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Pragmatic trials: ignoring a mediator and adjusting for confounding.

ABSTRACT:

Objectives

In pragmatic trials, the new treatment is compared with usual care (heterogeneous control arm) that makes the comparison of the new treatment with each treatment within the control arm more difficult. The usual assumption is that we can fully capture the relations between different quantities. In this paper we use simulation to assess the performance of statistical methods that adjust for confounding when the assumed relations are not true. The true relations contain a mediator and heterogeneity with or without confounding, but the assumption is that there is no mediator and that confounding and heterogeneity are fully captured. The statistical methods that are compared include multivariable logistic regression, propensity score, disease risk score, inverse probability weighting, doubly robust inverse probability weighting and standardisation.

Results

The misconception that there is no mediator can cause to misleading comparative effectiveness of individual treatments when a method that estimates the conditional causal effect is used. Using a method that estimates the marginal causal effect is a better approach, but not for all scenarios.

SUBMITTER: Pericleous P

PROVIDER: S-EPMC6425675 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Pragmatic trials: ignoring a mediator and adjusting for confounding.

Pericleous Paraskevi P

BMC research notes 20190320 1

<h4>Objectives</h4>In pragmatic trials, the new treatment is compared with usual care (heterogeneous control arm) that makes the comparison of the new treatment with each treatment within the control arm more difficult. The usual assumption is that we can fully capture the relations between different quantities. In this paper we use simulation to assess the performance of statistical methods that adjust for confounding when the assumed relations are not true. The true relations contain a mediato ...[more]

PMID: 30894221

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Project description:BackgroundPatients with previously untreated BRAF V600E mutation-positive melanoma in BREAK-3 showed a median overall survival (OS) of 18.2 months for dabrafenib versus 15.6 months for dacarbazine (hazard ratio [HR], 0.76; 95% confidence interval, 0.48-1.21). Because patients receiving dacarbazine were allowed to switch to dabrafenib at disease progression, we attempted to adjust for the confounding effects on OS.Materials and methodsRank preserving structural failure time models (RPSFTMs) and the iterative parameter estimation (IPE) algorithm were used. Two analyses, "treatment group" (assumes treatment effect could continue until death) and "on-treatment observed" (assumes treatment effect disappears with discontinuation), were used to test the assumptions around the durability of the treatment effect.ResultsA total of 36 of 63 patients (57%) receiving dacarbazine switched to dabrafenib. The adjusted OS HRs ranged from 0.50 to 0.55, depending on the analysis. The RPSFTM and IPE "treatment group" and "on-treatment observed" analyses performed similarly well.ConclusionRPSFTM and IPE analyses resulted in point estimates for the OS HR that indicate a substantial increase in the treatment effect compared with the unadjusted OS HR of 0.76. The results are uncertain because of the assumptions associated with the adjustment methods. The confidence intervals continued to cross 1.00; thus, the adjusted estimates did not provide statistically significant evidence of a treatment benefit on survival. However, it is clear that a standard intention-to-treat analysis will be confounded in the presence of treatment switching-a reliance on unadjusted analyses could lead to inappropriate practice. Adjustment analyses provide useful additional information on the estimated treatment effects to inform decision making.Implications for practiceTreatment switching is common in oncology trials, and the implications of this for the interpretation of the clinical effectiveness and cost-effectiveness of the novel treatment are important to consider. If patients who switch treatments benefit from the experimental treatment and a standard intention-to-treat analysis is conducted, the overall survival advantage associated with the new treatment could be underestimated. The present study applied established statistical methods to adjust for treatment switching in a trial that compared dabrafenib and dacarbazine for metastatic melanoma. The results showed that this led to a substantially increased estimate of the overall survival treatment effect associated with dabrafenib.

Dataset Information

Pragmatic trials: ignoring a mediator and adjusting for confounding.

Objectives

Results

Publications

Pragmatic trials: ignoring a mediator and adjusting for confounding.

Similar Datasets

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