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Statistical analyses of longitudinal data with drop-outs based on direct likelihood, and using all the available data, provide unbiased and fully efficient estimates under some assumptions about the drop-out mechanism. Unfortunately, these assumptions can never be tested from the data. Thus, sensitivity analyses should be routinely performed to assess the robustness of inferences to departures from these assumptions. However, each specific scientific context requires different considerations when setting up such an analysis, no standard method exists and this is still an active area of research. We propose a flexible procedure to perform sensitivity analyses when dealing with continuous outcomes, which are described by a linear mixed model in an initial likelihood analysis. The methodology relies on the pattern-mixture model factorisation of the full data likelihood and was validated in a simulation study. The approach was prompted by a randomised clinical trial for sleep-maintenance insomnia treatment. This case study illustrated the practical value of our approach and underlined the need for sensitivity analyses when analysing data with drop-outs: some of the conclusions from the initial analysis were shown to be reliable, while others were found to be fragile and strongly dependent on modelling assumptions. R code for implementation is provided. [ABSTRACT FROM AUTHOR]

When fitting marginal models to correlated outcomes, the so-called sandwich variance is commonly used. However, this is not the case when fitting mixed models. Using two data sets, we illustrate the problems that can be encountered. We show that the differences or the ratios between the naive and sandwich standard deviations of the fixed effects estimators provide convenient means of assessing the fit of the model, as both are consistent when the covariance structure is correctly specified, but only the latter is when that structure is misspecified. When the number of statistical units is not too small, the sandwich formula correctly estimates the variance of the fixed effects estimator even if the random effects are misspecified, and it can be used in a diagnostic tool for assessing the misspecification of the random effects. A simple comparison with the naive variance is sufficient and we propose considering a ratio of the naive and sandwich standard deviation out of the [3/4; 4/3] interval as signaling a risk of erroneous inference due to a model misspecification. We strongly advocate broader use of the sandwich variance for statistical inference about the fixed effects in mixed models. [ABSTRACT FROM AUTHOR]

Three methods of handling missing items in Quality of Life (QOL) studies are compared using data from a clinical trial. Complete case analysis can lead to a selection bias and a loss of precision if there is a low answer rate to one item. The two other methods, simple mean imputation and multiple imputation, lead to similar results. We conclude that when the scale has been carefully validated in the population of interest and includes only items with a high response rate (e.g., a mean response rate greater than 95% for the items of a given subscale), a simple imputation method can be used to handle incomplete observations. [ABSTRACT FROM AUTHOR]

Compares monoinfected and coinfected human immunodeficiency virus (HIV) patients in Martinique, French West Indies. Information on clinical grade at diagnosis; Progression towards AIDS of coinfected and monoinfected patients.

Sera of supposedly healthy blood donors were screened for the presence of anti HTLV-I p24 antibodies, and HLA typing for A, B, C and DR antigens was performed for 68 seropositive subjects and 92 seronegative controls. HLA phenotypes of the two groups were not significantly different but the level of the antibody response was related to the antigens of the HLA-B (P = 0.02) and -C loci (P = 0.003). Subjects with HLA-B12 or-B21 antigens had lower titres than the others. Subjects with HLA-Cw2 or-Cw7 antigens had higher titres than the others, but only the difference between HLA-B12 positive and negative subjects (P = 0.002) remained significant at the α = 0.10 level if the classical, although conservative, Bonferroni procedure was used to correct for the number of comparisons performed. [ABSTRACT FROM AUTHOR]

Abstract: Objective: The framework consists of cohort or case-series studies with intermittent exposure and two types of events. The aim is to define and estimate an association measure between the exposure and the occurrence of one type of event rather than the other. Study Design and Setting: The model and the estimation method are obtained by extending Farrington''s approach for one type of recurrent event. The proposed association measure “RRc” is the ratio of the relative risks pertaining to each type of event. The estimated RRc and its confidence interval are derived under the independence assumption between the counts of the two types of events. The data that are analyzed are part of the data of a study on antimicrobial resistance in children. Results: An interpretation of the RRc is proposed in terms of an odds ratio, which parallels a similar association measure defined in cross-sectional studies (“ORc”). The estimated value of the RRc agrees with the ORc reported in previous studies. Conclusion: The RRc appears as a useful tool for evaluating the risk of colonization (or infection) with resistant rather than susceptible bacteria following a previous intake of a given antibiotic conditional on colonization (or infection) with any bacteria. [Copyright &y& Elsevier]

Focuses about medical sciences. Prevalence of diabetes in Asia and in Europe; Effect of blood transfusion on colorectal cancer; Relation between nutritional status and immunity of influenza virus among the aged.