Based on this DAG of the relationship between air flow reduction (the pathomechanism associated with ventilatory problems in COPD) and one of its clinical manifestations (dyspnea on exertion (DOE)) we see that dynamic hyperinflation plays a role and confounds the effect of having a low maximal inspiratory pressure (a measure of inspiratory muscle strength. Inspiratory muscle training (IMT) is a specific intervention that attempts to address Low MIP. However, in patients with the primary cause of Low MIP being DHI this intervention may be less effective. Measuring DHI and adjusting for this confounder in the subgroup analysis, or even stratifying on it prior to randomization with a block design, would result in a study that is more helpful to therapists considering the potential benefit of IMT in their specific patient as it would isolate another factor that influences the effectiveness of IMT based on the underlying causal structure (Seems simple enough - though primarily not done in these studies on IMT.)
Additionally, if anyone disagrees with the decisions associated with the design (stratification or subgroup analysis) then they at least they see the underlying assumptions and whether the approach taken was rationale. For example, when assessing a trial I need to decide whether I disagree with the underlying causal assumptions of the authors or not. If I disagree I need to consider the grounds on which I disagree. I may disagree but be willing to accept their assumptions because I may not have good ground to disagree. Then, accepting their assumptions, I can assess the rationality of their stratification and/or subgroup analysis, which follows logically from the causal structure. If I do not accept their underlying assumptions and I have good grounds for why that structure is wrong, or if there is a irrational use (improper use of logic) of an accepted structure then I can communicate these concerns (either during the review of the paper, or post publication as an editorial). But they are very separate concerns and should be articulated as such.
So when done right the use of causal models (DAGs) help with the design of clinical practice relevant studies. When used the process of reviewing the study becomes easier (assumptions are more clearly presented). Therefore when done wrong the use of causal models helps with the review and modification of design or analysis.
So to the question: “What can be done to stimulate more research in physical therapy that has direct clinical relevance?” Since causal structure underlies practice and research - more effort should be made to articulate the causal structures in the design and dissemination of the research for it to have direct clinical relevance.
Finally - the DAG (link to DAGitty, and code for modifying) above is available as part of the Physical Therapy DAG repository on GitHub here in the COPD repo.