peer smoking on adolescents' own smoking is still a matter of debate. In addition, little research has examined the role of sign8cant others' behavior on different stages of smoking onset. In particular, not much information is available regarding gender and ethnic differences in social influences on smoking behavior. We use structural equation modeling to address these issues. Different theoretical perspectives from cognitive-affective theories (Ajzen 1985; Ajzen and Fishbein 1980) and social learning theories (Akers et al. 1979; Bandura 1969, 1982, 1986) have been integrated into a structural model of smoking influence. The results show that friends' smoking affects adolescent initiation into smoking both directly and indirectly, whereas parental smoking influences smoking initiation only indirectly. The data also show that friends' and parents' smoking affect smoking escalation only indirectly. In general, friends' smoking has a stronger effect on adolescents' smoking behavior, particularly on initiation. Multiple group comparisons of the structural models predicting smoking initiation among males and females reveal that parental approval of smoking plays a sign8cant mediating role for females, but not for males. Comparisons of Whites, Blacks, Hispanics, and other ethnic groups reveal that there are some signiJicant differences in the pathways of friends' influences among the four groups.

In recent years, the use of the last observation carried forward (LOCF) approach in imputing missing data in clinical trials has been greatly criticized, and several likelihood-based modeling approaches are proposed to analyze such incomplete data. One of the proposed likelihood-based methods is the Mixed-Effect Model Repeated Measure (MMRM) model. To compare the performance of LOCF and MMRM approaches in analyzing incomplete data, two extensive simulation studies are conducted, and the empirical bias and Type I error rates associated with estimators and tests of treatment effects under three missing data paradigms are evaluated. The simulation studies demonstrate that LOCF analysis can lead to substantial biases in estimators of treatment effects and can greatly inflate Type I error rates of the statistical tests, whereas MMRM analysis on the available data leads to estimators with comparatively small bias, and controls Type I error rates at a nominal level in the presence of missing completely at random (MCAR) or missing at random (MAR) and some possibility of missing not at random (MNAR) data. In a sensitivity analysis of 48 clinical trial datasets obtained from 25 New Drug Applications (NDA) submissions of neurological and psychiatric drug products, MMRM analysis appears to be a superior approach in controlling Type I error rates and minimizing biases, as compared to LOCF ANCOVA analysis. In the exploratory analyses of the datasets, no clear evidence of the presence of MNAR missingness is found.

Most school-based smoking prevention studies employ designs in which schools or classrooms are assigned to different treatment conditions while observations are made on individual students. This design requires that the treatment effect be assessed against the between-school variance. However, the between-school variance is usually larger than the variance that would be obtained if students were individually randomized to different conditions. Consequently, the power of the test for a treatment effect is reduced, and it becomes difficult to detect important treatment effects. To assess the potential loss of power or to calculate appropriate sample sizes, investigators need good estimates of the intraclass correlations for the variables of interest. The authors calculated intraclass correlations for some common outcome variables in a school-based smoking prevention study, using a three-level model-i.e., students nested within classrooms and classrooms nested within schools. The authors present the intraclass correlation estimates for the entire data set, as well as separately by sex and ethnicity. They also illustrate the use of these estimates in the planning of future studies.

This research examines the relative importance of parental and friends' influences on adolescents' smoking behavior and changes in the effects of social influences during adolescence. Data were collected at 4 times from 7th to 9th grades. Random‐effects ordinal regression models were employed to predict the repeated classification of adolescent smoking status using time effects, prior smoking status, friends' smoking, and parental smoking. In general, the effects of friends' smoking are stronger than those of parental smoking, and these differences increase over time. In addition, friends' smoking has greater effects on nonsmokers than smokers. Separate models for males and females disclose some gender differences. In particular, the effects of friends' smoking are stronger for females than for males, and the increasing trend of friends' influences is more noticeable for females than for males. Models for 4 ethnic groups also suggest differential susceptibility to social influences in different cultures.