Efficacy of daily intake of Lactobacillus casei Shirota on respiratory symptoms and influenza vaccination immune response: a randomized, double-blind, placebo-controlled trial in healthy elderly nursing home residents.

Background: Age is associated with immune dysregulation, which results in an increased infection rate and reduced effectiveness of vaccination.

Objective: We assessed whether an intervention with Lactobacillus casei Shirota (LcS) in elderly nursing home residents reduced their susceptibility to respiratory symptoms and improved their immune response to influenza vaccination.

Design: Between October 2007 and April 2008, a randomized, double-blind, placebo-controlled trial was conducted in 737 healthy people aged ≥ 65 y in 53 nursing homes in Antwerp, Belgium.

Intrauterine exposure to environmental pollutants and body mass index during the first 3 years of life.

Objective: We investigated the association between body mass index (BMI) standard deviation score (SDS) and prenatal exposure to hexachlorobenzene, dichlorodiphenyldichloroethylene (DDE), dioxin-like compounds, and polychlorinated biphenyls (PCBs).

Methods: In this prospective birth cohort study, we assessed a random sample of mother-infant pairs (n = 138) living in Flanders, Belgium, with follow-up until the children were 3 years of age. We measured body mass index as standard deviation scores (BMI SDS) of children 1-3 years of age as well as pollutants measured in cord blood.

A longitudinal analysis on the association between antibiotic use, intestinal microflora, and wheezing during the first year of life.

Objective: To examine the association between the intestinal flora at the age of three weeks and wheezing during the first year of life in a prospective birth cohort study.

Methods: The Asthma and Allergy study is a prospective birth cohort study. A total of 154 children were recruited through maternity clinics.

Incomplete hierarchical data.

The researcher collecting hierarchical data is frequently confronted with incompleteness. Since the processes governing missingness are often outside the investigator's control, no matter how well the experiment has been designed, careful attention is needed when analyzing such data.We sketch a standard framework and taxonomy largely based on Rubin's work.

A latent-class mixture model for incomplete longitudinal Gaussian data.

In the analyses of incomplete longitudinal clinical trial data, there has been a shift, away from simple methods that are valid only if the data are missing completely at random, to more principled ignorable analyses, which are valid under the less restrictive missing at random assumption. The availability of the necessary standard statistical software nowadays allows for such analyses in practice. While the possibility of data missing not at random (MNAR) cannot be ruled out, it is argued that analyses valid under MNAR are not well suited for the primary analysis in clinical trials.

A local influence sensitivity analysis for incomplete longitudinal depression data.

In the analyses of incomplete longitudinal clinical trial data, there has been a shift, away from simple ad hoc methods that are valid only if the data are missing completely at random (MCAR), to more principled (likelihood-based or Bayesian) ignorable analyses, which are valid under the less restrictive missing at random (MAR) assumption. The availability of the necessary standard statistical software allows for such analyses in practice. Although the possibility of data missing not at random (MNAR) cannot be ruled out, it is argued that analyses valid under MNAR are not well suited for the primary analysis in clinical trials.

Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials.

Background: In many clinical trials, data are collected longitudinally over time. In such studies, missingness, in particular dropout, is an often encountered phenomenon.

Methods: We discuss commonly used but often problematic methods such as complete case analysis and last observation carried forward and contrast them with broadly valid and easy to implement direct-likelihood methods.

Analyzing incomplete longitudinal clinical trial data.

Using standard missing data taxonomy, due to Rubin and co-workers, and simple algebraic derivations, it is argued that some simple but commonly used methods to handle incomplete longitudinal clinical trial data, such as complete case analyses and methods based on last observation carried forward, require restrictive assumptions and stand on a weaker theoretical foundation than likelihood-based methods developed under the missing at random (MAR) framework. Given the availability of flexible software for analyzing longitudinal sequences of unequal length, implementation of likelihood-based MAR analyses is not limited by computational considerations. While such analyses are valid under the comparatively weak assumption of MAR, the possibility of data missing not at random (MNAR) is difficult to rule out.