Body Mass Index and Cancer Risk: The Evidence for Causal Association

Increased body mass index (BMI), as an approximation of body adiposity, is a risk factor for developing several adult malignancies. To quantify these risks, we reported a comprehensive systematic review (Lancet 2008; 371: 569-78) of prospective observational studies determining associations between BMI and risk of incident cancer for 20 cancer types. We demonstrated that associations are: (i) sex-specific; (ii) exist for a wider range of malignancies than previously thought; and (iii) are broadly consistent across geographic populations. In the present paper, we tested these data against the Bradford-Hill criteria of causal association, and argue that the available data support strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence and probably analogy. However, the experimental evidence supporting reversibility is currently lacking, though indirect evidence from longitudinal data in cohort studies and long-term follow-up post-bariatric surgery is emerging. We additionally assessed these data against appropriate adjustment for available confounding factors; measurement error and study design; and residual confounding; and found lack of alternative explanations. We conclude that there is considerable evidence to support a causal association between BMI and risk for many cancer types, but in order to establish the role of weight control in cancer prevention, there is a need to develop trial frameworks in which to better test reversibility.

A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study

When estimating the effect of treatment on HIV using data from observational studies, standard methods may produce biased estimates due to the presence of time-dependent confounders. Such confounding can be present when a covariate, affected by past exposure, is both a predictor of the future exposure and the outcome. One example is the CD4 cell count, being a marker for disease progression for HIV patients, but also a marker for treatment initiation and influenced by treatment. Fitting a marginal structural model (MSM) using inverse probability weights is one way to give appropriate adjustment for this type of confounding. In this paper we study a simple and intuitive approach to estimate similar treatment effects, using observational data to mimic several randomized controlled trials. Each 'trial' is constructed based on individuals starting treatment in a certain time interval. An overall effect estimate for all such trials is found using composite likelihood inference. The method offers an alternative to the use of inverse probability of treatment weights, which is unstable in certain situations. The estimated parameter is not identical to the one of an MSM, it is conditioned on covariate values at the start of each mimicked trial. This allows the study of questions that are not that easily addressed fitting an MSM. The analysis can be performed as a stratified weighted Cox analysis on the joint data set of all the constructed trials, where each trial is one stratum. The model is applied to data from the Swiss HIV cohort study.

Patent foramen ovale may be causal for the first stroke but unrelated to subsequent ischemic events

Studies with very long follow-up are scarce in patients with cryptogenic stroke and patent foramen ovale (PFO). Little is known about the etiology of recurrent cerebrovascular events (CVE) in PFO patients.

A causal model of mathematics performance in early adolescence: the role of sex

Using path analysis, the present investigation was done to clarify possible causal linkages among general scholastic aptitude, academic achievement in mathematics, self-concept of ability, and performance on a mathematics examination. Subjects were 122 eighth-grade students who completed a mathematics examination as well as a measure of self-concept of ability. Aptitude and achievement measures were obtained from school records. Analysis showed sex differences in prediction of performance on the mathematics examination. For boys, this performance could be predicted from scholastic aptitude and previous achievement in mathematics. For girls, performance only could be predicted from previous achievement in mathematics. These results indicate that the direction, strength, and magnitude of relations among these variables differed for boys and girls, while mean levels of performance did not.

An empirical examination of the integration of causal attribution and social learning theories in achievement situations

Using path analysis, the present investigation sought to clarify possible operational linkages among constructs from social learning and attribution theories within the context of a self-esteem system. Subjects were 300 undergraduate university students who completed a measure of self-esteem and indicated expectancies for success and minimal goal levels for an experimental task. After completing the task and receiving feedback about their performance, subjects completed causal attribution and self-esteem questionnaires. Results revealed gender differences in the degree and strength of the proposed relations, but not in the mean levels of the variables studied. Results suggested that the integration of social learning and attribution theories within a single conceptual model provides a better understanding of students' behaviors and self-esteem in achievement situations.

The causal effect of switching to second-line ART in programmes without access to routine viral load monitoring

We examined the effect of switching to second-line antiretroviral therapy (ART) on mortality in patients who experienced immunological failure in ART programmes without access to routine viral load monitoring in sub-Saharan Africa.

A missense mutation in the skeletal muscle chloride channel 1 (CLCN1) as candidate causal mutation for congenital myotonia in a New Forest pony

A 7-month-old New Forest foal presented for episodes of recumbency and stiffness with myotonic discharges on electromyography. The observed phenotype resembled congenital myotonia caused by CLCN1 mutations in goats and humans. Mutation of the CLCN1 gene was considered as possible cause and mutation analysis was performed. The affected foal was homozygous for a missense mutation (c.1775A>C, p.D592A) located in a well conserved domain of the CLCN1 gene. The mutation showed a recessive mode of inheritance within the reported pony family. Therefore, this CLCN1 polymorphism is considered to be a possible cause of congenital myotonia.

THE ROLE OF AN EXPLICIT CAUSAL FRAMEWORK IN AFFECTED SIB PAIR DESIGNS WITH COVARIATES

The affected sib/relative pair (ASP/ARP) design is often used with covariates to find genes that can cause a disease in pathways other than through those covariates. However, such "covariates" can themselves have genetic determinants, and the validity of existing methods has so far only been argued under implicit assumptions. We propose an explicit causal formulation of the problem using potential outcomes and principal stratification. The general role of this formulation is to identify and separate the meaning of the different assumptions that can provide valid causal inference in linkage analysis. This separation helps to (a) develop better methods under explicit assumptions, and (b) show the different ways in which these assumptions can fail, which is necessary for developing further specific designs to test these assumptions and confirm or improve the inference. Using this formulation in the specific problem above, we show that, when the "covariate" (e.g., addiction to smoking) also has genetic determinants, then existing methods, including those previously thought as valid, can declare linkage between the disease and marker loci even when no such linkage exists. We also introduce design strategies to address the problem.

Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.