-being. Many disorders impair walking and its positive impacts through a variety of complex and interrelated mechanisms. Any attempt to understand walking impairments, or the effects of interventions intended to treat these impairments, must respect this complexity. Therefore, our main objectives in conducting this study were to (1) propose a comprehensive model for quantifying the causes and consequences of walking impairments and (2) demonstrate the potential utility of the model for supporting clinical care and addressing basic scientific questions related to walking. To achieve these goals, we introduced a model, described by a directed acyclic graph, consisting of 10 nodes and 23 primary causal paths. We gave detailed descriptions of each node and path based on domain knowledge. We then demonstrated the model's utility using a large sample of gait data (N = 9504) acquired as part of routine care at a regional referral center. We analyzed five relevant examples that involved many of the model's nodes and paths. We computed causal effect magnitudes as Shapley values and displayed the overall importance of variables (mean absolute Shapley value), the variation of Shapley values with respect to underlying variables, and Shapley values for individual observations (case studies). We showed that the model was plausible, captured some well-known cause-effect relationships, provided new insights into others, and generated novel hypotheses requiring further testing through simulation or experiment. To aid in transparency, reproducibility, and future enhancements we have included an extensively commented Rmarkdown file and a deidentified data set.

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“…Causal inference has gained popularity over the years and has been validated by reproducing the results of RCTs and deriving accurate effects from synthetic data. For example, in the context of CP treatment, we have recently shown that a standard causal inference technique can accurately and precisely estimate the effects of rectus femoris transfer compared to an RCT (12). We have used similar methods to estimate the effect of SEMLS (13).…”

Section: Causal Inferencementioning

confidence: 99%

Estimating the Efficacy of Common Treatments in Children and Young Adults Diagnosed with Cerebral Palsy Using Three Machine Learning Algorithms

Schwartz

Ries

Georgiadis

2021

Preprint

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Background: Orthopedic and neurological deformity are often treated in children and young adults with cerebral palsy (CP). Due to challenges arising from combinatorics, research funding priorities, and medical practicalities, the efficacy of these treatments is not well studied. Objectives: Our goal was to estimate the efficacy of 13 common orthopedic and neurological treatments at four different levels of outcome in children and young adults diagnosed with CP. The outcome levels considered were anatomy and physiology, gait parameter, overall gait pattern, and function. Methods We used three well-establish causal inference approaches (direct matching, virtual twins, and Bayesian causal forests) and a large clinical gait analysis database to estimate the average treatment effect on the treated (ATT). We then examined the efficacy across treatments, methods, and outcome levels. Results The median ATT of 13 common treatments in children and young adults with CP, measured as Cohen's D, bordered on medium at the anatomy and physiology level (median [IQR] = 0.42 [.05, .60]) and became smaller as we moved along the causal chain through gait parameter (0.21 [.01, .33]), overall gait pattern (0.09 [.03, .19]), and function (-0.01 [-.06, .13]). Conclusions Current treatments have medium effects on anatomy and physiology, but modest to minimal efficacy on gait and function. Further work is needed to understand the source of heterogeneous treatment effects, which are large in this patient population. Replication or refutation of these findings by other centers will be valuable to establish the generalizability of these results and for benchmarking of best practices.

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Rectus femoris transfer in children with cerebral palsy: comparing a propensity score‐matched observational study to a randomized controlled trial

Cited by 9 publications

References 35 publications

A model for understanding the causes and consequences of walking impairments

A model for understanding the causes and consequences of walking impairments

A Model for Understanding the Causes and Consequences of Walking Impairments

Estimating the Efficacy of Common Treatments in Children and Young Adults Diagnosed with Cerebral Palsy Using Three Machine Learning Algorithms

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