Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials

Potential clinical impact of predictive modeling of heterogeneous treatment effects: scoping review of the impact of the PATH Statement

Background

The PATH Statement (2020) proposed predictive modeling for examining heterogeneity in treatment effects (HTE) in randomized clinical trials (RCTs). It distinguished risk modeling, which develops a multivariable model predicting individual baseline risk of study outcomes and examines treatment effects across risk strata, from effect modeling, which directly estimates individual treatment effects from models that include treatment, multiple patient characteristics and interactions of treatment with selected characteristics.

Purpose

To identify, describe and evaluate findings from reports that cite the Statement and present predictive modeling of HTE in RCTs.

Data Extraction

We identified reports using PubMed, Google Scholar, Web of Science, SCOPUS through July 5, 2024. Using double review with adjudication, we assessed consistency with Statement recommendations, credibility of HTE findings (applying criteria adapted from the Instrument to assess Credibility of Effect Modification Analyses (ICEMAN)), and clinical importance of credible findings.

Results

We identified 65 reports (presenting 31 risk models, 41 effect models). Contrary to Statement recommendations, only 25 of 48 studies with positive overall findings included a risk model; most effect models included multiple predictors with little prior evidence for HTE. Claims of HTE were noted in 23 risk modeling and 31 effect modeling reports, but risk modeling met credibility criteria more frequently (87 vs 32 percent). For effect models, external validation of HTE findings was critical in establishing credibility. Credible HTE from either approach was usually judged clinically important (24 of 30). In 19 reports from trials suggesting overall treatment benefits, modeling identified subgroups of 5-67% of patients predicted to experience no benefit or net treatment harm. In five that found no overall benefit, subgroups of 25-60% of patients were nevertheless predicted to benefit.

Conclusions

Multivariable predictive modeling identified credible, clinically important HTE in one third of 65 reports. Risk modeling found credible HTE more frequently; effect modeling analyses were usually exploratory, but external validation served to increase credibility.

Design of trials in lacunar stroke and cerebral small vessel disease: review and experience with the LACunar Intervention Trial 2 (LACI-2)

Cerebral small vessel disease (cSVD) causes lacunar stroke (25% of ischaemic strokes), haemorrhage, dementia, physical frailty, or is 'covert', but has no specific treatment. Uncertainties about the design of clinical trials in cSVD, which patients to include or outcomes to assess, may have delayed progress. Based on experience in recent cSVD trials, we reviewed ways to facilitate future trials in patients with cSVD.We assessed the literature and the LACunar Intervention Trial 2 (LACI-2) for data to inform choice of Participant, Intervention, Comparator, Outcome, including clinical versus intermediary endpoints, potential interventions, effect of outcome on missing data, methods to aid retention and reduce data loss. We modelled risk of missing outcomes by baseline prognostic variables in LACI-2 using binary logistic regression.Imaging versus clinical outcomes led to larger proportions of missing data. We present reasons for and against broad versus narrow entry criteria. We identified numerous repurposable drugs with relevant modes of action to test in various cSVD subtypes. Cognitive impairment is the most common clinical outcome after lacunar ischaemic stroke but was missing more frequently than dependency, quality of life or vascular events in LACI-2. Assessing cognitive status using Diagnostic and Statistical Manual for Mental Disorders Fifth Edition can use cognitive data from multiple sources and may help reduce data losses.Trials in patients with all cSVD subtypes are urgently needed and should use broad entry criteria and clinical outcomes and focus on ways to maximise collection of cognitive outcomes to avoid missing data.

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.

Overcoming combination fatigue: Addressing high-dimensional effect measure modification and interaction in clinical, biomedical, and epidemiologic research using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Growing interest in precision medicine, gene-environment interactions, health equity, expanding diversity in research, and the generalizability results, requires researchers to evaluate how the effects of treatments or exposures differ across numerous subgroups. Evaluating combination complexity, in the form of effect measure modification and interaction, is therefore a common study aim in the biomedical, clinical, and epidemiologic sciences. There is also substantial interest in expanding the combinations of factors analyzed to include complex treatment protocols (e.g., multiple study arms or factorial randomization), comorbid medical conditions or risk factors, and sociodemographic and other subgroup identifiers. However, expanding the number of subgroup category combinations creates combination fatigue problems, including concerns over small sample size, reduced power, multiple testing, spurious results, and design and analytic complexity. Creative new approaches for managing combination fatigue and evaluating high-dimensional effect measure modification and interaction are needed. Intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) has already attracted substantial interest in social epidemiology, and has been hailed as the new gold standard for investigating health inequities across complex intersections of social identity. Leveraging the inherent advantages of multilevel models, a more general multicategorical MAIHDA can be used to study statistical interactions and predict effects across high-dimensional combinations of conditions, with important advantages over alternative approaches. Though it has primarily been used thus far as an analytic approach, MAIHDA should also be used as a framework for study design. In this article, I introduce MAIHDA to the broader health sciences research community, discuss its advantages over conventional approaches, and provide an overview of potential applications in clinical, biomedical, and epidemiologic research.