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Results for “causal inference”

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At least 109 records · Page 6Linked to original sources
PubMed2005

An integrative genomics approach to infer causal associations between gene expression and disease.

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

11-beta-Hydroxysteroid Dehydrogenase Type 1↗
PubMed2006

Inferring causality assessments from predictive responses: cue interaction without cue competition.

The authors criticize the use of participants' predictive responses during a learning phase as a measure of causal assessments. Simulations demonstrate that a general aim to minimize error as required in the prediction task will produce results in accordance with those observed, in particular apparent cue interaction effects. It is argued that measures estimated from prediction responses do not reflect causal ratings, but are side-effects of learning explained by adaptation in strategy to changes in presented cell frequencies. The results are reinterpreted to be in line with a causal model view, not requiring dual processes or cue competition of lower level associative processes.

Association Learning↗
PubMed1990

Towards causal inference in occupational cancer epidemiology--I. An example of the interpretive value of using local rates as the reference statistic.

A brief overview is made of the criteria currently applied for establishing causation in occupational cancer epidemiology, and further criteria or 'desiderata' are proposed. These supplement the present somewhat simplistic ones for 'sufficient evidence of carcinogenicity' advocated by the International Agency for Research against Cancer in their monograph series. A frequent cause of confounding, not always appreciated, is the limited choice of a reference population for the statistical analyses. The study population is often an industrial one with marked regional and urban biases, especially for some of the cancers under scrutiny: these biases can influence markedly the resultant Standardized Mortality Ratio (SMR) calculated, and thus lead to misleading conclusions. The British Rubber Manufacturers' Mortality Study, 1946-1980, provided an opportunity to extract the data set of one of the 13 factories involved. Both local and national death rates were applied as a reference, and the use of the former substantially reduced the high SMRs previously recorded for lung and stomach cancers. This emphasized the need for further analyses when making other internal comparisons in order to accommodate more of the 'desiderata' listed. Ultimately causation can be inferred only after weighing up all the evidence, and must be based on a 'balance of probabilities'.

Data Interpretation, Statistical↗
PubMed2001

Causal inference on the difference of the restricted mean lifetime between two groups.

When comparing survival times between two treatment groups, it may be more appropriate to compare the restricted mean lifetime, i.e., the expectation of lifetime restricted to a time L, rather than mean lifetime in order to accommodate censoring. When the treatments are not assigned to patients randomly, as in observational studies, we also need to account for treatment imbalances in confounding factors. In this article, we propose estimators for the difference of the restricted mean lifetime between two groups that account for treatment imbalances in prognostic factors assuming a proportional hazards relationship. Large-sample properties of our estimators based on martingale theory for counting processes are also derived. Simulation studies were conducted to compare these estimators and to assess the adequacy of the large-sample approximations. Our methods are also applied to an observational database of acute coronary syndrome patients from Duke University Medical Center to estimate the treatment effect on the restricted mean lifetime over 5 years.

Biometry↗