Adjustment for compliance behavior in trials of epidural analgesia in labor using instrumental variable meta-analysis

Sensitivity of estimands in clinical trials with imperfect compliance

In clinical trials that are subject to noncompliance, the commonly used intention-to-treat estimand is valid as a causal effect of treatment assignment but is sensitive to the level of compliance. An alternative estimand, the complier average causal effect (CACE), measures the average effect of treatment received in the latent subset of subjects who would comply with either assigned treatment. Because the principal stratum of compliers can vary with the circumstances of the trial, CACE too depends on the compliance fraction. We propose a model in which an underlying latent proto-compliance interacts with trial characteristics to determine a subject's compliance behavior. When the latent compliance is independent of the individual treatment effect, the average causal effect is constant across compliance classes, and CACE is robust across trials and equal to the population average causal effect. We demonstrate the potential degree of sensitivity of CACE in a simulation study, an analysis of data from a trial of vitamin A supplementation in children, and a meta-analysis of trials of epidural analgesia in labor.

Estimating the Complier Average Causal Effect in a Meta-Analysis of Randomized Clinical Trials With Binary Outcomes Accounting for Noncompliance: A Generalized Linear Latent and Mixed Model Approach

Noncompliance, a common problem in randomized clinical trials (RCTs), can bias estimation of the effect of treatment receipt using a standard intention-to-treat analysis. The complier average causal effect (CACE) measures the effect of an intervention in the latent subpopulation that would comply with their assigned treatment. Although several methods have been developed to estimate the CACE in analyzing a single RCT, methods for estimating the CACE in a meta-analysis of RCTs with noncompliance await further development. This article reviews the assumptions needed to estimate the CACE in a single RCT and proposes a frequentist alternative for estimating the CACE in a meta-analysis, using a generalized linear latent and mixed model with SAS software (SAS Institute, Inc.). The method accounts for between-study heterogeneity using random effects. We implement the methods and describe an illustrative example of a meta-analysis of 10 RCTs evaluating the effect of receiving epidural analgesia in labor on cesarean delivery, where noncompliance varies dramatically between studies. Simulation studies are used to evaluate the performance of the proposed method.

Rejoinder to "CACE and meta-analysis (letter to the editor)" by Stuart Baker

First, we thank Dr. Baker for his thoughtful comments on our paper describing a Bayesian hierarchical model to estimate the complier average causal effect (CACE) in meta-analysis. It brings out several points that are helpful to readers in understanding estimation of CACE in randomized trials with noncompliance.

Attention to childbirth and delivery in a university hospital: comparison of practices developed after Network Stork

OBJECTIVE

to compare, after four years of the implementation of the Stork Network, the obstetric practices developed in a university hospital according to the classification of the World Health Organization.

METHOD

cross-sectional study carried out in the year of adherence to the Stork Network (377 women) and replicated four years later (586 women). Data were obtained through medical records and a structured questionnaire. The Chi-square test was used in the analysis.

RESULTS

four years after the implementation of the Stork Network, in Category A practices (demonstrably useful practices/good practices), there was increased frequency of companions, non-pharmacological methods, skin-to-skin contact and breastfeeding stimulation, and decreased freedom of position/movement. In Category B (harmful practices), there was reduction of trichotomy and increased venoclysis. In Category C (practices with no sufficient evidence), there was increase of Kristeller's maneuver. In Category D (improperly used practices), the percentage of digital examinations above the recommended level increased, as well as of analgesics and analgesia, and there was decrease of episiotomy.

CONCLUSION

these findings indicate the maintenance of a technocratic and interventionist assistance and address the need for changes in the obstetric care model. A globally consolidated path is the incorporation of midwife nurses into childbirth for the appropriate use of technologies and the reduction of unnecessary interventions.

A Bayesian hierarchical model estimating CACE in meta-analysis of randomized clinical trials with noncompliance

Noncompliance to assigned treatment is a common challenge in analysis and interpretation of randomized clinical trials. The complier average causal effect (CACE) approach provides a useful tool for addressing noncompliance, where CACE is defined as the average difference in potential outcomes for the response in the subpopulation of subjects who comply with their assigned treatments. In this article, we present a Bayesian hierarchical model to estimate the CACE in a meta-analysis of randomized clinical trials where compliance may be heterogeneous between studies. Between-study heterogeneity is taken into account with study-specific random effects. The results are illustrated by a re-analysis of a meta-analysis comparing the effect of epidural analgesia in labor versus no or other analgesia in labor on the outcome cesarean section, where noncompliance varied between studies. Finally, we present simulations evaluating the performance of the proposed approach and illustrate the importance of including appropriate random effects and the impact of over- and under-fitting.

Adjusting for bias in unblinded randomized controlled trials

It may not always be possible to blind participants of a randomized controlled trial for treatment allocation. As a result, estimators of the actual treatment effect may be biased. In this paper, we will extend a novel method, originally introduced in genetic research, for instrumental variable meta-analysis, adjusting for bias due to unblinding of trial participants. Using simulation studies, this novel method, "Egger Correction for non-Adherence", is introduced and compared to the performance of the "intention-to-treat," "as-treated," and conventional "instrumental variable" estimators. Scenarios considered (time-varying) non-adherence, confounding, and between-study heterogeneity. The effect of treatment on a binary endpoint was quantified by means of a risk difference. In all scenarios with unblinded treatment allocation, the Egger Correction for non-Adherence method was the least biased estimator. However, unless the variation in adherence was relatively large, precision was lacking, and power did not surpass 0.50. As a comparison, in a meta-analysis of blinded randomized controlled trials, power of the conventional IV estimator was 1.00 versus at most 0.14 for the Egger Correction for non-Adherence estimator. Due to this lack of precision and power, we suggest to use this method mainly as a sensitivity analysis.