Modeling the Adenoma and Serrated Pathway to Colorectal CAncer (ASCCA)

Several colorectal cancer (CRC) screening models have been developed describing the progression of adenomas to CRC. Currently, there is increasing evidence that serrated lesions can also develop into CRC. It is not clear whether screening tests have the same test characteristics for serrated lesions as for adenomas, but lower sensitivities have been suggested. Models that ignore this type of colorectal lesions may provide overly optimistic predictions of the screen‐induced reduction in CRC incidence. To address this issue, we have developed the Adenoma and Serrated pathway to Colorectal CAncer (ASCCA) model that includes the adenoma‐carcinoma pathway and the serrated pathway to CRC as well as characteristics of colorectal lesions. The model structure and the calibration procedure are described in detail. Calibration resulted in 19 parameter sets for the adenoma‐carcinoma pathway and 13 for the serrated pathway that match the age‐ and sex‐specific adenoma and serrated lesion prevalence in the COlonoscopy versus COlonography Screening (COCOS) trial, Dutch CRC incidence and mortality rates, and a number of other intermediate outcomes concerning characteristics of colorectal lesions. As an example, we simulated outcomes for a biennial fecal immunochemical test screening program and a hypothetical one‐time colonoscopy screening program. Inclusion of the serrated pathway influenced the predicted effectiveness of screening when serrated lesions are associated with lower screening test sensitivity or when they are not removed. To our knowledge, this is the first model that explicitly includes the serrated pathway and characteristics of colorectal lesions. It is suitable for the evaluation of the (cost)effectiveness of potential screening strategies for CRC.     

Popularity Contagion Among Adolescents

This study aimed to support the theory of popularity contagion, which posits that popularity spreads among friends spontaneously and regardless of behavioral changes. Peer nominations of status and behavior were collected annually between 6th and 12th grades from a total of 1062 adolescents. Longitudinal hypotheses were mostly supported using path analyses, showing (1) that individual popularity could be predicted by friends’ popularity levels over time, even when controlling for stability of individual popularity; (2) that this prediction was not accounted for by behavioral contagion of aggressive or prosocial behaviors; and (3) that individual social preference generally could not be predicted by friends’ preference levels over time. Implications, limitations, and directions for future research are discussed.     

Effects of Network Approval on Accounts of Commitment Trajectories in Intraracial and Interracial Relationships

The current study used dyadic data to investigate the impact of relationship type and social support on the retrospective accounts of commitment trajectories of romantic relationships. Past research suggests that social support is a positive contributor to relationship stability and commitment, which may be especially true for partners in interracial relationships who face broader societal opposition than intraracial couples. Using multilevel modeling, we investigated the effects of sex, relationship type, and social support on reports of commitment. Results showed differences in trajectories of commitment based on couple type (interracial vs. intraracial) for both men and women. Social support was found to have an especially strong impact for women in interracial relationships compared with women in intraracial relationships, but there was no differential impact among men.     

Experienced Well-Being and Labor Market Status: The Role of Pleasure and Meaning

Social Indicators Research - This paper examines the experienced well-being of employed and unemployed workers. We use the survey-adapted Day Reconstruction Method of the Innovation Sample of the...     

Semiparametric temporal process regression of survival-out-of-hospital

Lifetime Data Analysis - The recurrent/terminal event data structure has undergone considerable methodological development in the last 10–15 years. An example of the data structure that has...     

Learned Labels Shape Pre‐speech Infants’ Object Representations

Infants rapidly learn both linguistic and nonlinguistic representations of their environment and begin to link these from around 6 months. While there is an increasing body of evidence for the effect of labels heard in‐task on infants’ online processing, whether infants’ learned linguistic representations shape learned nonlinguistic representations is unclear. In this study 10‐month‐old infants were trained over the course of a week with two 3D objects, one labeled, and one unlabeled. Infants then took part in a looking time task in which 2D images of the objects were presented individually in a silent familiarization phase, followed by a preferential looking trial. During the critical familiarization phase, infants looked for longer at the previously labeled stimulus than the unlabeled stimulus, suggesting that learning a label for an object had shaped infants’ representations as indexed by looking times. We interpret these results in terms of label activation and novelty response accounts and discuss implications for our understanding of early representational development.     

A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.