Estimands in heath technology assessment: a causal inference perspective

Lancker, Kelly Van; Vo, Tat‐Thang; Akacha, Mouna

doi:10.1002/sim.9539

Cited by 5 publications

(5 citation statements)

References 19 publications

(13 reference statements)

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“…8,25 Recently, novel methods have been proposed to improve standard MAIC and STC, for example, by allowing the use of data-driven or machine learning techniques in the estimation of the weight model (in MAIC) and of the outcome model (in STC). 13 Alternative matching schemes are also suggested to enhance the performance of MAIC when the effective sample size is small, 14,26,27 or when an observational study is included in the analysis. 28 These new techniques should be more widely used for practical applications in the future.…”

Section: Reporting and Discussionmentioning

confidence: 99%

Population adjusted‐indirect comparisons in health technology assessment: A methodological systematic review

Truong

Tran

Lê

et al. 2023

Research Synthesis Methods

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In health technology assessment (HTA), population‐adjusted indirect comparisons (PAICs) are increasingly considered to adjust for the difference in the target population between studies. We aim to assess the conduct and reporting of PAICs in recent HTA practice, by performing, a methodological systematic review of studies implementing PAICs from PubMed, EMBASE Classic, Embase/Ovid Medline All, and Cochrane databases from January 1, 2010 to Feb 13, 2023. Four independent researchers screened the titles, abstracts, and full‐texts of the identified records, then extracted data on methodological and reporting characteristics of 106 eligible articles. Most PAIC analyses (96.9%, n = 157) were conducted by (or received funding from) pharmaceutical companies. Prior to adjustment, 44.5% of analyses (n = 72) (partially) aligned the eligibility criteria of different studies to enhance the similarity of their target populations. In 37.0% of analyses (n = 60), the clinical and methodological heterogeneity across studies were extensively assessed. In 9.3% of analyses (n = 15), the quality (or bias) of individual studies was evaluated. Among 18 analyses using methods that required an outcome model specification, results of the model fitting procedure were adequately reported in three analyses (16.7%). These findings suggest that the conduct and reporting of PAICs are remarkably heterogeneous and suboptimal in current practice. More recommendations and guidelines on PAICs are thus warranted to enhance the quality of these analyses in the future.

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Section: Reporting and Discussionmentioning

confidence: 99%

Population adjusted‐indirect comparisons in health technology assessment: A methodological systematic review

Truong

Tran

Lê

et al. 2023

Research Synthesis Methods

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“…There are two main ways to calculate the true value of the relative effect of treatment C versus B in the AC trial population,

{normalΔ}_{BC (italicAC)}

: the marginal relative effect and the conditional relative effect. This topic has been debated particularly between Remiro‐Azócar et al 17 and Phillippo et al, 25 and attracts ongoing attention and discussion from other researchers 28–30 . It is clear that MAIC methods target marginal relative effects.…”

Section: Discussionmentioning

confidence: 99%

“…This topic has been debated particularly between Remiro-Az ocar et al 17 and Phillippo et al, 25 and attracts ongoing attention and discussion from other researchers. [28][29][30] It is clear that MAIC methods target marginal relative effects. See Remiro-Az ocar et al, 17 and the response of Phillippo et al, 25 for discussion related to the type of conditional treatment effects that ML-NMR targets.…”

Section: Discussionmentioning

confidence: 99%

Four alternative methodologies for simulated treatment comparison: How could the use of simulation be re‐invigorated?

Zhang,

Bujkiewicz,

Jackson

2023

Research Synthesis Methods

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Simulated treatment comparison (STC) is an established method for performing population adjustment for the indirect comparison of two treatments, where individual patient data (IPD) are available for one trial but only aggregate level information is available for the other. The most commonly used method is what we call ‘standard STC’. Here we fit an outcome model using data from the trial with IPD, and then substitute mean covariate values from the trial where only aggregate level data are available, to predict what the first of these trial's outcomes would have been if its population had been the same as the second. However, this type of STC methodology does not involve simulation and can result in bias when the link function used in the outcome model is non‐linear. An alternative approach is to use the fitted outcome model to simulate patient profiles in the trial for which IPD are available, but in the other trial's population. This stochastic alternative presents additional challenges. We examine the history of STC and propose two new simulation‐based methods that resolve many of the difficulties associated with the current stochastic approach. A virtue of the simulation‐based STC methods is that the marginal estimands are then clearly targeted. We illustrate all methods using a numerical example and explore their use in a simulation study.

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“…Marginal (also called unconditional or populationaveraged) causal estimands reflect the effect of treatment in the population defined, pragmatically speaking, by the trial's inclusion/exclusion criteria. 10 Examples include the treatment effect on the difference scale…”

Section: Treatment Effect Estimandsmentioning

confidence: 99%

“…However, estimands like equations (1)-(3) average over the distribution of X in the larger patient population (from which the observed patients are an assumed random sample) and may not be the most relevant estimand if the target population where the treatment would be applied differs greatly in terms of X from the patients in the trial. 10,16 In such cases, model-based conditional effects are sometimes argued to transport better to external populations or generalize to broader populations, 15 although this requires that the regression model in equation ( 4) is approximately correct. Estimation of these effects is typically done via fitting regression models using maximum likelihood estimation.…”

Section: Treatment Effect Estimandsmentioning

confidence: 99%

Covariate adjustment in randomized controlled trials: General concepts and practical considerations

Van Lancker,

Bretz,

Dukes

2024

Clinical Trials

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There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the US Food and Drug Administration recently issued guidance that emphasizes the importance of distinguishing between conditional and marginal treatment effects. Although these effects may sometimes coincide in the context of linear models, this is not typically the case in other settings, and this distinction is often overlooked in clinical trial practice. Considering these developments, this article provides a review of when and how to use covariate adjustment to enhance precision in randomized controlled trials. We describe the differences between conditional and marginal estimands and stress the necessity of aligning statistical analysis methods with the chosen estimand. In addition, we highlight the potential misalignment of commonly used methods in estimating marginal treatment effects. We hereby advocate for the use of the standardization approach, as it can improve efficiency by leveraging the information contained in baseline covariates while remaining robust to model misspecification. Finally, we present practical considerations that have arisen in our respective consultations to further clarify the advantages and limitations of covariate adjustment.

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Estimands in heath technology assessment: a causal inference perspective

Cited by 5 publications

References 19 publications

Population adjusted‐indirect comparisons in health technology assessment: A methodological systematic review

Population adjusted‐indirect comparisons in health technology assessment: A methodological systematic review

Four alternative methodologies for simulated treatment comparison: How could the use of simulation be re‐invigorated?

Covariate adjustment in randomized controlled trials: General concepts and practical considerations

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