Using the Reliable Change Index Statistic to Analyze Developmental Changes in Infants at Risk for Autism Spectrum Disorder

Longitudinal studies of the development of autism spectrum disorders (ASD) provide an understanding of which variables may be important predictors of an ASD. The objective of the current study is to apply the reliable change index (RCI) statistic to examine whether the Parent Observation of Early Markers Scale (POEMS) is sensitive to developmental change, and whether these changes can be quantified along a child’s developmental trajectory. Ninety-six children with older siblings with autism were followed from 1-36 months of age. Group-based RCI analysis confirms that the POEMS is capable of detecting significant changes within pre-defined diagnostic groups. Within-subject analysis suggests that ongoing monitoring of a child at-risk for an ASD requires interpretation of both significant intervals identified by the RCI statistic, as well as the presence of repeated high (i.e., >70) scores. This study provides preliminary evidence for a reasonably sensitive and specific means by which individual change can be clinically monitored via parent report.

Predicting the Risk of Lung Cancer in Never-smokers

Despite being considered a disease of smokers, approximately 10-15% of lung cancer cases occur in never-smokers. Lung cancer risk prediction models have demonstrated excellent ability to discriminate cases from non-cases, and have been shown to be more efficient at selecting individuals for future screening than current criteria. Existing models have primarily been developed in populations of smokers, thus there was a need to develop an accurate model in never-smokers. This study focused on developing and validating a model using never-smokers from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox regression analysis, with six-year follow-up, was used for model building. Predictors included: age, body mass index, education level, personal history of cancer, family history of lung cancer, previous chest X-ray, and secondhand smoke exposure. This model achieved fair discrimination (optimism corrected c-statistic = 0.6645) and good calibration. This represents an improvement on existing neversmoker models, but is not suitable for individual-level risk prediction.