Distinct problems in the analysis of failure times with competing causes of failure include the estimation of treatment or exposure effects on specific failure types, the study of interrelations among failure types, and the estimation of failure rates for some causes given the removal of certain other failure types. The usual formation of these problems is in terms of conceptual or latent failure times for each failure type. This approach is criticized on the basis of unwarranted assumptions, lack of physical interpretation and identifiability problems. An alternative approach utilizing cause-specific hazard functions for observable quantities, including time-dependent covariates, is proposed. Cause-specific hazard functions are shown to be the basic estimable quantities in the competing risks framework. A method, involving the estimation of parameters that relate time-dependent risk indicators for some causes to cause-specific hazard functions for other causes, is proposed for the study of interrelations among failure types. Further, it is argued that the problem of estimation of failure rates under the removal of certain causes is not well posed until a mechanism for cause removal is specified. Following such a specification, one will sometimes be in a position to make sensible extrapolations from available data to situations involving cause removal. A clinical program in bone marrow transplantation for leukemia provides a setting for discussion and illustration of each of these ideas. Failure due to censoring in a survivorship study leads to further discussion.
The rigor of the HSPP trial suggests high credence for the intervention impact results. Consistent with previous trials, there is no evidence from this trial that a school-based social-influences approach is effective in the long-term deterrence of smoking among youth.
This paper gives sharp bounds for the joint survival function G(ti, t2,. . . ,t.) -X1 > tb, X2 > t2,. . . ,Xr > tr), and for the marginal survival functions Sjt) = X > t), j = 1,2,.. . ,r, when the sub-survival functions S;(t)= AX >,=mn=1,2.rXk) are fixed. Theorem 1 gives the bounds for r = 2, and Theorem 2 gives the bounds for general r. Theorem 3 applies the result to the competing risks problem, and presents empirical bounds based on the observations. Finally, an example illustrates the bounds.
Key Words correlated data, generalized estimating equations (GEE), generalized linear mixed models (GLMM), permutation tests, matched design s Abstract Group randomized trials (GRTs) in public health research typically use a small number of randomized groups with a relatively large number of participants per group. Two fundamental features characterize GRTs: a positive correlation of outcomes within a group, and the small number of groups. Appropriate consideration of these fundamental features is essential for design and analysis. This paper presents the fundamental features of GRTs and the importance of considering these features in design and analysis. It also reviews and contrasts the main analytic methods proposed for GRTs, emphasizing the assumptions required to make these methods valid and efficient. Also discussed are various design issues, along with guidelines for choosing among them. A real data example illustrates these issues and methods.
A number of longitudinal studies have explored the role of friends', parents', and older siblings' smoking in children's smoking acquisition. A reasonable implication of this previous research is that intervention efforts could be beneficially directed toward countering the potential influence of friends' and possibly older siblings' smoking but not parents' smoking. However, methodological limitations of this previous research motivated our reevaluation of the role of friends', parents', and older siblings' smoking in children's smoking. Close friends' smoking status was assessed when children were in 5th grade, whereas parents' and older siblings' smoking status was assessed when children were in 3rd grade. The outcome, children's daily smoking status, was assessed in 12th grade. The setting was 40 Washington state school districts that participated in the long-term Hutchinson Smoking Prevention Project. Participants were the 4,576 families for whom close friends', parents', and older siblings' smoking status as well as children's smoking status were available. The probability that each close friend's smoking influenced the child to smoke daily was 9% (95% CI = 6%-12%), the probability that each parent's smoking influenced the child to smoke daily was 11% (95% CI = 9%-14%), and the probability that each older sibling's smoking influenced the child to smoke daily was 7% (95% CI = 1%-13%). These results suggest that close friends', parents', and siblings' smoking were similarly important influences on children's smoking. Family-focused interventions could be a valuable future direction of prevention research.
In contrast to previous research, the results provide new evidence suggesting that family smoking influences both initiation and escalation of children's smoking. Results also quantify, in terms of probabilities, the importance of parents' and older siblings' smoking on children's three major smoking transitions. Parents' smoking, as well as older siblings' smoking, are important behaviors to target in preventing adolescents from making smoking transitions.
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