Is the relationship among outcome variables shown in randomized trials?

Evaluation of selective outcome reporting and trial registration practices among addiction clinical trials

BACKGROUND AND AIMS

Selective outcome reporting occurs when trialists pre-specify primary and secondary outcomes during trial planning but alter the definitions in the published report. Here, we investigate selective outcome reporting in published addiction randomized controlled trials (RCTs) and evaluate whether particular funding sources are associated with an increased likelihood of selective outcome reporting.

DESIGN

We conducted a cross-sectional study of published addiction clinical trials. A PubMed search was performed to identify RCTs in addiction journals from 2013 to 2017. Included studies used a randomized design to address one of the following topics: (1) drug, alcohol and tobacco addiction prevention, (2) stabilization following excessive use of a substance, (3) relapse prevention or (4) recovery maintenance.

SETTING

Single-center, medical research institution.

PARTICIPANTS

Our sample included 162 RCTs that were prospectively registered with a clearly defined primary outcome.

MEASUREMENT

We extracted the following items from addiction RCTs: journal, funding source, trial registry number (if included), sample size, dates of subject enrollment, whether primary and secondary outcomes were denoted, all published outcomes, P-value for all outcomes and whether authors mentioned any deviations from the trial protocol as it related to RCT outcomes.

FINDINGS

In total, 47 of 162 RCTs (29.0%) had at least one major discrepancy between the trial registry and published RCT. Overall, these 47 RCTs included 54 major discrepancies. The most common major discrepancy was demotion of a primary registered outcome (19/54, 35.2%). The majority of RCTs (132/162, 81.5%) were funded from public sources. Additionally, 166 RCTs were excluded from our sample because registration could not be confirmed.

CONCLUSIONS

There is evidence suggestive of selective outcome reporting in addiction randomized controlled trials (RCTs). The most common major discrepancies pertained to the primary outcome.

Bin-CE: A comprehensive web application to decide upon the best set of outcomes to be combined in a binary composite endpoint

The estimation of the Sample Size Requirement (SSR) when using a binary composite endpoint (i.e. two or more outcomes combined in a unique primary endpoint) is not trivial. Besides information about the rate of events for each outcome, information about the strength of association between the outcomes is crucial, since it can determine an increase or decrease of the SSR. Specifically, the greater the strength of association between outcomes the higher the SSR. We present Bin-CE, a free tool to assist clinicians for computing the SSR for binary composite endpoints. In a first step, the user enters a set of candidate outcomes, the assumed rate of events for each outcome and the assumed effect of therapy on each outcome. Since the strength of the association between outcomes is usually unknown, a semi-parametric approach linking the a priori clinical knowledge of the potential degree of association between outcomes with the exact values of these parameters was programmed with Bin-CE. Bin-CE works with a recursive algorithm to choose the best combination of outcomes that minimizes the SSR. In addition, Bin-CE computes the sample size using different algorithms and shows different figures plotting the magnitude of the sample size reduction, and the effect of different combinations of outcomes on the rate of the primary endpoint. Finally, Bin-CE is programmed to perform sensitivity analyses. This manuscript presents the mathematic bases and introduces the reader to the use of Bin-CE using a real example.

The Use of a Binary Composite Endpoint and Sample Size Requirement: Influence of Endpoints Overlap

Although composite endpoints (CE) are common in clinical trials, the impact of the relationship between the components of a binary CE on the sample size requirement (SSR) has not been addressed. We performed a computational study considering 2 treatments and a CE with 2 components: the relevant endpoint (RE) and the additional endpoint (AE). We assessed the strength of the components' interrelation by the degree of relative overlap between them, which was stratified into 5 groups. Within each stratum, SSR was computed for multiple scenarios by varying the events proportion and the effect of the therapy. A lower SSR using CE was defined as the best scenario for using the CE. In 25 of 66 scenarios the degree of relative overlap determined the benefit of using CE instead of the RE. Adding an AE with greater effect than the RE leads to lower SSR using the CE regardless of the AE proportion and the relative overlap. The influence of overlapping decreases when the effect on RE increases. Adding an AE with lower effect than the RE constitutes the most uncertain situation. In summary, the interrelationship between CE components, assessed by the relative overlap, can help to define the SSR in specific situations and it should be considered for SSR computation.