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PubMed2005

Smoking and cataract: review of causal association.

Several risk factors for the development of cataract have been identified. This review evaluates epidemiologic literature that has examined tobacco smoking as a risk factor for cataract formation using established causality criteria. Twenty-seven studies were included in this review. Evidence suggests that smoking has a 3-fold increase on the risk for incident nuclear cataract development. There was also evidence of dose response, temporal relationship, and reversibility of effect. There was limited evidence of an association between smoking and posterior subcapsular cataract, but little or no association with cortical cataract. Thus, the literature review indicated a strong association between smoking and the development of cataract, particularly nuclear cataract. The association fulfills the established criteria for causality. The association between smoking and other types of cataract is less distinct and requires further evaluation.

Cataract↗

PubMed2002

Bias and causal associations in observational research.

Readers of medical literature need to consider two types of validity, internal and external. Internal validity means that the study measured what it set out to; external validity is the ability to generalise from the study to the reader's patients. With respect to internal validity, selection bias, information bias, and confounding are present to some degree in all observational research. Selection bias stems from an absence of comparability between groups being studied. Information bias results from incorrect determination of exposure, outcome, or both. The effect of information bias depends on its type. If information is gathered differently for one group than for another, bias results. By contrast, non-differential misclassification tends to obscure real differences. Confounding is a mixing or blurring of effects: a researcher attempts to relate an exposure to an outcome but actually measures the effect of a third factor (the confounding variable). Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques. If a reader cannot explain away study results on the basis of selection, information, or confounding bias, then chance might be another explanation. Chance should be examined last, however, since these biases can account for highly significant, though bogus results. Differentiation between spurious, indirect, and causal associations can be difficult. Criteria such as temporal sequence, strength and consistency of an association, and evidence of a dose-response effect lend support to a causal link.

Bias↗

PubMed2000

Epidemiologic evidence and causal inference.

Preventing cancer depends on the ability to recognize and remove causal factors. In current practice, the methods used to judge cause from epidemiologic, clinical trials and biologic evidence include systematic narrative reviews, criteria-based inference methods, and meta-analysis. Subjectivity and values play a key role in the practice of causal inference, especially in selecting criteria and assigning rules of evidence to those criteria. Judging cause is a central concern of physicians, epidemiologists, and other public health professionals committed to cancer prevention.

Causality↗

PubMed2002

Medicolegal causal analysis.

This article reviews basic tort law concepts, which medical evaluators should be familiar with when working in the medicolegal environment. To facilitate medicolegal causal analysis, the concept of the health claim statement or argument is proposed. We describe medicolegal causal analysis model, which should assist the medical evaluator's determination of the relationship between an alleged accident and personal injury. This practical model minimizes bias and ensures that inferences are supported by factual medical evidence.

Causality↗

PubMed1996

Inferring causality.

The goal of research is to discover new knowledge to improve patient care. Research is essential because of its ability to establish causal relationships. This article discusses the minimum requirements a research report must meet to establish a causal relationship.

Causality↗

PubMed2002

Illness causal attributions: an exploratory study of their structure and associations with other illness cognitions and perceptions of control.

Two studies were conducted to investigate the cognitive organization and psychological meaning of illness causes. Using a direct similarity judgment method (Study 1), illness causes were found cognitively organized in a hierarchical configuration that could meaningfully be represented as a tree with three main branches--environmental, behavioral, and hidden causes--that further divided into subcategories. This classification of illness causes was associated with other components of the illness schema, namely, the consequences and control/cure dimensions, but not with timeline perceptions (Study 2). Perceptions of control were significantly associated with the cognitive organization of illness causal attributions. Personal relevancy was found as a moderator of illness causal attributions, influencing the relationships between attributions and other illness cognitions.

Adult↗

PubMed1997

Factor analysis, causal indicators and quality of life.

Exploratory factor analysis (EFA) remains one of the standard and most widely used methods for demonstrating construct validity of new instruments. However, the model for EFA makes assumptions which may not be applicable to all quality of life (QOL) instruments, and as a consequence the results from EFA may be misleading. In particular, EFA assumes that the underlying construct of QOL (and any postulated subscales or 'factors') may be regarded as being reflected by the items in those factors or subscales. QOL instruments, however, frequently contain items such as diseases, symptoms or treatment side effects, which are 'causal indicators'. These items may cause reduction in QOL for those patients experiencing them, but the reverse relationship need not apply: not all patients with a poor QOL need be experiencing the same set of symptoms. Thus a high level of a symptom item may imply that a patient's QOL is likely to be poor, but a poor level of QOL need not imply that the patient probably suffers from that symptom. This is the reverse of the common EFA model, in which it is implicitly assumed that changes in QOL and any subscales 'cause' or are likely to be reflected by corresponding changes in all their constituent items; thus the items in EFA are called 'effect indicators.' Furthermore, disease-related clusters of symptoms, or treatment-induced side-effects, may result in different studies finding different sets of items being highly correlated; for example, a study involving lung cancer patients receiving surgery and chemotherapy might find one set of highly correlated symptoms, whilst prostate cancer patients receiving hormone therapy would have a very different symptom correlation structure. Since EFA is based upon analyzing the correlation matrix and assuming all items to be effect indicators, it will extract factors representing consequences of the disease or treatment. These factors are likely to vary between different patient subgroups, according to the mode of treatment or the disease type and stage. Such factors contain little information about the relationship between the items and any underlying QOL constructs. Factor analysis is largely irrelevant as a method of scale validation for those QOL instruments that contain causal indicators, and should only be used with items which are effect indicators.

Causality↗

PubMed1996

Causal attributions predict rate of immune decline in HIV-seropositive gay men.

Research has suggested that attributions-the perceived causes of events-may affect psychological and physical health and the immune system. The authors hypothesized that attributions reflecting negative beliefs about the self, the future, and control would affect helper T cell (CD4) decline and onset of AIDS in individuals with HIV, either directly or through associations with psychological states such as depression. HIV+ gay men (N = 86) participated in a structured interview from which causal attributions were extracted and coded. Attributing negative events to aspects of the self significantly predicted faster CD4 decline over 18 months following the interview, controlling for potential psychological, behavioral, social, and health mediators such as depression and health behavior. However, attributions did not predict AIDS diagnosis during the study period. The results support the idea that causal attributions related to beliefs about the self may have an influence on the immune system.

Adult↗

PubMed2007

Conditional probability versus spatial contiguity in causal learning: Preschoolers use new contingency evidence to overcome prior spatial assumptions.

This study examines preschoolers' causal assumptions about spatial contiguity and how these assumptions interact with new evidence in the form of conditional probabilities. Preschoolers saw a toy that activated in the presence of certain objects. Children were shown evidence for the toy's activation rule in the form of patterns of probability: The toy was more likely to activate either when objects made contact with its surface (on condition) or when objects were several inches above its surface (over condition). In Experiment 1, 61 three-year-olds saw a deterministic activation rule. In Experiments 2 and 3, 48 four-year-olds saw an activation rule that was probabilistic. In Experiment 4, 30 four-year-olds saw a screening-off pattern of activation. In all 4 experiments, children used new evidence in the form of patterns of probability to make accurate causal inferences, even in the face of conflicting prior beliefs about spatial contiguity. However, children were more likely to make correct inferences when causes were spatially contiguous, particularly when faced with ambiguous evidence.

Association Learning↗

PubMed2005

Blocking and unblocking in human causal learning.

Three experiments sought to develop the suggestion that, under some circumstances, common associative learning mechanisms might underlie animal conditioning and human causal learning, by demonstrating, in humans, an effect analogous to the unblocking by reinforcer omission observed in animal conditioning. Experiment 1 found no such effect. Experiment 2, designed to prevent inhibitory influences that might have masked excitatory unblocking in Experiment 1, demonstrated unblocking, indicating common human-animal associative learning mechanisms in which the associability of a stimulus varies as a function of its predictive history. Experiment 3, using a similar design but with a procedure promoting application of rational inference processes, failed to detect the same unblocking effect, indicating that associative and cognitive mechanisms may influence human causal learning.

Adult↗

PubMed2005

Visual impressions of interactions between objects when the causal object does not move.

Stimuli were presented that consisted of a stationary row of black-bordered white rectangles. As observers watched, each rectangle in turn from left to right changed from white to black. The final rectangle did not change colour but moved off from left to right. The sequential colour change suggested motion from left to right, and observers reliably reported a visual impression that this illusory motion kicked or bumped the last rectangle, thereby making it move. The impression was stronger when the sequential colour change was faster, but was not significantly affected by the number of the rectangles in the row (ranging from 2 to 8). These results support the conclusion that neither contact nor motion of a causal object is necessary for a visual impression of causality to occur.

Causality↗

PubMed2000

Super-learning of causal judgements.

In three experiments, participants learned which of a variety of foods were capable of causing an allergic reaction in a hypothetical patient during training in which a compound of a treatment and a target food cue was paired with the reaction. In Experiment 1 the causal ratings of the target cue were increased if the treatment cue was pretrained as a preventative cause of the reaction. Experiments 2 and 3 demonstrated that the magnitude of this super-learning is unaffected by the order of compound and treatment cue training. The final study also showed that forward super-learning is not induced solely by simple exposure to the treatment cue prior to compound training but, rather, depends upon training the treatment cue as a preventative cause, whereas retrospective super-learning may be due merely to exposure of the treatment cue. These results are problematic for contingency-based accounts of causal induction but accord with modified and extended associative theories.

Adult↗

PubMed1991

Practical causal inference for ecoepidemiologists.

Environmental scientists and managers must determine whether a relationship between an environmental factor and an observed effect is causal and respond accordingly. Epidemiologists have, over the past 150 yr, developed a systematic approach to evaluating these relationships. Their criteria for objectively evaluating the relationship between a suspect cause and a chronic disease are (1) probability, (2) time order, (3) strength of association, (4) specificity, (5) consistency on replication, (6) predictive performance, and (7) coherence. These criteria can be used, with little modification, to evaluate associations in relation to diseases in fish and wildlife suspected to be caused by exposure to chemical pollutants. Some populations of fish and wildlife are members of the same guilds as subpopulations of humans. Investigations of chemically induced disease in these sentinel populations of fish and wildlife may identify the potential risks posed to these human subpopulations. Evidence evaluated using the epidemiologic criteria may assist environmental managers to determine whether a substantive case can be made to initiate preventative or remedial action. By applying the null hypothesis, scientists are forced to consider how much information must be ignored to conclude that a causal relationship does not exist.

Animals↗

PubMed2000

Positive and negative patterning in human causal learning.

Investigations of patterning discriminations by nonhuman animals have generally found that positive patterning is easier to learn than negative patterning. Studies of patterning discriminations in human causal learning tasks have failed to document any differences between positive and negative patterning. In the present study, human participants predicted an outcome on trials involving either a compound cue or its elements. Positive and negative patterning problems were successfully solved in a within-subjects design; negative patterning problems proved to be more difficult when an additional, 50% contingent cue was included (Experiment 2), but not when it was excluded (Experiment 1). Possible reasons for these results are discussed. The discussion concludes with an analysis of exemplar models (e.g., Pearce, 1994) of human causal learning and considers the conditions under which these models do and do not anticipate our results.

Adult↗

PubMed2003

A full-likelihood method for the evaluation of causality of sequence variants from family data.

In many disease genes, a substantial fraction of all rare variants detected cannot yet be used for genetic counselling because of uncertainty about their association with disease. One approach to the characterization of these unclassified variants is the analysis of patterns of cosegregation with disease in affected carrier families. Petersen et al. previously provided a simplistic Bayesian method for evaluation of causality of such sequence variants. In the present report, we propose a more general method based on the full pedigree likelihood, and we show that the use of this method can provide more accurate and informative assessment of causality than could the previous method. We further show that it is important that the pedigree information be as complete as possible and that the distinction be made between unaffected individuals and those of unknown phenotype.

Causality↗

PubMed2004

Elucidation of causal relationships for multi-sourced activities in the human brain by directed transinformation between time series of equivalent dipoles.

Visualizing the causal relationships among multi-sourced activities in the human brain non-invasively is important for the elucidation of the processing mechanism and for clinical application of the diagnosis of disease. We will show our preliminary results of estimating equivalent dipoles to show the multi-sourced brain activities and analyse directed transinformation through time series of dipoles with three-dimensional display. First, we estimated the equivalent dipoles from evoked potentials via a three-layered concentric spherical model and two-dipole estimation. Second, we analysed the directed transinformation between two time series of the first and second dipole moments by use of the derived two dipole positions and moments as the loci and magnitude of brain activities. Therefore, we obtained bi-directional information flow between the neuronal activities localized in three-dimensional space of the brain with respect to 21-ch somatosensory evoked potentials (SEPs). Our preliminary results can be interpreted as showing that the information, calculated based on our method, flows from the first dipole cluster located in the thalamus, to the second dipole cluster located in the somatosensory area. This does not go against the neurophysiological knowledge of SEPs that the activities move from the thalamus to the somatosensory area. With the above, we show the potential possibility of realizing the elucidation of causal relationships.

Action Potentials↗

PubMed2003

Framework for assessing causality in disease management programs: principles.

To credibly state that a disease management (DM) program "caused" a specific outcome it is required that metrics observed in the DM population be compared with metrics that would have been expected in the absence of a DM intervention. That requirement can be very difficult to achieve, and epidemiologists and others have developed guiding principles of causality by which credible estimates of DM impact can be made. This paper introduces those key principles. First, DM program metrics must be compared with metrics from a "reference population." This population should be "equivalent" to the DM intervention population on all factors that could independently impact the outcome. In addition, the metrics used in both groups should use the same defining criteria (ie, they must be "comparable" to each other). The degree to which these populations fulfill the "equivalent" assumption and metrics fulfill the "comparability" assumption should be stated. Second, when "equivalence" or "comparability" is not achieved, the DM managers should acknowledge this fact and, where possible, "control" for those factors that may impact the outcome(s). Finally, it is highly unlikely that one study will provide definitive proof of any specific DM program value for all time; thus, we strongly recommend that studies be ongoing, at multiple points in time, and at multiple sites, and, when observational study designs are employed, that more than one type of study design be utilized. Methodologically sophisticated studies that follow these "principles of causality" will greatly enhance the reputation of the important and growing efforts in DM.

Bias↗

PubMed1999

Causal criteria in nutritional epidemiology.

Making nutrition recommendations involves complex judgments about the balance between benefits and risks associated with a nutrient or food. Causal criteria are central features of such judgments but are not sufficient. Other scientific considerations include study designs, statistical tests, bias, confounding, and measurement issues. At a minimum, the set of criteria includes consistency, strength of association, dose response, plausibility, and temporality. The current practice, methods, and theory of causal inference permit flexibility in the choice of criteria, their relative priority, and the rules of inference assigned to them. Our approach is as follows. Consistency across study designs is compelling when the studies are of high quality and are not subject to biases. A statistically significant risk estimate with a > 20% increase or decrease in risk is considered a positive finding. A statistically significant linear or otherwise regularly increasing trend reinforces the judgment in favor of a recommendation. A plausible hypothesis likewise reinforces a recommendation, although the rules of inference for biological evidence are highly variable and depend on the situation. Temporality is, for nutrition recommendations, more a consideration of the extent to which a dietary factor affects disease onset or progression. Evidence supporting these criteria provides a strong basis for making a nutrition recommendation, given due consideration of the balance between presumed benefits and presumed harms. Recommendations should make clear their breadth of application; a narrow recommendation involves a single disease or condition whereas a broad recommendation involves all relevant diseases or conditions.

Causality↗