ABSTRACT
BACKGROUND: Phase II clinical trials are important milestones to determine whether a dose-effect exists and to decide on future doses to use in confirmatory studies. To take into account the overall shape of the dose-response curve, modeling the relationship by linear or non-linear models is preferable to the classical pair-wise comparisons of the effect of each dose versus the placebo or the comparator. The multiple comparisons and modeling approach has been developed within the last 10 years to address this important question in the clinical development of drugs. Despite some recent publications referring to this methodology, few detailed applications have been shown so far and several practical questions remain to be addressed. METHODS: Starting from a set of candidate models, model selection using classical methods criteria is possible. However, it suffers some limitations, not taking into account the uncertainty of the selection process itself. An attractive solution is to use model averaging, which applies appropriate weights to the parameters (e.g., the minimum effective dose) obtained from each model. RESULTS: A discussion of the selection criteria is first presented. Through two real examples, how to proceed with model selection and model averaging is presented and discussed. LIMITATIONS: The first multiple comparisons and modeling approach papers addressed normal responses. More recently, an extension of this methodology has been proposed to deal with other types of responses, in particular binary, time-to-event and longitudinal data. Questions that remain are concerned with the choice of the candidate models and of their parameters' guesstimates. CONCLUSIONS: The analysis of clinical dose-finding studies using a modeling of the entire curve offers a promising alternative as compared with the classical multiple comparisons methods, while not compromising the necessary rigor of the analysis.
ABSTRACT
A retrospective cohort study was conducted on 1541 HIV-infected patients to determine variables associated with the incidence of herpes zoster. A single failure Cox model showed that herpes zoster incidence increased following the first 6 months of antiretroviral treatment adjusted hazard ratio (AHR)=5 (95%CI=2.6-9.2), P<0.001; in the >60 years age group AHR=2 (95%CI=1-4), P=0.04; in patients in the top CD8 quartile AHR=2.1 (95%CI=1.3-3.6), P<0.001; and in patients previously reported to use crack cocaine AHR=5.9, (95%CI=1.4-25), P=0.02. Herpes zoster incidence increased in patients with CD4 counts<500 per mm(3) and gradually declined since 1992-1996, with AHR=0.3 (95%CI=0.2-0.5), P<0.001 for the 1997-2002 period and AHR=0.24 (95%CI=0.14-0.4), P<0.001 for the 2002-2008 period. Contrary to what has been described elsewhere, there was no specific effect of protease inhibitors on herpes zoster incidence. The present study is the first to suggest that crack cocaine is associated with an increased incidence of herpes zoster. The neurological or immunological effects of crack are discussed.
