The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration

Kent, David M.; Klaveren, David van; Paulus, Jessica K.; D’Agostino, Ralph B.; Goodman, Steven N.; Hayward, Rodney A.; Ioannidis, John P. A.; Patrick-Lake, Bray; Morton, Sally C.; Pencina, Michael J.; Raman, Gowri; Ross, Joseph S.; Selker, Harry P.; Varadhan, Ravi; Vickers, Andrew; Wong, John B.; Steyerberg, Ewout W.

doi:10.7326/m18-3668

Cited by 113 publications

(157 citation statements)

References 189 publications

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“…Nevertheless, since this review focuses on regression methods which are typically performed on the odds or hazard ratio scales, when we use the terms "effect modifier" and "effect modification" and "statistical interaction", we are generally referring to effect modification on a relative scale (e.g. hazard ratio or odds ratio), unless we otherwise specify-although we recognize that these too are scale dependent concepts [11][12][13][14][15]. Additionally, we note that we generally eschew the term "individual treatment effects", since person level effects cannot be observed or measured in parallel arm clinical trials (owing to the fundamental problem of causal inference, only one counterfactual outcome can be observed in a given patient).…”

Section: Methodsmentioning

confidence: 99%

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Predictive approaches to heterogeneous treatment effects: a scoping review

Rekkas

Paulus

Raman

et al. 2020

BMC Med Res Methodol

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Background Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. Methods We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. Results The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). Conclusions Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.

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Section: Methodsmentioning

confidence: 99%

“…The TEP was comprised of 16 experts who represented various perspectives on predictive HTE analyses, including expertise in HTE, prediction modeling, clinical trials, and guideline development as well as a patient advocate. More details on the TEP are available in the PATH Statement [ 12 , 13 ].…”

Section: Methodsmentioning

confidence: 99%

Predictive approaches to heterogeneous treatment effects: a scoping review

Rekkas

Paulus

Raman

et al. 2020

BMC Med Res Methodol

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“…Harrell and Lazzeroni [ 45 ] laid out some initial steps one can follow toward an optimal decision making using both RCTs and electronic health record (EHR) data. We also refer the reader to the recent PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement [ 46 , 47 ], developed to provide guidance for predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. Predictive HTE analysis aims to express treatment effects in terms of predicted risks and predict which of 2 or more treatments will be better for a particular individual, which aligns closely with our review aim here.…”

Section: Discussionmentioning

confidence: 99%

A scoping review of causal methods enabling predictions under hypothetical interventions

et al. 2021

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Background The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications, this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions. Aims We aimed to identify published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and potential pitfalls and challenges with using the method. Finally, we aimed to highlight unresolved methodological challenges. Methods We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used for predictions under hypothetical interventions. We included both methodologies proposed in statistical/machine learning literature and methodologies used in applied studies. Results We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full-text screening, of which 13 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation. Conclusions There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models: (1) enriching prediction models derived from observational studies with estimated causal effects from clinical trials and meta-analyses and (2) estimating prediction models and causal effects directly from observational data. These methods require extending to dynamic treatment regimes, and consideration of multiple interventions to operationalise a clinical decision support system. Techniques for validating ‘causal prediction models’ are still in their infancy.

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“…It is well-recognized, however, that most outcomes are not caused by single exposures in isolation but by multiple exposures in combination. That is why experimental and observational studies frequently report results of subgroup or stratified analyses [4][5][6][7][8][9][10][11][12], to explore if the effect of the primary exposure under study depends on the level of another factor or on the effect of a secondary exposure. Indeed, it is often appropriate to investigate if the causal relation between an exposure and outcome under study is different for different types or groups of individuals [11][12][13][14][15][16][17].…”

Section: Effect Modification and Interaction Are Natural Aspects Of Cmentioning

confidence: 99%

Tutorial: A nontechnical explanation of the counterfactual definition of effect modification and interaction

Bours

2021

Journal of Clinical Epidemiology

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The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration

Cited by 113 publications

References 189 publications

Predictive approaches to heterogeneous treatment effects: a scoping review

Predictive approaches to heterogeneous treatment effects: a scoping review

A scoping review of causal methods enabling predictions under hypothetical interventions

Tutorial: A nontechnical explanation of the counterfactual definition of effect modification and interaction

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