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PubMed2006

Polydesigns and causal inference.

In an increasingly common class of studies, the goal is to evaluate causal effects of treatments that are only partially controlled by the investigator. In such studies there are two conflicting features: (1) a model on the full cohort design and data can identify the causal effects of interest, but can be sensitive to extreme regions of that design's data, where model specification can have more impact; and (2) models on a reduced design (i.e., a subset of the full data), for example, conditional likelihood on matched subsets of data, can avoid such sensitivity, but do not generally identify the causal effects. We propose a framework to assess how inference is sensitive to designs by exploring combinations of both the full and reduced designs. We show that using such a "polydesign" framework generates a rich class of methods that can identify causal effects and that can also be more robust to model specification than methods using only the full design. We discuss implementation of polydesign methods, and provide an illustration in the evaluation of a needle exchange program.

Biometry↗
PubMed2026

Multi-omics causal inference of childhood asthma triggered by ambient particulate matter.

BACKGROUND: The causal impact of fine particulate matter (PM2.5), an established environmental risk factor, on childhood asthma and its biological mechanisms remain to be elucidated. The objective of the present study was to evaluate the causal association between PM2.5 and childhood asthma and to dissect the mediating role of plasma proteins through a multi-omics integrated Mendelian randomisation (MR) framework. METHODS: Two-sample MR was performed on large-scale genome-wide association data to estimate the causal effect of PM2.5 on childhood asthma. Genes commonly associated with PM2.5 and childhood asthma were screened by transcriptome-wide association study (TWAS) and subjected to enrichment analyses and MR. Mediator proteins were identified by two-step MR. Potential adverse effects were scanned by phenome-wide MR (Phe-MR). RESULTS: MR revealed a significant positive causal effect of PM2.5 on childhood asthma (OR=1.897, 95% CI: 1.063-3.388, p=0.030). TWAS highlighted 70 genes co-expressed in PM2.5 and childhood asthma that were enriched in inflammatory pathways such as lysosome- and leukocyte-mediated immunity. MEAF6 was validated as a protective gene and RNF40 as a risk gene for childhood asthma. Two-step MR identified FUT10 as a positive mediator mediating 19.3% of the causal effect, and CD200 and MANBA as negative mediator proteins. Phe-MR indicated the association of these genes and proteins with multiple other diseases, implying possible adverse effects from therapeutic intervention. CONCLUSION: Long-term PM2.5 exposure is causally linked to childhood asthma with MEAF6, RNF40, CD200, MANBA and FUT10 identified as key molecules. The study provides new evidence for the biological mechanisms linking PM2.5 to childhood asthma.

Journal Article↗
PubMedDate not supplied

[Regression methods and causal inference: structural equations models].

The estimate of correlations among observed outcomes is crucial in biomedical research, especially when the aim of the study is to infer, from the magnitude of these correlations, the causal influence of certain, sometimes latent, factors. In such situations, a typical regression approach, known as "structural equation models" (SEM), which was introduced in the 1970s, becomes significant. These models allow hypotheses to be formulated quite clearly, thanks to some explicit and rigorous graphical representations, on which the "path analysis" is based. SEM, which were initially used in economics, have in the past decade been applied in a wide variety of fields, especially in genetic epidemiology. It's in this field that SEM are extraordinarily effective, representing a simple yet powerful means of estimating the contribution of genes and the environment to the phenotypic expression of a given disease. To this end, data on twins are particularly useful, and in this case the correlation between the outcomes describes the extent of similarity of the twin phenotypes. From this standpoint, SEM undoubtedly constitute one of the most promising statistical tools for family studies and quantitative genetic research. The method can be easily extended to traditional epidemiology, and some interesting applications have already been developed in occupational and social epidemiology. In this paper, we describe in detail the SEM approach and discuss the use of these models in genetic epidemiology, using twin studies as an example. We also discuss the application of SEM in fields other than genetic research.

Causality↗
PubMed2006

Causal inference for non-mortality outcomes in the presence of death.

Evaluation of the causal effect of a baseline exposure on a morbidity outcome at a fixed time point is often complicated when study participants die before morbidity outcomes are measured. In this setting, the causal effect is only well defined for the principal stratum of subjects who would live regardless of the exposure. Motivated by gerontologic researchers interested in understanding the causal effect of vision loss on emotional distress in a population with a high mortality rate, we investigate the effect among those who would live both with and without vision loss. Since this subpopulation is not readily identifiable from the data and vision loss is not randomized, we introduce a set of scientifically driven assumptions to identify the causal effect. Since these assumptions are not empirically verifiable, we embed our methodology within a sensitivity analysis framework. We apply our method using the first three rounds of survey data from the Salisbury Eye Evaluation, a population-based cohort study of older adults. We also present a simulation study that validates our method.

Aged↗
PubMed2000

[Achievement of causal inference in the social medicine in Japan].

As a condition to achieving an agreement of recognition on the causal relationship in medicine, we firstly explained Hume's problem and counterfactual model. We, however, emphasized that we believe in the existence of causality on medical issues in our daily lives. Therefore, we illustrated conditions when we usually believe in causality. On the other hand, we criticized two well-known key phrases, "lack of mechanism in epidemiology" and "black box in epidemiology", which have often been used in Japan for skeptic viewpoints against epidemiologic methods even if epidemiology is often used to elucidate a causal effect in medicine in the world. We emphasized that a priori determinations of levels for inference of mechanism is necessary. And, the level and feature of mechanism should be defined in concrete expressions. After explanation of these basic concepts, we mentioned a classic view on specific diseases and non-specific diseases which have not been sufficiently discussed enough yet in Japan. As an example, we used the statements in the Japanese Compensation Law for the Health Effect by Environmental Pollution. In Japan, the classification of these diseases has been confused with that between manifestational criteria of diseases and causal criteria of them. We described the basic concepts to illustrate the causal relationship between non-specific disease and its exposure by using attached figures. Actually, we cannot recognize disease occurrence as a specific disease for several reasons. We indicated that we can recognize the magnitude of effect by causal relationships in medicine as a quantitative continuous variable.

Causality↗
PubMed1987

A simplified logic of causal inference.

This paper provides a simplified method for evaluating the evidence in favour of a causal claim. It analyses the evidence bearing upon such a claim in terms of two questions: Do the putative cause and effect covary, and can alternative non-causal explanations of the relationship be ruled out? The different research designs for assessing covariation are outlined, as are the ways in which these designs permit a researcher to decide between alternative explanations of the relationship.

Depressive Disorder↗
PubMed1998

Gulf War veterans' illnesses: a case study in causal inference.

The objective of this study was to determine from the published epidemiological literature whether there is evidence for a causal association between service in the Gulf War and illness in U.S. veterans. Eleven published studies were analyzed using standardized epidemiologic criteria for assessing causality. A consistent association was found between deployment to the Gulf and self-reports of symptoms. No consistency was seen in physical findings or laboratory results. Strength of association varied with different study designs. Dose-response information is limited, because of lack of quantitative data on exposures. Biological plausibility varies for different risk factors. Specificity of association is not seen. Frequency of self-reported symptoms is increased in U.S. Gulf War veterans compared to other veterans of the same era, but specific causes of illnesses cannot be ascertained. Major gaps in data that impeded this analysis include (1) lack of objective data on specific environmental exposures (2) lack of baseline health assessments, and (3) lack of objective measures of post-deployment health status. In future deployment of U.S. troops, accurate exposure and health data will be needed if the causes of subsequent illnesses are to be accurately assessed.

Environmental Exposure↗
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↗
PubMed2002

Principal stratification in causal inference.

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

Child↗
PubMed2026

The role of ferroptosis in juvenile idiopathic arthritis: Causal inference and mediation by immune phenotypes.

This study employed a bidirectional 2-step, two-sample Mendelian randomization approach to investigate the causal relationships between ferroptosis-related genes and juvenile idiopathic arthritis (JIA) and to explore the mediating role of immune cells. Ferroptosis genes were identified from the deCODE database and matched with protein quantitative trait locus data as exposures to evaluate their causal effects on JIA, while immune cell traits were similarly assessed. For genes showing positive Mendelian randomization results, further analyses were conducted to determine whether immune cells mediated the effects on JIA, with mediation analysis performed only in the presence of causal associations. Data were sourced from the GWAS, FerrDb, and other public repositories. Nine ferroptosis-related genes were found to have causal links with JIA: HSPB1, DECR1, LIFR, and CTSB increased JIA risk, whereas PIEZO1, DPP4, BID, and others were protective. Forty immune cell traits were also causally associated with JIA. Mediation analysis revealed that several immune cells, including CD127- CD8+ T cells, partially mediated the genetic effects, with mediation proportions reaching up to 18.6%. Collectively, these results point to a ferroptosis-immune-JIA axis, suggesting that ferroptosis-related genes contribute to JIA pathogenesis through immune cell mediation and offering new mechanistic insights and potential therapeutic targets.

Arthritis, Juvenile↗