Sufficient-component Cause Research Articles

The paradox of predation studies.

Understanding the causal relationships that contribute to mortality in populations is a priority for epidemiology, animal husbandry and ecology. Of all the sources of mortality in nature, predation is perhaps the most important, while simultaneously being one of the most difficult to study and understand. In this opinion piece, we use the epidemiological concept of the sufficient-component cause model to outline why we believe that predation studies often misrepresent predators as sufficient cause of death (or natural mortality) in ecological studies. This is pivotal in conservation biology because such studies have often led to demands for predator removal throughout the world. We use the sufficient-component cause model to illustrate the paradox that multiple studies, each studying singular putative causes of mortality (including predation), will sum to more than 100% mortality when added together. We suggest that the sufficient-component framework should be integrated into both fundamental and applied ecology to better understand the role of predators in natural ecosystems.

A historical note about the sufficient cause model with special consideration of the work of Max Verworn 1912

The aim of this paper is to present the historical roots of the sufficient component cause model, which is very well known in epidemiology. I have analyzed Max Verworn's writings on the description of the sufficient component cause model. Verworn introduced a precursor of the sufficient component cause model as early as 1912, possibly inspired by Ernst Mach. He argued for the abandonment of the concept of "cause" (singular). Instead, he preferred the term "conditions." Unlike Karl Pearson, Verworn was not opposed to causal considerations. However, according to Verworn, every process or state is determined by numerous conditions and not by a single factor or "cause." Instead of speaking of causalism, Verworn preferred to speak of conditionalism. The earliest description of the concept of the sufficient component cause model known in the epidemiological literature since 1976 dates back at least to 1912.

Population attributable fraction based on marginal sufficient component cause model for mediation settings

Population attributable fraction (PAF), defined as the proportion of the occurrence of a disease which will be reduced by eliminating risk factors in a population, is one of the most common measurements for evaluating the benefit of a health-related policy in epidemiologic study. In this article, we propose an alternative PAF defined based on sufficient cause framework, which decompose the occurrence of a disease into several pathways including mediation and mechanistic interaction. We propose a formal statistical definition and regression-based estimator for PAF based on sufficient cause framework within mediation settings. Under monotonicity assumption, the proposed method can decompose the occurrence of a disease into nine PAFs corresponding to all types of mechanisms attributing to exposure and the mediator, including the portion attributing to exposure directly, to mediator, to indirect effect through mediator, to the mechanistic interaction, to both of mediation and interaction, and to none of exposure or mediator. We apply the proposed method to explore the mechanism of a hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) mediated by and/or interacted with alanine aminotransferase (ALT) and hepatitis B virus (HBV). When treating ALT as mediator, 56.77% of diseased subjects can be attributable to either HCV or abnormal ALT. When treating HBV as mediator, HCC is mainly induced by an exogenous high HBV viral load directly. The proposed method can identify the impact of exposure and pathway effects, and benefit to allocate the resources on intervention strategies.

Classical causation research practices and sufficient-component cause model – Appraisal and pitfalls

The sufficient-component cause model is one of the most discussed recent theories in disease causation. Despite some limitations, it seems one of the best theories to explain real world phenomena. It seems that this model has many implications on the current classical research methods related to disease causation. However, these implications have not been sufficiently explored. Therefore, based on the sufficient-component cause model, this paper aims to review, identify and rectify pitfalls in the classical causation research practices, especially those related to cohort study design.

Marginal Sufficient Component Cause Model: An Emerging Causal Model With Merits?

For decades, the sufficient cause model and the counterfactual model have shaped our understanding of causation in biomedical science, and the link between these two models has enabled us to obtain a deeper understanding of causality. Recently, a new causal model-the marginal sufficient component cause model-was proposed and applied in the context of interaction or mediation. The proponents of this model have emphasized its utility in visualizing the presence of "agonism" (a subtype of mechanistic interaction) in the counterfactual framework, claiming that the concept of agonism has not been clearly defined in causal inference and that agonistic interaction cannot be visualized by the conventional sufficient cause model. In this article, we illustrate that careful scrutiny based on the conventional sufficient cause model yields further insights into the concept of agonism in a more biologic sense. We primarily focus on the following three points: (1) "agonism" defined in the counterfactual model can be visualized as sets of sufficient causes in the conventional sufficient cause model; (2) although the so-called independent competing assumption or no redundancy assumption may seem irrelevant in the marginal sufficient component cause model, researchers do need to assume that potential completion times of relevant marginal sufficient causes differ; and (3) possibly differing potential completion times of marginal sufficient causes cannot be discerned until their hidden mechanistic paths are considered in the conventional sufficient cause model. In this rapidly progressing field of research, decades after its introduction, the sufficient cause model retains its worth.

Causality and initiation of alcohol control policy. A response to Allamani.

In a recent commentary, Allamani asked how one can establish causality in epidemiological research, and specifically about causality as it relates to alcohol control policy. Epidemiology customarily uses a sufficient-component cause model, where a sufficient cause for an outcome is determined by a set of minimal conditions and events that inevitably produce the stated outcome. While this model is theoretically clear, its operationalisation often involves probabilistic elements. Recent advances in agent-based modelling may improve operationalisation. The implications for alcohol control policy from this model are straightforward: the so-called alcohol-attributable fraction denotes the cases of morbidity or mortality which would not have happened in the absence of alcohol use.

Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking

The nine Bradford Hill (BH) viewpoints (sometimes referred to as criteria) are commonly used to assess causality within epidemiology. However, causal thinking has since developed, with three of the most prominent approaches implicitly or explicitly building on the potential outcomes framework: directed acyclic graphs (DAGs), sufficient-component cause models (SCC models, also referred to as ‘causal pies’) and the grading of recommendations, assessment, development and evaluation (GRADE) methodology. This paper explores how these approaches relate to BH’s viewpoints and considers implications for improving causal assessment. We mapped the three approaches above against each BH viewpoint. We found overlap across the approaches and BH viewpoints, underscoring BH viewpoints’ enduring importance. Mapping the approaches helped elucidate the theoretical underpinning of each viewpoint and articulate the conditions when the viewpoint would be relevant. Our comparisons identified commonality on four viewpoints: strength of association (including analysis of plausible confounding); temporality; plausibility (encoded by DAGs or SCC models to articulate mediation and interaction, respectively); and experiments (including implications of study design on exchangeability). Consistency may be more usefully operationalised by considering an effect size’s transportability to a different population or unexplained inconsistency in effect sizes (statistical heterogeneity). Because specificity rarely occurs, falsification exposures or outcomes (i.e., negative controls) may be more useful. The presence of a dose-response relationship may be less than widely perceived as it can easily arise from confounding. We found limited utility for coherence and analogy. This study highlights a need for greater clarity on BH viewpoints to improve causal assessment.

Six-way decomposition of causal effects: Unifying mediation and mechanistic interaction.

The sufficient component cause (SCC) model and counterfactual model are two common methods for causal inference, each with their own advantages: the SCC model allows the mechanistic interaction to be detailed, whereas the counterfactual model features a systemic framework for quantifying causal effects. Hence, integrating the SCC and counterfactual models may facilitate the conceptualization of causation. Based on the marginal SCC (mSCC) model, we propose a novel counterfactual mSCC framework that includes the steps of definition, identification, and estimation. We further propose a six-way effect decomposition for assessing mediation and the mechanistic interaction. The results demonstrate that when all variables are binary, the six-way decomposition is an extension of four-way decomposition and that without agonism, the six-way decomposition is reduced to four-way decomposition. To illustrate the utility of the proposed decomposition, we apply it to a Taiwanese cohort to examine the mechanism of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) with liver inflammation measured by alanine aminotransferase (ALT) as a mediator. Among the HCV-induced HCC cases, 62.27% are not explained by either mediation or interaction in relation to ALT; 9.32% are purely mediated by ALT; 16.53% are caused by the synergistic effect of HCV and ALT; and 9.31% are due to the mediated synergistic effect of HCV and ALT. In summary, we introduce an SCC model framework based on counterfactual theory and detail the required identification assumptions and estimation procedures; we also propose a six-way effect decomposition to unify mediation and mechanistic interaction analyses.

New approaches to disease causation research based on the sufficient-component cause model.

Up to date, the sufficient-component cause model seems to be a theoretical framework for disease causation in epidemiology, and its implications in epidemiological research methods is currently still limited. Recently, pitfalls in current epidemiological research methods were addressed based on the sufficient-component cause model; hence, new research approaches are needed as alternatives. Therefore, this paper aims to review and suggest new epidemiological methods used to assess disease causation. A new approach was discussed to identify potential mechanisms of disease occurrence which may be useful for risk prediction and disease prevention. In addition, a novel “exposed case-control” design was introduced to identify potential component causes. Furthermore, this paper suggested a new approach of conducting a systematic review/meta-analysis related to causation studies.Significance for public healthThis paper presented several novel epidemiological research approaches based on the sufficient-component cause model. The new approaches are of great importance in disease prediction and prevention, and precision medicine. Therefore, the future use of the suggested new epidemiological methods in the area of medicine and public health is promising.

A Graphical Description of Partial Exchangeability.

Partial exchangeability is sufficient for the identification of some causal effects of interest. Here we review the use of common graphical tools and the sufficient component cause model in the context of partial exchangeability. We illustrate the utility of single world intervention graphs (SWIGs) in depicting partial exchangeability and provide an illustrative example of when partial exchangeability might hold in the absence of complete exchangeability.

Stochastic approach for mechanistic interaction under longitudinal studies with noninformative right censoring.

Characterizing the mechanistic interactions between exposures and diseases is one of the most critical issues in epidemiologic studies. Previous studies have proposed a stochastic sufficient component cause framework, under which each sufficient cause is treated as a stochastic process instead of a time-invariant random variable. However, different types of mechanistic interactions such as synergism and agonism cannot be further identified. In this study, we proposed a stochastic marginal sufficient component cause model to conceptualize and identify agonism and synergism by exploiting the additional information. We further provided six approaches to identify and estimate agonism and synergism based on an additive hazard model and a complementary log model. Researchers can easily adjust confounding factors by including appropriate covariates into a regression model. Simulations have proven that approaches under three models are all valid tests. The power of an additive hazard model increases as the total follow-up time increases and is higher than that of the other two models. We applied this method to a Taiwanese cohort data set to investigate the mechanistic interaction among hepatitis B and C viruses on the incidence of hepatocellular carcinoma. The hazard of people with agonistic interaction is 1.28×10-5 (95% CI: 6.97×10-6 , 1.87×10-5 ), and the cumulative hazard of those people is 7.41×10-2 (95% CI: 4.09×10-2 , 1.07×10-1 ), which is approximately 3.5 times stronger than that of synergistic interaction. The proposed method makes it possible to quantify different types of mechanistic interactions in longitudinal studies with censored data.

Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science: Concepts and Assumptions.

In this article, we conclude our 3-part series by focusing on several concepts that have proven useful for formulating causal questions and inferring causal effects. The process of causal inference is of key importance for physiologic childbirth science, so each concept is grounded in content related to women at low risk for perinatal complications. A prerequisite to causal inference is determining that the question of interest is causal rather than descriptive or predictive. Another critical step in defining a high-impact causal question is assessing the state of existing research for evidence of causality. We introduce 2 causal frameworks that are useful for this undertaking, Hill's causal considerations and the sufficient-component cause model. We then provide 3 steps to aid perinatal researchers in inferring causal effects in a given study. First, the researcher should formulate a rigorous and clear causal question. We introduce an example of epidural analgesia and labor progression to demonstrate this process, including the central role of temporality. Next, the researcher should assess the suitability of the given data set to answer this causal question. In randomized controlled trials, data are collected with the express purpose of answering the causal question. Investigators using observational data should also ensure that their chosen causal question is answerable with the available data. Finally, investigators should design an analysis plan that targets the causal question of interest. Some data structures (eg, time-dependent confounding by labor progress when estimating the effect of epidural analgesia on postpartum hemorrhage) require specific analytical tools to control for bias and estimate causal effects. The assumptions of consistency, exchangeability, and positivity may be especially useful in carrying out these steps. Drawing on appropriate causal concepts and considering relevant assumptions strengthens our confidence that research has reduced the likelihood of alternative explanations (eg bias, chance) and estimated a causal effect.

Sharp bounds on sufficient-cause interactions under the assumption of no redundancy

BackgroundSufficient-cause interaction is a type of interaction that has received much attention recently. The sufficient component cause model on which the sufficient-cause interaction is based is however a non-identifiable model. Estimating the interaction parameters from the model is mathematically impossible.MethodsIn this paper, I derive bounding formulae for sufficient-cause interactions under the assumption of no redundancy.ResultsTwo real data sets are used to demonstrate the method (R codes provided). The proposed bounds are sharp and sharper than previous bounds.ConclusionsSufficient-cause interactions can be quantified by setting bounds on them.

Complementary Log Regression for Sufficient-Cause Modeling of Epidemiologic Data.

The logistic regression model is the workhorse of epidemiological data analysis. The model helps to clarify the relationship between multiple exposures and a binary outcome. Logistic regression analysis is readily implemented using existing statistical software, and this has contributed to it becoming a routine procedure for epidemiologists. In this paper, the authors focus on a causal model which has recently received much attention from the epidemiologic community, namely, the sufficient-component cause model (causal-pie model). The authors show that the sufficient-component cause model is associated with a particular ‘link’ function: the complementary log link. In a complementary log regression, the exponentiated coefficient of a main-effect term corresponds to an adjusted ‘peril ratio’, and the coefficient of a cross-product term can be used directly to test for causal mechanistic interaction (sufficient-cause interaction). The authors provide detailed instructions on how to perform a complementary log regression using existing statistical software and use three datasets to illustrate the methodology. Complementary log regression is the model of choice for sufficient-cause analysis of binary outcomes. Its implementation is as easy as conventional logistic regression.

Excess relative risk as an effect measure in case-control studies of rare diseases.

Epidemiologists often use ratio-type indices (rate ratio, risk ratio and odds ratio) to quantify the association between exposure and disease. By comparison, less attention has been paid to effect measures on a difference scale (excess rate or excess risk). The excess relative risk (ERR) used primarily by radiation epidemiologists is of peculiar interest here, in that it involves both difference and ratio operations. The ERR index (but not the difference-type indices) is estimable in case-control studies. Using the theory of sufficient component cause model, the author shows that when there is no mechanistic interaction (no synergism in the sufficient cause sense) between the exposure under study and the stratifying variable, the ERR index (but not the ratio-type indices) in a rare-disease case-control setting should remain constant across strata and can therefore be regarded as a common effect parameter. By exploiting this homogeneity property, the related attributable fraction indices can also be estimated with greater precision. The author demonstrates the methodology (SAS codes provided) using a case-control dataset, and shows that ERR preserves the logical properties of the ratio-type indices. In light of the many desirable properties of the ERR index, the author advocates its use as an effect measure in case-control studies of rare diseases.

A novel approach for identifying causal models of complex diseases from family data.

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal-normal, normal-disease, and disease-disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene-environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene-environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.

BOOLEAN ALGEBRAS AND CLASSIFICATION OF INTERACTIONS IN SUFFICIENT-COMPONENT CAUSE MODEL

A mathematical model of the sufficient-component cause frame- work is considered based on the theories of Boolean algebra. The model consists of the space of states of a binary experiment and a set of symmetries of the ex- periment. The space of states is a Boolean algebra of n Boolean variables where n is the number of the binary causes in the experiment. The set of symmetries of the experiment is a subgroup of the group of all automorphisms of Boolean algebra of the states of experiment. This subgroup is generated by transfor- mations preserving a type of interaction. An experimenter should deduce these transformations from the peculiar properties of the experiment. Examples of such transformations are provided. Classification of interactions is obtained by the calculation of the orbits of action of the group of symmetries on the space of states of the experiment. It is shown that the classification of the interaction for the ordinary symmetries of sufficient causes is the same asreported in re- lated works. Other symmetries of the binary experiment are considered as well. It is shown that the corresponding classification of the interaction types in a binary experiment depends substantially on the symmetries of the experiment. Statistical criteria of particular types of responses are proven and the problem

Estimation of a Common Effect Parameter from Follow-Up Data When There Is No Mechanistic Interaction

In a stratified analysis, the results from different strata if homogeneity assumption is met are pooled together to obtain a single summary estimate for the common effect parameter. However, the effect can appear homogeneous across strata using one measure but heterogeneous using another. Consequently, two researchers analyzing the same data can arrive at conflicting conclusions if they use different effect measures. In this paper, the author draws on the sufficient component cause model to develop a stratified-analysis method regarding a particular effect measure, the ‘peril ratio’. When there is no mechanistic interaction between the exposure under study and the stratifying variable (i.e., when they do not work together to complete any sufficient cause), the peril ratio is constant across strata. The author presents formulas for the estimation of such a common peril ratio. Three real data are re-analyzed for illustration. When the data is consistent with peril-ratio homogeneity in a stratified analysis, researchers can use the formulas in this paper to pool the strata.

Approches d’analyse causale en épidémiologie

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, “causal models”, though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the formulation of causal hypotheses, which will be a basis for all methodological choices. Beyond this step, statistical analysis tools recently developed offer new possibilities to delineate complex relationships, in particular in life course epidemiology.

Understanding the causation of primary angle closure disease using the sufficient component cause model

Several risk factors have been associated with primary angle closure disease, but their actual role in causation of an individual case is not clear. Concept paper. No patient participation. The sufficient component cause model is briefly explained in the context of primary angle closure disease. The framework is used to conceptualize the role of individual mechanisms of disease. The possibility of personalized treatment for primary angle closure disease is discussed in this context. Qualitative concepts in disease causality may refine research and treatment in primary angle closure disease. The minimum set of conditions that are sufficient for primary angle closure disease to occur is considered the sufficient component cause model for that individual case. Described risk factors (including genes) as well as currently unknown influences play a role in the model. There may be many such models and all complementary components in any sufficient-cause model must be present for disease to occur. Interruption of any one component in that model can be used for treatment. Pupillary block is likely a component of most such models and may currently be considered a universally necessary component of these models. The sufficient component cause model can be used as a framework to explain the role of individual mechanisms of causation and treatment of primary angle closure disease. It also aids understanding of the proportion of disease due to specific causes.