Adherence-adjustment in placebo-controlled randomized trials: An application to the candesartan in heart failure randomized trial

Impact of angiotensin receptor-neprilysin inhibition (ARNI) in improving ejection fraction and left and right ventricular remodeling in heart failure

Angiotensin receptor neprilysin inhibitors (ARNI), a new therapeutic class of agents acting on the renin angiotensin aldosterone system (RAAS) and neutral endopeptidase system has been developed in treatment of ventricular remodeling and has attracted considerable attention. The first in class is LCZ696, which is a molecule that combines Valsartan (ARB) and Sacubitril (neprilysin inhibitor) within a single substance. Sacubitril-Valsartan is the first angiotensin receptor enkephalin inhibitors (ARNI), which can block angiotensin II type 1 receptor (AT1R) while inhibiting enkephalin (NEP) and effectively reverse ventricular remodeling in heart failure patients. It has been recommended by the European and American authoritative guidelines on heart failure as Class I for the treatment of chronic heart failure particularly as intensive care medicine. Sacubitril-Valsartan demonstrated significant effects in improving left ventricular performance and remodeling in patients with heart failure with reduced ejection fraction. Sacubitril acts on increased levels of circulating natriuretic peptides by preventing their enzymatic breakdown and Valsartan, which acts to lessen the effects of the RAAS. However, not more research has been done on its effects on the right ventricle remodeling. This review aimed to assess the impact of angiotensin receptor neprilysin inhibitors on left and right ventricular remodeling in heart failure patients.

Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials

Pragmatic trials offer practical means of obtaining real-world evidence to help improve decision-making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable-based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non-adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies.

Contrasting approaches for addressing non-adherence in randomized controlled trials: An illustration from the REFLUX trial

BACKGROUND

In many randomized controlled trials, a substantial proportion of patients do not comply with the treatment protocol to which they have been randomly assigned. Randomized controlled trials are required to report results according to the intention-to-treat estimand, but recent methodological guidance recognizes the importance of estimating other causal quantities.

METHODS

This article outlines an analytical framework for randomized controlled trials with non-compliance. We apply the ICH E9 (R1) addendum and combine it with the potential outcomes framework to define key estimands, outline the major assumptions for identification of each estimand, and highlight the assumptions that cannot be verified from the randomized controlled trial data. We contrast the assumptions and estimates in a re-analysis of the REFLUX trial. We report alternative estimates for the effectiveness of receipt of laparoscopic surgery versus medical management for patients with gastro-intestinal reflux disease.

RESULTS

The article finds that adjusted as-treated and per-protocol estimates were similar in magnitude to those based intention-to-treat methods. Instrumental variable estimates of the complier average causal effect were larger, with wider confidence intervals.

CONCLUSION

We recommend that in randomized controlled trials with non-compliance, studies should outline which estimand is most relevant to the study context, evaluate key assumptions, and present estimates from a range of methods as a sensitivity analysis.

Intention-to-treat analysis when only a baseline value is available

Objectives

How to perform an intention to treat (ITT) analysis when a patient has a baseline value but no follow-up measurements is problematic. The purpose of this study was to compare different methods that deal with this problem, i.e. no imputation (standard and alternative mixed model analysis), single imputation (i.e. baseline value carried forward), and multiple imputation (selective and non-selective).

Study design and setting

We used a simulation study with different scenarios regarding 1) the association between missingness and the baseline value, 2) whether the patients did or did not receive the treatment, and 3) the percentage of missing data, and two real life data sets.

Results

Bias and coverage were comparable between the two mixed model analyses and multiple imputation in most situations including the real life data examples. Only in the situation when the patients in the treatment group were simulated not to have received the treatment, selective imputation using this information outperformed all other methods.

Conclusions

In most situations a standard mixed model analysis without imputation is appropriate as ITT analysis. However, when patients with missing follow-up data allocated to the treatment group did not received treatment, it is advised to use selective imputation, using this information, although the results should be interpreted with caution.

Adjusting for adherence in randomized trials when adherence is measured as a continuous variable: An application to the Lipid Research Clinics Coronary Primary Prevention Trial

Background:

Clinicians and patients may be more interested in per-protocol effect estimates than intention-to-treat effect estimates from randomized trials. However, per-protocol effect estimates may be biased due to insufficient adjustment for prognostic factors that predict adherence. Adjustment for this bias is possible when appropriate methods, such as inverse probability weighting, are used. But, when adherence is measured as a continuous variable, constructing these weights can be challenging.

Methods:

In the placebo arm of the Lipid Research Clinics Coronary Primary Prevention Trial, we estimated the 7-year cumulative incidence of coronary heart disease under 100% adherence and 0% adherence to placebo. We used dose-response discrete-hazards models with inverse probability weighting to adjust for pre- and post-randomization covariates. We considered several continuous distributions for constructing the inverse probability weights.

Results:

The risk difference estimate for 100% adherence compared with 0% adherence ranged from −7.7 to −6.1 percentage points without adjustment for baseline and post-baseline covariates, and ranged from −1.8 to 2.2 percentage points with adjustment using inverse probability weights, depending on the dose-response model and inverse probability weight distribution used.

Conclusions:

Methods which appropriately adjust for time-varying post-randomization variables can explain away much of the bias in the “effect” of adherence to placebo. When considering continuous adherence, investigators should consider several models as estimates may be sensitive to the model chosen.