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AbstractThe identification of good surrogate endpoints is a challenging endeavor. This may, at least partially, be attributable to the fact that most researchers have focused on the identification of a single surrogate endpoint. It is thus implicitly assumed that the treatment effect on the true endpoint (T) can be accurately predicted based on the treatment effect on one surrogate endpoint (S) only. Given the complex nature of many diseases and the different therapeutic pathways in which a treatment can impact T, this assumption may be too optimistic. For example, in oncology, the effect of a treatment often depends on both the treatment’s efficacy and its toxicity. In the present article, the meta-analytic framework of? is extended to the setting where multiple S are considered. To cope with potential model convergence issues that often arise in a meta-analytic framework, several simplified model fitting strategies are proposed. Further, simulation studies are conducted to evaluate the properties of the estimated surrogacy metrics, and the new methodology is applied on a case study in schizophrenia. An online Appendix that details how the analyses can be conducted in practice (using the R package Surrogate) is also provided. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to evaluate the psychometric properties of a shortened version of the Raven Standard Progressive Matrices (SPM) under an item response theory framework (the one- and two-parameter logistic models). The shortened Raven SPM was administered to N = 453 cognitively healthy adults aged between 24 and 83 years. The IQ point estimates that were obtained under the one- and two-parameter logistic models were very similar (r = 0.97), but the two-parameter logistic-based test version had a higher measurement precision. The results showed that older age and being female were associated with a lower Raven SPM test performance. A user-friendly computer program was provided to facilitate the scoring and norming of the shortened Raven SPM under the different frameworks. (Contains 3 tables and 4 figures.)

The Memory Compensation Questionnaire (MCQ) is a psychometrically sound instrument that assesses the variety and extent to which an individual compensates for actual or perceived memory losses. Until now, only an English version of the MCQ has been psychometrically evaluated. The aim of the present study was to establish a Dutch version of the MCQ and evaluate its psychometric properties. The MCQ data of N = 556 cognitively healthy adults (61.8% females) aged between 50.1 and 95.3 years (M = 73.9 years, SD = 8.0) were analyzed. The results showed that the factor structure of the Dutch version of the MCQ corresponded well with that of the English version of the MCQ. The reliabilities of the scales of the Dutch version of the MCQ were all high (all Cronbach's [alpha]s [greater than or equal to] 0.77). Demographic variables (especially age and gender) affected most of the MCQ scale scores. Regression-based normative data that take these demographic influences into account were established, and a user-friendly computer program was provided to facilitate the scoring and norming of the MCQ. (Contains 9 tables.)

The Concept Shifting Test (CST) is a newly developed Trail Making Type test that measures concept shifting and executive functioning. The purpose of the present study was to determine whether CST performance was affected by age, gender, educational level, or handedness and to establish the normal range of test performance. The CST was administered to a large sample (N = 1,794) of cognitively intact adults (age range 24 to 81 years). The results showed that CST performance was superior in women, decreased with age, and increased with level of education. Handedness did not affect CST performance. Normative data that are corrected for the influences of these demographical variables were established with a regression-based normative approach.

The Stroop Color-Word Test was administered to 1,856 cognitively screened, healthy Dutch-speaking participants aged 24 to 81 years. The effects of age, gender, and education on Stroop test performance were investigated to adequately stratify the normative data. The results showed that especially the speed-dependent Stroop scores (time to complete a subtest), rather than the accuracy measures (the errors made per Stroop subtask), were profoundly affected by the demographic variables. In addition to the main effects of the demographic variables, an Age x Low Level of Education interaction was found for the Error III and the Stroop Interference scores. This suggests that executive function, as measured by the Stroop test, declines with age and that the decline is more pronounced in people with a low level of education. This is consistent with the reserve hypothesis of brain aging (i.e., that education generates reserve capacity against the damaging effects of aging on brain functions). Normative Stroop data were established using both a regression-based and traditional approach, and the appropriateness of both methods for generating normative data is discussed. (Contains 10 tables, 2 figures, and 2 notes.)

Pharmaceutical products intended for parenteral use must be free from pyrogenic (fever-inducing) contamination. Pyrogens comprise endotoxins from Gram-negative bacteria and non-endotoxin pyrogens from Gram-positive bacteria, viruses, and fungi. The longstanding compendial test for pyrogens is the rabbit pyrogen test, but in 2010 the monocyte activation test (MAT) for pyrogenic and pro-inflammatory contaminants was introduced into the European Pharmacopoeia (Ph. Eur.) as a non-animal replacement for the rabbit pyrogen test. The present study describes the first product-specific Good Manufacturing Practice validation of Ph. Eur. MAT, Quantitative Test, Method A for the testing of three therapeutic monoclonal antibodies. The study used the MAT version with cryo-preserved peripheral blood mononuclear cells and interleukin-6 as the readout. Much of the data presented here for one of the antibodies was included in a successful product license application to the European Medicines Agency. [ABSTRACT FROM AUTHOR]

The meta-analytic approach has become the gold-standard methodology for the evaluation of surrogate endpoints and several implementations are currently available in SAS and R. The methodology is based on hierarchical models that are numerically demanding and, when the amount of data is limited, maximum likelihood algorithms may not converge or may converge to an ill-conditioned maximum such as a boundary solution. This may produce misleading conclusions and have negative implications for the evaluation of new drugs. In the present work, we explore the use of two distinct functions in R (lme and lmer) and the MIXED procedure in SAS to assess the validity of putative surrogate endpoints in the meta-analytic framework, via simulations and the analysis of a real case study. We describe some problems found with the lmer function in R that led to a poorer performance as compared with the lme function and MIXED procedure. [ABSTRACT FROM AUTHOR]

Given the heterogeneous responses to therapy and the high cost of treatments, there is an increasing interest in identifying pretreatment predictors of therapeutic effect. Clearly, the success of such an endeavor will depend on the amount of information that the patient‐specific variables convey about the individual causal treatment effect on the response of interest. In the present work, using causal inference and information theory, a strategy is proposed to evaluate individual predictive factors for cancer immunotherapy efficacy. In a first step, the methodology proposes a causal inference model to describe the joint distribution of the pretreatment predictors and the individual causal treatment effect. Further, in a second step, the so‐called predictive causal information (PCI), a metric that quantifies the amount of information the pretreatment predictors convey on the individual causal treatment effects, is introduced and its properties are studied. The methodology is applied to identify predictors of therapeutic success for a therapeutic vaccine in advanced lung cancer. A user‐friendly R library EffectTreat is provided to carry out the necessary calculations. [ABSTRACT FROM AUTHOR]