RESUMEN
Spinocerebellar ataxias (SCA) are rare inherited neurodegenerative disorders characterized by a progressive impairment of gait, balance, limb coordination, and speech. There is currently no composite scale that includes multiple aspects of the SCA experience to assess disease progression and treatment effects. Applying the method of partial least squares (PLS) regression, we developed the Spinocerebellar Ataxia Composite Scale (SCACOMS) from two SCA natural history datasets (NCT01060371, NCT02440763). PLS regression selected items based on their ability to detect clinical decline, with optimized weights based on the item's degree of progression. Following model validation, SCACOMS was leveraged to examine disease progression and treatment effects in a 48-week SCA clinical trial cohort (NCT03701399). Items from the Clinical Global Impression-Global Improvement Scale (CGI-I), the Friedreich Ataxia Rating Scale (FARS) - functional stage, and the Modified Functional Scale for the Assessment and Rating of Ataxia (f-SARA) were objectively selected with weightings based on their sensitivity to clinical decline. The resulting SCACOMS exhibited improved sensitivity to disease progression and greater treatment effects (compared to the original scales from which they were derived) in a 48-week clinical trial of a novel therapeutic agent. The trial analyses also provided a SCACOMS-derived estimate of the temporal delay in SCA disease progression. SCACOMS is a useful composite measure, effectively capturing disease progression and highlighting treatment effects in patients with SCA. SCACOMS will be a powerful tool in future studies given its sensitivity to clinical decline and ability to detect a meaningful clinical impact of disease-modifying treatments.
RESUMEN
The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Phase 1 Schizophrenia trial compared the effectiveness of one typical and four atypical antipsychotic medications. Although trials such as CATIE present important opportunities for pharmacogenetics research, the very richness of the clinical data presents challenges for statistical interpretation, and in particular the risk that data mining will lead to false-positive discoveries. For this reason, it is both misleading and unhelpful to perpetuate the current practice of reporting association results for these trials one gene at a time, ignoring the fact that multiple gene-by-phenotype tests are being carried out on the same data set. On the other hand, suggestive associations in such trials may lead to new hypotheses that can be tested through both replication efforts and biological experimentation. The appropriate handling of these forms of data therefore requires dissemination of association statistics without undue emphasis on select findings. Here we attempt to illustrate this approach by presenting association statistics for 2769 polymorphisms in 118 candidate genes evaluated for 21 pharmacogenetic phenotypes. On current evidence it is impossible to know which of these associations may be real, although in total they form a valuable resource that is immediately available to the scientific community.
