Causal inference under interference with prognostic scores for dynamic group therapy studies

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Bing Han ◽  
Susan M. Paddock ◽  
Lane Burgette
Keyword(s):  
Group Therapy ◽  
Causal Inference ◽  
Patient Outcomes ◽  
Causal Effect ◽  
Research Question ◽  
Prognostic Score ◽  
Prognostic Scores ◽  
Model Framework ◽  
Dynamic Therapy ◽  
Session Attendance

Abstract Group therapy is a common treatment modality for behavioral health conditions. Patients often enter and exit groups on an ongoing basis, leading to dynamic therapy groups. Examining the effect of high versus low session attendance on patient outcomes is a research question of interest. However, there are several challenges to identifying causal effects in this setting, including the lack of randomization, interference among patients, and the interrelatedness of patient participation. Dynamic therapy groups motivate a unique causal inference scenario, as the treatment statuses are completely defined by the patient attendance record for the therapy session, which is also the structure inducing interference. We adopt the Rubin causal model framework to define the causal effect of high versus low session attendance of group therapy at both the individual patient and peer levels. We propose a strategy to identify individual, peer, and total effects of high attendance versus low attendance on patient outcomes by the prognostic score stratification. We examine performance of our approach via simulation and apply it to data from a group cognitive behavioral therapy trial for treating depression among patients in a substance use disorders treatment setting.

A Survey on Causal Inference

2021 ◽  
Vol 15 (5) ◽  
pp. 1-46
Author(s):  
Liuyi Yao ◽  
Zhixuan Chu ◽  
Sheng Li ◽  
Yaliang Li ◽  
Jing Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Research Direction ◽  
Estimation Methods ◽  
Potential Outcome ◽  
Outcome Framework ◽  
Benchmark Datasets ◽  
Inference Methods

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

Causal inference via string diagram surgery

2021 ◽  
pp. 1-22
Author(s):  
Bart Jacobs ◽  
Aleks Kissinger ◽  
Fabio Zanasi
Keyword(s):  
Causal Inference ◽  
Probabilistic Reasoning ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Causal Effects ◽  
Stochastic Matrices ◽  
Counterfactual Reasoning ◽  
Special Cases ◽  
String Diagram ◽  
Set Up

Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.

Why Causal Inference Matters to Nurses: The Case of Nurse Staffing and Patient Outcomes

2016 ◽  
Vol 21 (2) ◽  
Author(s):  
Deena Costa ◽  
Olga Yakusheva
Keyword(s):  
Causal Inference ◽  
Patient Outcomes ◽  
Nurse Staffing ◽  
Future Research ◽  
Nursing Research ◽  
Cross Sectional ◽  
Meaningful Improvement ◽  
Causal Pathways ◽  
Causal Pathway ◽  
Study Designs

Since the early 1990s researchers have steadily built a broad evidence base for the association between nurse staffing and patient outcomes. However, the majority of the studies in the literature employ designs that are unable to robustly examine causal pathways to meaningful improvement in patient outcomes. A focus on causal inference is essential to moving the field of nursing research forward, and as part of the essential skill-set for all nurses as consumers of research. In this article, we aim to describe the importance of causal inference in nursing research and discuss study designs that are more likely to produce causal findings. We first review the conceptual framework supporting this discussion and then use selected examples from the literature, typifying three key study designs – cross-sectional, longitudinal, and randomized control trials (RCTs). The discussion will illustrate strengths and limitation of existing evidence, focusing on the causal pathway between nurse staffing and outcomes. The article conclusion considers implications for future research.

Volunteer Adolescents in Adolescent Group Therapy

1976 ◽  
Vol 129 (5) ◽  
pp. 407-413 ◽  
Author(s):  
Stuart Fine ◽  
Graham Knight-Webb ◽  
Karen Breau
Keyword(s):  
Group Therapy ◽  
Group Process ◽  
Group Leaders ◽  
Adolescent Group Therapy ◽  
Adolescent Group ◽  
Knowledge And Skills ◽  
The Third ◽  
Session Attendance ◽  
New Knowledge ◽  
Better Than

SummaryThe effects on patients, volunteers and staff of using volunteer adolescents in adolescent group therapy are examined. From 40 candidates eight volunteers were selected and oriented to the group process. Four volunteers and four patients were placed in each of two groups, and eight patients were placed in the third group. Patients and volunteers were aware of their identities in the first group session. Attendance in the groups with volunteers was better than in the group without. The volunteers themselves gained new knowledge and skills, and their presence was even helpful to the group leaders.

You Can’t Drive a Car With Only Three Wheels

2019 ◽  
Vol 188 (9) ◽  
pp. 1682-1685 ◽  
Author(s):  
Hailey R Banack
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Causal Effects ◽  
Measurement Bias ◽  
Exposure Misclassification ◽  
Unmeasured Confounding ◽  
Obstetrical Care ◽  
The Impact ◽  
Fundamental Requirement

Abstract Authors aiming to estimate causal effects from observational data frequently discuss 3 fundamental identifiability assumptions for causal inference: exchangeability, consistency, and positivity. However, too often, studies fail to acknowledge the importance of measurement bias in causal inference. In the presence of measurement bias, the aforementioned identifiability conditions are not sufficient to estimate a causal effect. The most fundamental requirement for estimating a causal effect is knowing who is truly exposed and unexposed. In this issue of the Journal, Caniglia et al. (Am J Epidemiol. 2019;000(00):000–000) present a thorough discussion of methodological challenges when estimating causal effects in the context of research on distance to obstetrical care. Their article highlights empirical strategies for examining nonexchangeability due to unmeasured confounding and selection bias and potential violations of the consistency assumption. In addition to the important considerations outlined by Caniglia et al., authors interested in estimating causal effects from observational data should also consider implementing quantitative strategies to examine the impact of misclassification. The objective of this commentary is to emphasize that you can’t drive a car with only three wheels, and you also cannot estimate a causal effect in the presence of exposure misclassification bias.

Causal inference for clinicians

2019 ◽  
Vol 24 (3) ◽  
pp. 109-112 ◽  
Author(s):  
Steven D Stovitz ◽  
Ian Shrier
Keyword(s):  
Decision Making ◽  
Causal Inference ◽  
Clinical Decision Making ◽  
Causal Effect ◽  
Treatment Decision ◽  
Clinical Decision ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Treatment Decision Making ◽  
Inference Methods

Evidence-based medicine (EBM) calls on clinicians to incorporate the ‘best available evidence’ into clinical decision-making. For decisions regarding treatment, the best evidence is that which determines the causal effect of treatments on the clinical outcomes of interest. Unfortunately, research often provides evidence where associations are not due to cause-and-effect, but rather due to non-causal reasons. These non-causal associations may provide valid evidence for diagnosis or prognosis, but biased evidence for treatment effects. Causal inference aims to determine when we can infer that associations are or are not due to causal effects. Since recommending treatments that do not have beneficial causal effects will not improve health, causal inference can advance the practice of EBM. The purpose of this article is to familiarise clinicians with some of the concepts and terminology that are being used in the field of causal inference, including graphical diagrams known as ‘causal directed acyclic graphs’. In order to demonstrate some of the links between causal inference methods and clinical treatment decision-making, we use a clinical vignette of assessing treatments to lower cardiovascular risk. As the field of causal inference advances, clinicians familiar with the methods and terminology will be able to improve their adherence to the principles of EBM by distinguishing causal effects of treatment from results due to non-causal associations that may be a source of bias.

Glasgow Prognostic Score Is Superior to Other Inflammation-Based Scores in Predicting Survival of Diffuse Large B-Cell Lymphoma

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1454-1454
Author(s):  
Xiaoxiao Hao ◽  
Yongqiang Wei ◽  
Fen Huang ◽  
Xiaolei Wei ◽  
Yuankun Zhang ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Lymphoma ◽  
Prognostic Score ◽  
Univariate Analysis ◽  
Glasgow Prognostic Score ◽  
B Cell Lymphoma ◽  
Prognostic Scores ◽  
Lymphocyte Ratio ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Inflammation-based prognostic scores, such as the glasgow prognostic score (GPS), prognostic index(PI), prognostic nutritional index(PNI), neutrophil lymphocyte ratio(NLR), platelet lymphocyte ratio(PLR) was related to survival in many solid tumors. Recent study showed that GPS can be used to predict outcome in diffuse large B-cell lymphoma(DLBCL). However other inflammation related scores had not been reported in DLBCL, and it also remained unknown which of them was more useful to evaluate the survival in DLBCL. In this retrospective study, a number of 252 newly diagnosed and histologically proven DLBCL patients from January 2003 to December 2014 were included. An elevated GPS, PI, NLR, PNI and PLR were all associated with decreased overall survival(p=0.000, p=0.000, p=0.006, p=0.001 and p=0.001, respectively) and event-free survival (p=0.000, p=0.000, p=0.011, p=0.001 and p=0.009, respectively) in univariate analysis. Multivariate analysis indicated that GPS(RR=1.768, 95%CI=1.043-3.000, p =0.034) remained an independent prognostic predictor in DLBCL. The area under the curve of GPS (0.735, 95%CI=0.645-0.824) was greater than that of PI(0.710, 95%CI=0.621-0.799), PNI(0.600, 95%CI=0.517-0.683), NLR(0.572, 95%CI=0.503-0.642), and PLR(0.599, 95%CI=0.510-0.689) by Harrell's C-statistics. Especially in the DLBCL patients treated with R-CHOP, GPS also remained the most powerful inflammation-based prognostic score when comparing with PI, NLR, PNI and PLR (p=0.004, p=0.000, respectively for OS and EFS). In conclusion, these results indicate that Inflammation-based prognostic scores such as GPS, PI, NLR, PNI and PLR can be used to evaluate the outcome in DLBCL patients. Among them, GPS is the most powerful tool in predicting survival in DLBCL patients, even in the rituximab era. Disclosures No relevant conflicts of interest to declare.

scholarly journals Consistency Test between Scoring Systems for Predicting Outcomes of Chronic Myeloid Leukemia in a Saudi Population Treated with Imatinib

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Haneen R. Banjar ◽  
Enaam Alsobhi
Keyword(s):  
Chronic Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Quantitative Polymerase Chain Reaction ◽  
Hematological Parameters ◽  
Prognostic Score ◽  
Scoring Systems ◽  
Transcript Level ◽  
Prognostic Scores ◽  
Molecular Response ◽  
Risk Categories

Inconsistency in prognostic scores occurs where two different risk categories are applied to the same chronic myeloid leukemia (CML) patient. This study evaluated common scoring systems for identifying risk groups based on patients’ molecular responses to select the best prognostic score when conflict prognoses are obtained from patient profiles. We analyzed 104 patients diagnosed with CML and treated at King Abdulaziz Medical City, Saudi Arabia, who were monitored for major molecular response (achieving a BCR-ABL1 transcript level equal to or less than 0.1%) by Real-Time Quantitative Polymerase Chain Reaction (RQ-PCR), and their risk profiles were identified using Sokal, Hasford, EUTOS, and ELTS scores based on the patients’ clinical and hematological parameters at diagnosis. Our results found that the Hasford score outperformed other scores in identifying risk categories for conflict groups, with an accuracy of 63%.

Predicting complete cytogenetic response and subsequent progression-free survival in 2060 patients with CML on imatinib treatment: the EUTOS score

Blood ◽  
2011 ◽  
Vol 118 (3) ◽  
pp. 686-692 ◽  
Author(s):  
Joerg Hasford ◽  
Michele Baccarani ◽  
Verena Hoffmann ◽  
Joelle Guilhot ◽  
Susanne Saussele ◽  
...  
Keyword(s):  
High Risk ◽  
Kinase Inhibitors ◽  
Prognostic Score ◽  
Progression Free Survival ◽  
Risk Groups ◽  
High Risk Group ◽  
Prognostic Scores ◽  
Imatinib Treatment ◽  
Free Survival ◽  
Eutos Score

AbstractThe outcome of chronic myeloid leukemia (CML) has been profoundly changed by the introduction of tyrosine kinase inhibitors into therapy, but the prognosis of patients with CML is still evaluated using prognostic scores developed in the chemotherapy and interferon era. The present work describes a new prognostic score that is superior to the Sokal and Euro scores both in its prognostic ability and in its simplicity. The predictive power of the score was developed and tested on a group of patients selected from a registry of 2060 patients enrolled in studies of first-line treatment with imatinib-based regimes. The EUTOS score using the percentage of basophils and spleen size best discriminated between high-risk and low-risk groups of patients, with a positive predictive value of not reaching a CCgR of 34%. Five-year progression-free survival was significantly better in the low- than in the high-risk group (90% vs 82%, P = .006). These results were confirmed in the validation sample. The score can be used to identify CML patients with significantly lower probabilities of responding to therapy and survival, thus alerting physicians to those patients who require closer observation and early intervention.

scholarly journals Modeling utilitarian‐hedonic dual mediation (UHDM) in the purchase and use of games

2013 ◽  
Vol 23 (2) ◽  
pp. 229-256 ◽  
Author(s):  
Robert Davis ◽  
Bodo Lang ◽  
Neil Gautam
Keyword(s):  
Structural Equation ◽  
Causal Effect ◽  
Research Question ◽  
Role Playing ◽  
Hedonic Consumption ◽  
Content Type ◽  
Role Playing Game ◽  
Game Context ◽  
The Relationship ◽  
Practical Implications

PurposeIt is assumed that consumers consume games to experience hedonic and utilitarian value. However, there is no conceptual model or empirical evidence that supports this hypothesis in the game context or clarifies whether these consumption values have dual mediated or individual effects. Therefore, the purpose of this research is to model the relationship between hedonic and utilitarian consumption and game purchase and usage.Design/methodology/approachThis research question is answered through two studies. In Study One, qualitative interviews with 18 gamers were implemented to explore the relationship between hedonic and utilitarian consumption and, game purchase and usage behaviour. In Study Two, we surveyed 493 consumers and conducted confirmatory factor analysis and structural equation modelling across four game types to model this relationship.FindingsThe paper concludes that hedonic rather than utilitarian consumption positively impacts purchase and usage. Support was also found for the utilitarian‐hedonic dual mediation model (UHDM). Therefore, utilitarian consumption has an indirect causal effect on game purchase or usage through hedonic consumption.Practical implicationsGame development for consumers online, on wireless devices and on consoles should place greater emphasis on the practical implications of hedonic consumption. Attention could be focused on perceived enjoyment, self‐concept, self‐congruity and self‐efficacy as the primary drivers of use and purchase. Practical solutions should also be developed to develop the UHDM effect.Originality/valueThis is the first paper in the game context to explore and model the relationship between hedonic, utilitarian consumption and the UHDM effect on game purchase and usage. This paper is also unique because it provides results across four game groups: all games (ALL), Sports/Simulation/Driving (SSD), Role Playing Game/Massively Multiplayer Online Role‐Playing Game Strategy (RPG), and Action/Adventure/Fighting (AAF).

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