@article {247,
	title = {The relation between the age at diagnosis of problem behaviors related to aggression and distal outcomes in Swedish children},
	journal = {European Child {\&} Adolescent Psychiatry},
	year = {2018},
	month = {Nov},
	abstract = {<p>Severe childhood aggressive behaviors are known to predict negative outcomes later in life; however, little is known about the effect of when in childhood aggression problems are diagnosed. While an earlier first diagnosis of problematic aggressive behavior might be associated with increased severity and, thus, worse outcomes, it is also possible that an earlier diagnosis affords an earlier start of treatment programs or indicates that greater attention is being paid to behavioral problems, thus resulting in attenuation of the severity of childhood aggression\&$\#$39;s impact on distal outcomes. The current study analyzed data from the population-based Swedish Data Registries, which include data on all children formally diagnosed by the Swedish medical system with a wide range of aggression problems between ages 8 and 18 (N{\th}inspace}={\th}inspace}5816) during the years 1987\&ndash;2013, along with a matched control. Time-to-event analyses investigated whether the age at time of diagnosis affects later life outcomes while controlling for relevant confounders. Results show that for both boys and girls, those with a later diagnosis had lower average incomes (regression coefficient b{\th}inspace}={\th}inspace}\&minus;{\th}inspace}0.055, p{\th}inspace}\&lt;{\th}inspace}0.005) and a higher probability of having a criminal record (odds ratio 1.126, p{\th}inspace}\&lt;{\th}inspace}0.005) than children with earlier diagnoses. The effect on suicide attempts was not significant after correcting for multiple testing (odds ratio 1.264, p{\th}inspace}={\th}inspace}0.016). Grade score was not significantly affected. The results warrant further research concerning the potential advantage of earlier diagnoses, especially concerning generalizability beyond the Swedish population.</p>
},
	issn = {1435-165X},
	doi = {10.1007/s00787-018-1250-9},
	url = {https://doi.org/10.1007/s00787-018-1250-9},
	author = {Ian Campbell and Lundstr{\"o}m, Sebastian and Henrik Larsson and Lichtenstein, Paul and Gitta Lubke}
}
@article {248,
	title = {Inference Based on the Best-Fitting Model Can Contribute to the Replication Crisis: Assessing Model Selection Uncertainty Using a Bootstrap Approach},
	journal = {Structural Equation Modeling: A Multidisciplinary Journal},
	volume = {23},
	year = {2016},
	pages = {479-490},
	abstract = {<p>Inferences and conclusions drawn from model fitting analyses are commonly based on a single \&ldquo;best fitting\&rdquo; model. If model selection and inference are carried out using the same data model selection, uncertainty is ignored. We illustrate the Type I error inflation that can result from using the same data for model selection and inference, and we then propose a simple bootstrap-based approach to quantify model selection uncertainty in terms of model selection rates. A selection rate can be interpreted as an estimate of the replication probability of a fitted model. The benefits of bootstrapping model selection uncertainty are demonstrated in growth mixture analyses of data from the National Longitudinal Study of Youth, and a 2-group measurement invariance analysis of the Holzinger\&ndash;Swineford data.</p>
},
	doi = {10.1080/10705511.2016.1141355},
	url = {https://doi.org/10.1080/10705511.2016.1141355},
	author = {Gitta Lubke and Ian Campbell}
}
