Population adjusted-indirect comparisons in health technology assessment: A methodological systematic review.

In health technology assessment (HTA), population-adjusted indirect comparisons (PAICs) are increasingly considered to adjust for the difference in the target population between studies. We aim to assess the conduct and reporting of PAICs in recent HTA practice, by performing, a methodological systematic review of studies implementing PAICs from PubMed, EMBASE Classic, Embase/Ovid Medline All, and Cochrane databases from January 1, 2010 to Feb 13, 2023. Four independent researchers screened the titles, abstracts, and full-texts of the identified records, then extracted data on methodological and reporting characteristics of 106 eligible articles. Most PAIC analyses (96.9%, n = 157) were conducted by (or received funding from) pharmaceutical companies. Prior to adjustment, 44.5% of analyses (n = 72) (partially) aligned the eligibility criteria of different studies to enhance the similarity of their target populations. In 37.0% of analyses (n = 60), the clinical and methodological heterogeneity across studies were extensively assessed. In 9.3% of analyses (n = 15), the quality (or bias) of individual studies was evaluated. Among 18 analyses using methods that required an outcome model specification, results of the model fitting procedure were adequately reported in three analyses (16.7%). These findings suggest that the conduct and reporting of PAICs are remarkably heterogeneous and suboptimal in current practice. More recommendations and guidelines on PAICs are thus warranted to enhance the quality of these analyses in the future.

Group sequential testing under instrumented difference-in-differences approach.

Unmeasured confounding is a major obstacle to reliable causal inference based on observational studies. Instrumented difference-in-differences (iDiD), a novel idea connecting instrumental variable and standard DiD, ameliorates the above issue by explicitly leveraging exogenous randomness in an exposure trend. In this article, we utilize the above idea of iDiD, and propose a novel group sequential testing method that provides valid inference even in the presence of unmeasured confounders. At each time point, we estimate the average or conditional average treatment effect under iDiD setting using the data accumulated up to that time point, and test the significance of the treatment effect. We derive the joint distribution of the test statistics under the null using the asymptotic properties of M-estimation, and the group sequential boundaries are obtained using the α $$ \alpha $$ -spending functions. The performance of our proposed approach is evaluated on both synthetic data and Clinformatics Data Mart Database (OptumInsight, Eden Prairie, MN) to examine the association between rofecoxib and acute myocardial infarction, and our method detects significant adverse effect of rofecoxib much earlier than the time when it was finally withdrawn from the market.

The role of loneliness on hearing ability and dementia: A novel mediation approach.

BACKGROUND: To determine the potential mediating role of loneliness in the relationship between hearing ability and dementia. METHODS: Design: Longitudinal observational study. SETTING: English Longitudinal Study of Ageing (ELSA). PARTICIPANTS: Individuals aged 50 and older (N = 4232). MEASUREMENTS: Self-reported hearing ability and loneliness were assessed from Wave 2 (2004-2005) to Wave 7 (2014-2015) of ELSA. Dementia cases were ascertained via self- or carer-report or dementia medication at these waves. The medeff command in Stata version 17 was used to do cross-section mediation analysis between hearing ability, loneliness, and dementia (Waves 3-7). Path-specific effects proportional (cause-specific) hazard models were then used to investigate longitudinal mediation (Waves 2-7). RESULTS: In cross-sectional analyses in Wave 7 alone, loneliness only mediated 5.4% of the total effects of limited hearing on dementia (indirect effects = increased risk of 0.06%; 95% CI: 0.002%-0.15%) under limited hearing and 0.04% (95% CI: 0.001%-0.11%) under normal hearing). In longitudinal analyses, there was no statistical evidence of a mediating role for loneliness in explaining the relationship between hearing ability and time-to-dementia (indirect effect estimate hazard ratio = 1.01 (95% CI: 0.99-1.05). CONCLUSION: In this community-dwelling sample of English adults, there is a lack of evidence that loneliness mediates the relationship between hearing ability and dementia in both cross-sectional and longitudinal analyses. However, as the number of dementia cases in this cohort was low, replication in other cohorts with larger sample sizes is required to confirm the absence of a mediated effect via loneliness.

A cautionary note on the use of G-computation in population adjustment.

In a recent issue of the Journal; Remiro-Azócar et al. introduce a new method to adjust for population difference between two trials; when the individual patient data (IPD) are only accessible for one study. The proposed method generates the covariate data for the trial without IPD; then using a G-computation approach to transport information about the treatment effect from the other study with IPD to this trial. The authors advocate the use of G-computation over matching-adjusted indirect comparison because (i) the former allows for "useful extrapolation" when there is poor case-mix overlap between populations; and (ii) nonparametric; data-adaptive methods can be used to reduce the risk of (outcome) model misspecification. In this commentary; we provide a different perspective from these arguments. Despite certain disagreements; we believe that the proposed data generation approaches can open new and interesting research directions for population adjustment methodology in the future.

Longitudinal mediation analysis of time-to-event endpoints in the presence of competing risks.

This proposal is motivated by an analysis of the English Longitudinal Study of Ageing (ELSA), which aims to investigate the role of loneliness in explaining the negative impact of hearing loss on dementia. The methodological challenges that complicate this mediation analysis include the use of a time-to-event endpoint subject to competing risks, as well as the presence of feedback relationships between the mediator and confounders that are both repeatedly measured over time. To account for these challenges, we introduce path-specific effect proportional (cause-specific) hazard models. These extend marginal structural proportional (cause-specific) hazard models to enable effect decomposition on either the cause-specific hazard ratio scale or the cumulative incidence function scale. We show that under certain ignorability assumptions, the path-specific direct and indirect effects indexing this model are identifiable from the observed data. We next propose an inverse probability weighting approach to estimate these effects. On the ELSA data, this approach reveals little evidence that the total effect of hearing loss on dementia is mediated through the feeling of loneliness, with a non-statistically significant indirect effect equal to 1.01 (hazard ratio (HR) scale; 95% confidence interval (CI) 0.99 to 1.05).

How do psychologically based interventions for chronic musculoskeletal pain work? A systematic review and meta-analysis of specific moderators and mediators of treatment.

Psychologically based interventions aim to improve pain-related functioning by targeting pain-related fears, cognitions and behaviors. Mediation and moderation analyses permit further examination of the effect of treatment on an outcome. This systematic review and meta-analysis aims to synthetize the evidence of specific mediators and moderators (i.e., treatment targets) of psychologically based treatment effects on pain and disability. A total of 28 mediation and 11 moderation analyses were included. Thirteen mediation studies were included in a meta-analysis, and the rest was narratively synthetized. Reductions in pain-related fear (indirect effect [IE]: -0.07; 95% confidence interval [CI]: -0.11, -0.04) and catastrophizing (IE: -0.07; 95%CI: -0.14, -0.00), as well as increases in self-efficacy (IE: -0.07; 95%CI: -0.11, -0.04), mediated effects of cognitive behavioral therapy on disability but not on pain intensity, when compared to control treatments. Enhancing pain acceptance (IE: -0.17; 95%CI: -0.31, -0.03) and psychological flexibility (IE: -0.30; 95%CI: -0.41, -0.18) mediated acceptance and commitment therapy effects on disability. The narrative synthesis showed conflicting evidence, which did not support a robust moderated effect for any of the examined constructs. Overall, the methodological quality regarding mediation was low, and some key pitfalls are highlighted alongside recommendations to provide a platform for future research.

Challenges in Systematic Reviews and Meta-Analyses of Mediation Analyses.

Systematic reviews and meta-analyses of mediation studies are increasingly being implemented in practice. Nonetheless, the methodology for conducting such review and analysis is still in a development phase, with much room for improvement. In this paper, we highlight and discuss challenges that investigators face in systematic reviews and meta-analyses of mediation studies and propose ways of accommodating these in practice.

Quality assessment practice in systematic reviews of mediation studies: results from an overview of systematic reviews.

OBJECTIVE: To describe the bias assessment practice in recently published systematic reviews of mediation studies and to evaluate the quality of different bias assessment tools for mediation analysis proposed in the literature. METHOD: We conducted an overview of systematic reviews by searching MEDLINE (OvidSP), PsycINFO (OvidSP), Cochrane Database of Systematic Reviews (OvidSP), and PubMed databases for systematic reviews of mediation studies published from 2007 to 2020. Two reviewers independently screened the title, abstracts, and full texts of the identified reports and extracted the data. The publications of all mediation-specific quality assessment tools used in these reviews were also identified for the evaluation of the tools' development and validation. RESULT: Among 103 eligible reviews, 24 (23%) reviews did not assess the risk of bias of eligible studies, and 48 (47%) assessed risk of bias using a tool that was not specifically designed to evaluate mediation analysis. 31 (30.1%) reviews assessed the risk of mediation-specific biases, either narratively or by using specific tools for mediation studies. However, none of these tools were consensus-based, rigorously developed or validated. CONCLUSION: The quality assessment practice in recently published systematic reviews of mediation studies is suboptimal. To improve the quality and consistency of risk of bias assessments for mediation studies, a consensus-based bias assessment tool is needed.

The conduct and reporting of mediation analysis in recently published randomized controlled trials: results from a methodological systematic review.

OBJECTIVES: To describe the methodological characteristics of mediation analyses (MAs) reported in recent randomized controlled trials (RCTs) and to propose recommendations on the planning, conduct, and reporting of MAs in practice. STUDY DESIGN AND SETTING: We conducted a systematic review by searching MEDLINE (January 1, 2017, to December 1, 2018) for all reports of RCTs or secondary analyses of previously published RCTs that reported a MA. Two reviewers independently screened the title, abstracts, and full texts of the identified reports and extracted the data from the 98 eligible studies. RESULTS: MAs were nearly always (96%) based on a traditional mediation approach. Most studies did not report a sample size calculation for the MA (96%) or assess potential treatment-by-mediator interactions (96%). In 53% of studies, mediators and outcomes were simultaneously measured. In 57% of studies, mediator-mediator and mediator-outcome confounders were adjusted for in the analysis, although adjustment was often limited to few potential confounders. About 30% of studies discussed the assumptions underlying the MA. CONCLUSION: The conduct and reporting of MAs remained quite heterogeneous in practice. Future MAs could benefit from a consensus-based planning, conduct, and reporting guideline for MA.

A novel approach for identifying and addressing case-mix heterogeneity in individual participant data meta-analysis.

Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons.

Impact of Biomarker-based Design Strategies on the Risk of False-Positive Findings in Targeted Therapy Evaluation.

PURPOSE: When there is more than one potentially predictive biomarker for a new drug, the drug is often evaluated in different subpopulations defined by different biomarkers. We aim to (i) estimate the risk of false-positive findings with this approach and (ii) evaluate the cross-validated adaptive signature design (CVASD) as a potential alternative. EXPERIMENTAL DESIGN: By using numerically simulated data, we compare the current approach and the CVASD across different settings and scenarios. We consider three strategies for CVASD. The first two CVASD strategies are different in terms of the partitioning of the overall significance level (between the population test and the subgroup test). In the third CVASD strategy, the order of the two tests is reversed, that is, the population test is realized when the prioritized subgroup test is not statistically significant. RESULTS: The current approach results in a high risk of false-positive findings, whereas this risk is close to the nominal level of 5% once applying the CVASD, regardless of the strategy. When the treatment is equally effective to all patients, only the CVASD strategies could specify correctly the absence of a sensitive subgroup. When the treatment is only effective for some sensitive responders, the third CVASD strategy stands out by its ability to correctly identify the predictive biomarker(s). CONCLUSIONS: The drug-biomarker coevaluation based on a series of independent enrichment trials can result in a high risk of false-positive findings. CVASD with some appropriate adjustments can be a good alternative to overcome this multiplicity issue.