Boosting the precision of mediation analyses of randomised experiments through covariate adjustment

A Pre-Implementation Enhancement Strategy to Increase the Yield of Training and Consultation for School-Based Behavioral Preventive Practices: a Triple-Blind Randomized Controlled Trial

As the most common setting where youth access behavioral healthcare, the education sector frequently employs training and follow-up consultation as cornerstone implementation strategies to promote the implementation of evidence-based practices (EBPs). However, these strategies alone are not sufficient to promote desirable implementation (e.g., intervention fidelity) and youth behavioral outcomes (e.g., mitigated externalizing behaviors). Theory-informed pragmatic pre-implementation enhancement strategies (PIES) are needed to prevent the lackluster outcomes of training and consultation. Specifically, social cognitive theory explicates principles that inform the design of PIES content and specify mechanisms of behavior change (e.g., "intentions to implement" (ITI)) to target increasing providers' responsiveness to training and consultation. This triple-blind parallel randomized controlled trial preliminarily examined the efficacy of a pragmatic PIES based on social cognitive theories (SC-PIES) to improve implementation and youth behavioral outcomes from universal preventive EBPs in the education sector. Teachers from a diverse urban district were recruited and randomly assigned to the treatment (SC-PIES; ntreatment = 22) or active control condition (administrative meeting; ncontrol = 21). Based on the condition assigned, teachers received the SC-PIES or met with administrators before their EBP training. We assessed teachers' ITI, intervention fidelity, and youth behavioral outcome (academic engagement as an incompatible behavior to externalizing disorders) at baseline, immediately after training, and 6 weeks afterward. A series of ANCOVAs detected sizeable effects of SC-PIES, where teachers who received SC-PIES demonstrated significantly larger improvement in their ITI, intervention fidelity, and youth behaviors as compared to the control. Conditional analyses indicated that teachers' ITI partially mediated the effect of SC-PIES on intervention fidelity, which in turn led to improved youth behaviors. Findings suggest that theory-informed pragmatic PIES targeting providers' ITI can boost their responsiveness to implementation strategies, as reflected in improved implementation behaviors and youth behavioral outcomes. The results have implications for targeting motivational mechanisms of behavior change and situating preventive implementation strategies at the intersection between the preparation and active implementation stages of an implementation process. Limitations and implications for research and practice are discussed. Clinicaltrials.gov: NCT05240222. Registered on: 2/14/2022. Retrospectively registered.  https://clinicaltrials.gov/show/NCT05240222.

Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials

In cancer randomized controlled trials, surrogate endpoints are frequently time-to-event endpoints, subject to the competing risk from the time-to-event clinical outcome. In this context, we introduce a counterfactual-based mediation analysis for a causal assessment of surrogacy. We use a multistate model for risk prediction to account for both direct transitions towards the clinical outcome and indirect transitions through the surrogate outcome. Within the counterfactual framework, we define natural direct and indirect effects with a causal interpretation. Based on these measures, we define the proportion of the treatment effect on the clinical outcome mediated by the surrogate outcome. We estimate the proportion for both the cumulative risk and restricted mean time lost. We illustrate our approach by using 18-year follow-up data from the SPCG-4 randomized trial of radical prostatectomy for prostate cancer. We assess time to metastasis as a surrogate outcome for prostate cancer-specific mortality.

Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding

In this article, we will present statistical methods to assess to what extent the effect of a randomised treatment (versus control) on a time-to-event endpoint might be explained by the effect of treatment on a mediator of interest, a variable that is measured longitudinally at planned visits throughout the trial. In particular, we will show how to identify and infer the path-specific effect of treatment on the event time via the repeatedly measured mediator levels. The considered proposal addresses complications due to patients dying before the mediator is assessed, due to the mediator being repeatedly measured, and due to posttreatment confounding of the effect of the mediator by other mediators. We illustrate the method by an application to data from the LEADER cardiovascular outcomes trial.

Beyond total treatment effects in randomised controlled trials: Baseline measurement of intermediate outcomes needed to reduce confounding in mediation investigations

Background:

Random allocation avoids confounding bias when estimating the average treatment effect. For continuous outcomes measured at post-treatment as well as prior to randomisation (baseline), analyses based on (A) post-treatment outcome alone, (B) change scores over the treatment phase or (C) conditioning on baseline values (analysis of covariance) provide unbiased estimators of the average treatment effect. The decision to include baseline values of the clinical outcome in the analysis is based on precision arguments, with analysis of covariance known to be most precise. Investigators increasingly carry out explanatory analyses to decompose total treatment effects into components that are mediated by an intermediate continuous outcome and a non-mediated part. Traditional mediation analysis might be performed based on (A) post-treatment values of the intermediate and clinical outcomes alone, (B) respective change scores or (C) conditioning on baseline measures of both intermediate and clinical outcomes.

Methods:

Using causal diagrams and Monte Carlo simulation, we investigated the performance of the three competing mediation approaches. We considered a data generating model that included three possible confounding processes involving baseline variables: The first two processes modelled baseline measures of the clinical variable or the intermediate variable as common causes of post-treatment measures of these two variables. The third process allowed the two baseline variables themselves to be correlated due to past common causes. We compared the analysis models implied by the competing mediation approaches with this data generating model to hypothesise likely biases in estimators, and tested these in a simulation study. We applied the methods to a randomised trial of pragmatic rehabilitation in patients with chronic fatigue syndrome, which examined the role of limiting activities as a mediator.

Results:

Estimates of causal mediation effects derived by approach (A) will be biased if one of the three processes involving baseline measures of intermediate or clinical outcomes is operating. Necessary assumptions for the change score approach (B) to provide unbiased estimates under either process include the independence of baseline measures and change scores of the intermediate variable. Finally, estimates provided by the analysis of covariance approach (C) were found to be unbiased under all the three processes considered here. When applied to the example, there was evidence of mediation under all methods but the estimate of the indirect effect depended on the approach used with the proportion mediated varying from 57% to 86%.

Conclusion:

Trialists planning mediation analyses should measure baseline values of putative mediators as well as of continuous clinical outcomes. An analysis of covariance approach is recommended to avoid potential biases due to confounding processes involving baseline measures of intermediate or clinical outcomes, and not simply for increased precision.

Surrogate marker analysis in cancer clinical trials through time-to-event mediation techniques

The meta-analytic approach is the gold standard for validation of surrogate markers, but has the drawback of requiring data from several trials. We refine modern mediation analysis techniques for time-to-event endpoints and apply them to investigate whether pathological complete response can be used as a surrogate marker for disease-free survival in the EORTC 10994/BIG 1-00 randomised phase 3 trial in which locally advanced breast cancer patients were randomised to either taxane or anthracycline based neoadjuvant chemotherapy. In the mediation analysis, the treatment effect is decomposed into an indirect effect via pathological complete response and the remaining direct effect. It shows that only 4.2% of the treatment effect on disease-free survival after five years is mediated by the treatment effect on pathological complete response. There is thus no evidence from our analysis that pathological complete response is a valuable surrogate marker to evaluate the effect of taxane versus anthracycline based chemotherapies on progression free survival of locally advanced breast cancer patients. The proposed analysis strategy is broadly applicable to mediation analyses of time-to-event endpoints, is easy to apply and outperforms existing strategies in terms of precision as well as robustness against model misspecification.