Sustained adherence to a Mediterranean diet and physical activity on all-cause mortality in the Melbourne Collaborative Cohort Study: application of the g-formula.

Background: Adherence to a traditional Mediterranean diet has been associated with lower mortality and cardiovascular disease risk. The relative importance of diet compared to other lifestyle factors and effects of dietary patterns over time remains unknown.

Methods: We used the parametric G-formula to account for time-dependent confounding, in order to assess the relative importance of diet compared to other lifestyle factors and effects of dietary patterns over time.

Introduction to systematic reviews and meta-analysis.

Systematic reviews provide a method for collating and synthesizing research, and are used to inform healthcare decision making by clinicians, consumers and policy makers. A core component of many systematic reviews is a meta-analysis, which is a statistical synthesis of results across studies. In this review article, we introduce meta-analysis, focusing on the different meta-analysis models, their interpretation, how a model should be selected and discuss potential threats to the validity of meta-analyses.

A simple method to choose the most representative stride and detect outliers.

Kinematic data for gait analysis consists of joint angle curves plotted against percentages of the gait cycle. A typical gait analysis entails repeated measurement of the kinematic data. We present an automatic and computationally inexpensive method to choose the most representative curve and detect outliers amongst repeated curves.

Correcting for the bias caused by exposure measurement error in epidemiological studies.

An important goal of many epidemiological studies is to estimate the magnitude of association between an exposure and an outcome. Exposure measurement error causes bias in such estimates of association and can be substantial. In this article, we describe the problem of exposure measurement error and its effects.

Introduction to propensity scores.

Although randomization provides a gold-standard method of assessing causal relationships, it is not always possible to randomly allocate exposures. Where exposures are not randomized, estimating exposure effects is complicated by confounding. The traditional approach to dealing with confounding is to adjust for measured confounding variables within a regression model for the outcome variable.

Introduction to causal diagrams for confounder selection.

In respiratory health research, interest often lies in estimating the effect of an exposure on a health outcome. If randomization of the exposure of interest is not possible, estimating its effect is typically complicated by confounding bias. This can often be dealt with by controlling for the variables causing the confounding, if measured, in the statistical analysis.