A Simulation Study Comparing Different Statistical Approaches for the Identification of Predictive Biomarkers

Identification of target groups and individuals for adherence interventions using tree-based prediction models

Background: In chronically ill patients, medication adherence during implementation can be crucial for treatment success and can decrease health costs. In some populations, regression models do not show this relationship. We aim to estimate subgroup-specific and personalized effects to identify target groups for interventions.

Methods: We defined three cohorts of patients with type 1 diabetes (n = 12,713), type 2 diabetes (n = 85,162) and hyperlipidemia (n = 117,485) from German claims data between 2012 and 2015. We estimated the association of adherence during implementation in the first year (proportion of days covered) and mean total costs in the three following years, controlled for sex, age, Charlson’s Comorbidity Index, initial total costs, severity of the disease and surrogates for health behavior. We fitted three different types of models on training data: 1) linear regression models for the overall conditional associations between adherence and costs, 2) model-based trees to identify subgroups of patients with heterogeneous adherence effects, and 3) model-based random forests to estimate personalized adherence effects. To assess the performance of the latter, we conditionally re-estimated the personalized effects using test data, the fixed structure of the forests, and fixed effect estimates of the remaining covariates.

Results: 1) our simple linear regression model estimated a positive adherence effect, that is an increase in total costs of 10.73 Euro per PDC-point and year for diabetes type 1, 3.92 Euro for diabetes type 2 and 1.92 Euro for hyperlipidemia (all p ≤ 0.001). 2) The model-based tree detected subgroups with negative estimated adherence effects for diabetes type 2 (-1.69 Euro, 24.4% of cohort) and hyperlipidemia (-0.11 Euro, 36.1% and -5.50 Euro, 5.3%). 3) Our model-based random forest estimated personalized adherence effects with a significant proportion (4.2%–24.1%) of negative effects (up to -8.31 Euro). The precision of these estimates was high for diabetes type 2 and hyperlipidemia patients.

Discussion: Our approach shows that tree-based models can identify patients with different adherence effects and the precision of personalized effects is measurable. Identified patients can form target groups for adherence-promotion interventions. The method can also be applied to other outcomes such as hospitalization risk to maximize positive health effects of an intervention.

Inflammatory Tumor Microenvironment in Cranial Meningiomas: Clinical Implications and Intraindividual Reproducibility

The MIB-1 index was demonstrated to be significantly correlated to meningioma recurrence. However, to date, the relationship of the intraindividual course of the MIB-1 index and the growth fraction, respectively, to clinical tumor recurrence has not been demonstrated in cranial WHO grade 1 and 2 meningiomas. In the present paper, we compare the MIB-1 indices of 16 solely surgically treated primary meningiomas and their recurrent tumors regarding the course of the MIB-1 indices, time to recurrence, reproducibility and factors influencing the intraindividual MIB-1 indices. Regression analyses revealed (1) a strong intra-lab reproducibility (r = 0.88) of the MIB-1 index at the second versus the first operation, corresponding to a constant intrinsic growth activity of an individual meningioma, (2) a significant inverse correlation of both primary (r = −0.51) and secondary (r = −0.70) MIB-1 indices to time to recurrence, and (3) male sex, low plasma fibrinogen and diffuse CD68⁺ macrophage infiltrates contribute to an increase in the MIB-1 index. A strong intraindividual reproducibility of the MIB-1 index and a direct relationship of the MIB-1 index to the time to recurrence were observed. Individual MIB-1 indices might be used for tailored follow-up imaging intervals. Further research on the role of macrophages and inflammatory burden in the regrowth potential of meningiomas are needed.

Proliferative Potential, and Inflammatory Tumor Microenvironment in Meningioma Correlate with Neurological Function at Presentation and Anatomical Location-From Convexity to Skull Base and Spine

Emerging evidence emphasizes the prognostic importance of meningioma location. The present investigation evaluates whether progression-free survival (PFS), proliferative potential, World Health Organization (WHO) grades, and inflammatory burden differ between anatomical locations (skull base, non-skull base, and spinal) meningiomas. Five-hundred-forty-one patients underwent Simpson grade I or II resection for WHO grade 1 or 2 meningiomas. Univariable analysis revealed that spinal meningioma patients are significantly older, had a worse baseline Karnofsky Performance Status (KPS), higher acute-phase protein levels, lower incidence of WHO grade 2, lower mitotic counts, lower MIB-1 index, and less CD68+ macrophage infiltrates. Multivariable analysis identified WHO grade 2 (OR: 2.1, 95% CI: 1.1-3.7, p = 0.02) and cranial location (OR: 3.0, 95% CI: 1.8-4.9, p = 0.001) as independent predictors of diffuse CD68+ macrophage infiltrates. The mean PFS in cranial meningiomas was 115.9 months (95% CI: 107.5-124.3), compared to 162.2 months (95% CI: 150.5-174.0; log-rank test: p = 0.02) in spinal meningiomas. Multivariable Cox regression analysis revealed cranial location as an independent predictor (HR: 4.7, 95% CI: 1.0-21.3, p = 0.04) of shortened PFS. Increased MIB-1 indices ≥5% were significantly associated with location-specific deficits at presentation, such as decreased vision and seizure burden. Spinal meningiomas have a significantly longer PFS time and differ from the cranial meningiomas regarding MIB-1 index and density of tumor-associated macrophages.

Ticagrelor or Prasugrel in Patients With Acute Coronary Syndrome in Relation to Estimated Glomerular Filtration Rate

OBJECTIVES

The aim of this study was to assess the safety and efficacy of ticagrelor versus prasugrel for patients with acute coronary syndrome (ACS) according to their estimated glomerular filtration rates (eGFRs).

BACKGROUND

The outcomes of ticagrelor versus prasugrel in patients with ACS according to eGFR have not been defined.

METHODS

Patients (n = 4,012) were categorized into 3 groups: low eGFR (<60 mL/min/1.73 m²), intermediate eGFR (≥60 and <90 mL/min/1.73 m²), and high eGFR (≥90 mL/min/1.73 m²). The primary endpoint was a composite of all-cause death, myocardial infarction, and stroke; the secondary safety endpoint was Bleeding Academic Research Consortium types 3 to 5 bleeding, both at 1 year.

RESULTS

Patients with low eGFRs had a higher risk for the primary endpoint compared with patients with intermediate eGFRs (adjusted HR: 1.89; 95% CI: 1.46-2.46]) and those with high eGFRs (adjusted HR: 2.33; 95% CI: 1.57-3.46). A risk excess for low eGFR was also observed for bleeding (adjusted HR: 1.55 [95% CI: 1.12-2.13] vs intermediate eGFR; adjusted HR: 1.59 [95% CI: 1.01-2.50] vs high eGFR). However, eGFR did not affect the relative efficacy and safety of ticagrelor versus prasugrel. In patients with low eGFR, the primary endpoint occurred in 20.5% with ticagrelor and in 14.7% with prasugrel (HR: 1.47; 95% CI: 1.04-2.08; P = 0.029); there was no significant difference in bleeding.

CONCLUSIONS

These results show that among patients with ACS, reduction of eGFR is associated with increased risk for ischemic and bleeding events but has no significant impact on the relative efficacy and safety of ticagrelor versus prasugrel. (Prospective, Randomized Trial of Ticagrelor Versus Prasugrel in Patients With Acute Coronary Syndrome [ISAR-REACT 5]; NCT01944800).

A systematic review of subgroup analyses in randomised clinical trials in cardiovascular disease

Background:

Subgroup analyses are frequently used to assess heterogeneity of treatment effects in randomised clinical trials. Inconsistent, improper and incomplete implementation, reporting and interpretation have been identified as ongoing challenges. Further, subgroup analyses were frequently criticised because of unreliable or potentially misleading results. More recently, recommendations and guidelines have been provided to improve the reporting of data in this regard.

Methods:

This systematic review was based on a literature search within the digital archives of three selected medical journals, The New England Journal of Medicine, The Lancet and Circulation. We reviewed articles of randomised clinical trials in the domain of cardiovascular disease which were published in 2015 and 2016. We screened and evaluated the selected articles for the mode of implementation and reporting of subgroup analyses.

Results:

We were able to identify a total of 130 eligible publications of randomised clinical trials. In 89/130 (68%) articles, results of at least one subgroup analysis were presented. This was dependent on the considered journal (p < 0.001), the number of included patients (p < 0.001) and the lack of statistical significance of a trial’s primary analysis (p < 0.001). The number of reported subgroup analyses ranged from 1 to 101 (median = 13). We were able to comprehend the specification time of reported subgroup analyses for 71/89 (80%) articles, with 55/89 (62%) articles presenting exclusively pre-specified analyses. This information was not always traceable on the basis of provided trial protocols and often did not include the pre-definition of cut-off values for the categorization of subgroups. The use of interaction tests was reported in 84/89 (94%) articles, with 36/89 (40%) articles reporting heterogeneity of the treatment effect for at least one primary or secondary trial outcome. Subgroup analyses were reported more frequently for larger randomised clinical trials, and if primary analyses did not reach statistical significance. Information about the implementation of subgroup analyses was reported most consistently for articles from The New England Journal of Medicine, since it was also traceable on the basis of provided trial protocols. We were able to comprehend whether subgroup analyses were pre-specified in a majority of the reviewed publications. Even though results of multiple subgroup analyses were reported for most published trials, a corresponding adjustment for multiple testing was rarely considered.

Conclusion:

Compared to previous reviews in this context, we observed improvements in the reporting of subgroup analyses of cardiovascular randomised clinical trials. Nonetheless, critical shortcomings, such as inconsistent reporting of the implementation and insufficient pre-specification, persist.