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To account for the dropout from wave 3 to wave 4, we conducted inverse probability of attrition weighting (Weuve et al. 2012).
Fig. 2 Available data for all outcome analyses during wave 3 and wave 4. Abbreviations: IPAW, inverse probability of attrition weights.
The inverse probability of attrition weighting for each participant in wave 4 was given by: mathrm{Stabilized} mathrm{weights} = mathrm{P}left(mathrm{D}4=0right) / mathrm{P}left(mathrm{D}4=0Big|mathrm{L}3, mathrm{L}1right).
This eliminates all arrows into or from CV0. Inverse probability of attrition weights.
20 The probability of attrition was not statistically different across the study arms.
Adolescents with many ACD problems at T1 had a somewhat higher probability of attrition than those with lower levels (OR = 1.3, p =.003, standardized scores).
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Data were analysed in Stata SE 11.1 (Stata Corporation, Texas, USA) using survey commands with sampling and non-response weights to adjust for the clustered sample design, unequal probability of being sampled and attrition between study years.
However, we used inverse probability weighted method to directly analyse only the complete cases with special weights assigned to those cases based on estimated probabilities of completeness, thereby minimising the possibility of attrition bias.
We created two sets of inverse probability attrition weights, corresponding to all parent and adolescent surveys completed at each time.
Finally, we estimated the probability of death, loss to follow-up, attrition, and first severe morbidity episode, using the Kaplan-Meier method.
For example, a cohort of patients attending regular medical appointments would fit this attrition mechanism if the probability of dropping out after a given visit was likely to depend on the results of the same visit.
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