Evaluation of Planned Subgroup Analysis in Protocols of Randomized Clinical Trials

Description of subgroup reporting in clinical trials of chronic diseases: a meta-epidemiological study

INTRODUCTION

In trials, subgroup analyses are used to examine whether treatment effects differ by important patient characteristics. However, which subgroups are most commonly reported has not been comprehensively described.

DESIGN AND SETTINGS

Using a set of trials identified from the US clinical trials register (ClinicalTrials.gov), we describe every reported subgroup for a range of conditions and drug classes.

METHODS

We obtained trial characteristics from ClinicalTrials.gov via the Aggregate Analysis of ClinicalTrials.gov database. We subsequently obtained all corresponding PubMed-indexed papers and screened these for subgroup reporting. Tables and text for reported subgroups were extracted and standardised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Via logistic and Poisson regression models we identified independent predictors of result reporting (any vs none) and subgroup reporting (any vs none and counts). We then summarised subgroup reporting by index condition and presented all subgroups for all trials via a web-based interactive heatmap (https://ihwph-hehta.shinyapps.io/subgroup_reporting_app/).

RESULTS

Among 2235 eligible trials, 23% (524 trials) reported subgroups. Follow-up time (OR, 95%CI: 1.13, 1.04-1.24), enrolment (per 10-fold increment, 3.48, 2.25-5.47), trial starting year (1.07, 1.03-1.11) and specific index conditions (eg, hypercholesterolaemia, hypertension, taking asthma as the reference, OR ranged from 0.15 to 10.44), predicted reporting, sponsoring source and number of arms did not. Results were similar on modelling any result reporting (except number of arms, 1.42, 1.15-1.74) and the total number of subgroups. Age (51%), gender (45%), racial group (28%) were the most frequently reported subgroups. Characteristics related to the index condition (severity/duration/types etc) were frequently reported (eg, 69% of myocardial infarction trials reported on its severity/duration/types). However, reporting on comorbidity/frailty (five trials) and mental health (four trials) was rare.

CONCLUSION

Other than age, sex, race ethnicity or geographic location and characteristics related to the index condition, information on variation in treatment effects is sparse.

PROSPERO REGISTRATION NUMBER

CRD42018048202.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Subgroup analyses in venous thromboembolism trials reporting pharmacological interventions: A systematic review

INTRODUCTION

Randomized controlled trials (RCTs) that conduct subgroup analyses have the potential to provide information on treatment decisions in specific groups of patients from heterogeneous populations. Although we understand several factors can modify the incidence of venous thromboembolism (VTE) and the benefit/risk ratio of anticoagulation treatments, further evidence is warranted to show the heterogeneity of treatment effects in different subgroups of patients.

AIMS

The primary purpose was to evaluate the appropriateness and interpretation of subgroup analysis performed on VTE RCTs reporting pharmacological interventions.

MATERIALS AND METHODS

A systematic review of RCTs published between January 2017 and January 2022 was conducted. Claims of subgroup effects were evaluated with predefined criteria. High-quality claims of subgroup effect were further analyzed and discussed.

RESULTS

Overall, 28 RCTs with a generally low bias risk were included. The purposes of the treatments included pharmacologic thromboprophylaxis (17), therapeutic dose anticoagulation (9), and catheter-directed pharmacologic thrombolysis (2). The evaluated subgroup analyses generally presented: a high number of subgroup analyses reported, a lack of prespecification, and a lack of usage of statistical tests for interaction. The authors reported 13 claims of subgroup effect; only two were considered potentially reliable to represent heterogeneity in the direction or magnitude of treatment effect.

CONCLUSIONS

Subgroup analyses of VTE RCTs reporting pharmacologic interventions are generally methodologically poor. Most claims of subgroup effect did not meet critical criteria and lacked credibility. Clinicians in this field may proceed with scepticism when assessing claims of subgroup effects due to methodological concerns and misleading interpretations.