Predicting Treatment Responses in Patients With Osteoarthritis: Results From Two Phase III Tanezumab Randomized Clinical Trials

Test-retest and interrater reliability of experimental within-subject variability of pain reports as assessed by the focused analgesia selection test

Introduction

Within-subject variability (WSV) of pain intensity reports has been shown to predict the placebo response. The focused analgesia selection test (FAST), which allows to experimentally assess WSV of pain reports, has been used as a screening tool to identify participants who are likely to have a strong placebo response in drug-development clinical trials. Yet, the reliability of FAST has not been reported.

Objectives

To assess test-retest and interrater reliability of the FAST outcomes. To mimic pharma-sponsored clinical trials, we enlisted inexperienced assessors who underwent limited training.

Methods

Healthy volunteers performed the FAST twice within a week and were randomly assigned to either the test-retest group or the interrater group. T-tests, partial Pearson correlations, intraclass correlations (ICC), and Bland-Altman plots were generated to assess FAST outcomes' reliability.

Results

Sixty-three participants completed the study and were assigned to the test-retest (N = 33) or interrater (N = 30) arms. No statistically significant differences in the FAST outcomes were detected between the 2 sessions, except for the FAST covariance (FAST CoV) in the interrater assessment (P = 0.009). Test-retest reliabilities of the FAST-main outcomes were r = 0.461, ICC = 0.385 for the FAST R 2 and r = 0.605, ICC = 0.539 for the FAST ICC and in the interrater cohort, they were FAST R 2: r = 0.321, ICC = 0.337 and FAST ICC: r = 0.355, ICC = 0.330.

Conclusion

Using inexperienced assessors, the FAST outcomes test-retest ranged from moderate to strong, whereas the interrater reliability ranged from weak to poor. These results highlight the importance of adequately training study staff members before using this tool in multicentre clinical trials.

Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses

The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.

Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain

Drug treatments for pain often do not outperform placebo, and a better understanding of placebo mechanisms is needed to improve treatment development and clinical practice. In a large-scale fMRI study (N = 392) with pre-registered analyses, we tested whether placebo analgesic treatment modulates nociceptive processes, and whether its effects generalize from conditioned to unconditioned pain modalities. Placebo treatment caused robust analgesia in conditioned thermal pain that generalized to unconditioned mechanical pain. However, placebo did not decrease pain-related fMRI activity in brain measures linked to nociceptive pain, including the Neurologic Pain Signature (NPS) and spinothalamic pathway regions, with strong support for null effects in Bayes Factor analyses. In addition, surprisingly, placebo increased activity in some spinothalamic regions for unconditioned mechanical pain. In contrast, placebo reduced activity in a neuromarker associated with higher-level contributions to pain, the Stimulus Intensity Independent Pain Signature (SIIPS), and affected activity in brain regions related to motivation and value, in both pain modalities. Individual differences in behavioral analgesia were correlated with neural changes in both modalities. Our results indicate that cognitive and affective processes primarily drive placebo analgesia, and show the potential of neuromarkers for separating treatment influences on nociception from influences on evaluative processes.

Impact of the PATH Statement on Analysis and Reporting of Heterogeneity of Treatment Effect in Clinical Trials: A Scoping Review

Background

The Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement provides guidance for using predictive modeling to identify differences (i.e., heterogeneity) in treatment effects (benefits and harms) among participants in randomized clinical trials (RCTs). It distinguished risk modeling, which uses a multivariable model to predict risk of trial outcome(s) and then examines treatment effects within strata of predicted risk, from effect modeling, which predicts trial outcomes using models that include treatment, individual participant characteristics and interactions of treatment with selected characteristics.

Purpose

To describe studies of heterogeneous treatment effects (HTE) that use predictive modeling in RCT data and cite the PATH Statement.

Data Sources

The Cited By functions in PubMed, Google Scholar, Web of Science and SCOPUS databases (Jan 7, 2020 - June 5, 2023).

Study Selection

42 reports presenting 45 predictive models.

Data Extraction

Double review with adjudication to identify risk and effect modeling and examine consistency with Statement consensus statements. Credibility of HTE findings was assessed using criteria adapted from the Instrument to assess Credibility of Effect Modification Analyses (ICEMAN). Clinical importance of credible HTE findings was also assessed.

Data Synthesis

The numbers of reports, especially risk modeling reports, increased year-on-year. Consistency with consensus statements was high, except for two: only 15 of 32 studies with positive overall findings included a risk model; and most effect models explored many candidate covariates with little prior evidence for effect modification. Risk modeling was more likely than effect modeling to identify both credible HTE (14/19 vs 5/26) and clinically important HTE (10/19 vs 4/26).

Limitations

Risk of reviewer bias: reviewers assessing credibility and clinical importance were not blinded to adherence to PATH recommendations.

Conclusions

The PATH Statement appears to be influencing research practice. Risk modeling often uncovered clinically important HTE; effect modeling was more often exploratory.

Covariate Adjustment in Chronic Pain Trials: An Oft-Missed Opportunity

Self-reported pain intensity, frequently used as an outcome in randomized clinical trials (RCTs) of chronic pain, is often highly variable and could be associated with multiple baseline factors. Thus, the assay sensitivity of pain trials (ie, the ability of the trial to detect a true treatment effect) could be improved by including prespecified baseline factors in the primary statistical model. The objective of this focus article was to characterize the baseline factors included in statistical analyses of chronic pain RCTs. Seventy-three RCTs published between 2016 and 2021 that investigated interventions for chronic pain were included. The majority of trials identified a single primary analysis (72.6%; n = 53). Of these, 60.4% (n = 32) included one or more covariates in the primary statistical model, most commonly baseline value of the primary outcome, study site, sex, and age. Only one of the trials reported information regarding associations between covariates and outcomes (ie, information that could inform prioritization of covariates for prespecification in future analyses). These findings demonstrate inconsistent use of covariates in the statistical models in chronic pain clinical trials. Prespecified adjustments for baseline covariates that could increase precision and assay sensitivity should be considered in future clinical trials of chronic pain treatments. PERSPECTIVE: This review demonstrates inconsistent inclusion and potential underutilization of covariate adjustment in analyses of chronic pain RCTs. This article highlights areas for possible improvement in design and reporting related to covariate adjustment to improve efficiency in future RCTs.