A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study

When estimating the effect of treatment on HIV using data from observational studies, standard methods may produce biased estimates due to the presence of time-dependent confounders. Such confounding can be present when a covariate, affected by past exposure, is both a predictor of the future exposure and the outcome. One example is the CD4 cell count, being a marker for disease progression for HIV patients, but also a marker for treatment initiation and influenced by treatment. Fitting a marginal structural model (MSM) using inverse probability weights is one way to give appropriate adjustment for this type of confounding. In this paper we study a simple and intuitive approach to estimate similar treatment effects, using observational data to mimic several randomized controlled trials. Each 'trial' is constructed based on individuals starting treatment in a certain time interval. An overall effect estimate for all such trials is found using composite likelihood inference. The method offers an alternative to the use of inverse probability of treatment weights, which is unstable in certain situations. The estimated parameter is not identical to the one of an MSM, it is conditioned on covariate values at the start of each mimicked trial. This allows the study of questions that are not that easily addressed fitting an MSM. The analysis can be performed as a stratified weighted Cox analysis on the joint data set of all the constructed trials, where each trial is one stratum. The model is applied to data from the Swiss HIV cohort study.

An empirical examination of the integration of causal attribution and social learning theories in achievement situations

Using path analysis, the present investigation sought to clarify possible operational linkages among constructs from social learning and attribution theories within the context of a self-esteem system. Subjects were 300 undergraduate university students who completed a measure of self-esteem and indicated expectancies for success and minimal goal levels for an experimental task. After completing the task and receiving feedback about their performance, subjects completed causal attribution and self-esteem questionnaires. Results revealed gender differences in the degree and strength of the proposed relations, but not in the mean levels of the variables studied. Results suggested that the integration of social learning and attribution theories within a single conceptual model provides a better understanding of students' behaviors and self-esteem in achievement situations.

A Cox Model for Biostatistics of the Future

Professor Sir David R. Cox (DRC) is widely acknowledged as among the most important scientists of the second half of the twentieth century. He inherited the mantle of statistical science from Pearson and Fisher, advanced their ideas, and translated statistical theory into practice so as to forever change the application of statistics in many fields, but especially biology and medicine. The logistic and proportional hazards models he substantially developed, are arguably among the most influential biostatistical methods in current practice. This paper looks forward over the period from DRC's 80th to 90th birthdays, to speculate about the future of biostatistics, drawing lessons from DRC's contributions along the way. We consider "Cox's model" of biostatistics, an approach to statistical science that: formulates scientific questions or quantities in terms of parameters gamma in probability models f(y; gamma) that represent in a parsimonious fashion, the underlying scientific mechanisms (Cox, 1997); partition the parameters gamma = theta, eta into a subset of interest theta and other "nuisance parameters" eta necessary to complete the probability distribution (Cox and Hinkley, 1974); develops methods of inference about the scientific quantities that depend as little as possible upon the nuisance parameters (Barndorff-Nielsen and Cox, 1989); and thinks critically about the appropriate conditional distribution on which to base infrences. We briefly review exciting biomedical and public health challenges that are capable of driving statistical developments in the next decade. We discuss the statistical models and model-based inferences central to the CM approach, contrasting them with computationally-intensive strategies for prediction and inference advocated by Breiman and others (e.g. Breiman, 2001) and to more traditional design-based methods of inference (Fisher, 1935). We discuss the hierarchical (multi-level) model as an example of the future challanges and opportunities for model-based inference. We then consider the role of conditional inference, a second key element of the CM. Recent examples from genetics are used to illustrate these ideas. Finally, the paper examines causal inference and statistical computing, two other topics we believe will be central to biostatistics research and practice in the coming decade. Throughout the paper, we attempt to indicate how DRC's work and the "Cox Model" have set a standard of excellence to which all can aspire in the future.

Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.

On Causal Mediation Analysis with a Survival Outcome

Suppose that having established a marginal total effect of a point exposure on a time-to-event outcome, an investigator wishes to decompose this effect into its direct and indirect pathways, also know as natural direct and indirect effects, mediated by a variable known to occur after the exposure and prior to the outcome. This paper proposes a theory of estimation of natural direct and indirect effects in two important semiparametric models for a failure time outcome. The underlying survival model for the marginal total effect and thus for the direct and indirect effects, can either be a marginal structural Cox proportional hazards model, or a marginal structural additive hazards model. The proposed theory delivers new estimators for mediation analysis in each of these models, with appealing robustness properties. Specifically, in order to guarantee ignorability with respect to the exposure and mediator variables, the approach, which is multiply robust, allows the investigator to use several flexible working models to adjust for confounding by a large number of pre-exposure variables. Multiple robustness is appealing because it only requires a subset of working models to be correct for consistency; furthermore, the analyst need not know which subset of working models is in fact correct to report valid inferences. Finally, a novel semiparametric sensitivity analysis technique is developed for each of these models, to assess the impact on inference, of a violation of the assumption of ignorability of the mediator.

Longitudinal extension of myelomalacia by intramedullary and subdural hemorrhage in a canine model of spinal cord injury

BACKGROUND CONTEXT In canine intervertebral disc (IVD) extrusion, a spontaneous animal model of spinal cord injury, hemorrhage is a consistent finding. In rodent models, hemorrhage might be involved in secondary tissue destruction by biochemical mechanisms. PURPOSE This study aimed to investigate a causal association between the extents of intramedullary, subdural and epidural hemorrhage and the severity of spinal cord damage following IVD extrusion in dogs. STUDY DESIGN/SETTING A retrospective study using histologic spinal cord sections from 83 dogs euthanized following IVD extrusion was carried out. METHODS The degree of hemorrhage (intramedullary, subdural, epidural), the degree of spinal cord damage in the epicenter (white and gray matter), and the longitudinal extent of myelomalacia were graded. Associations between the extent of hemorrhage and the degree of spinal cord damage were evaluated statistically. RESULTS Intramedullary and subdural hemorrhages were significantly associated with the degree of white (p<.001/ p=.004) and gray (both p<.001) matter damage, and with the longitudinal extension of myelomalacia (p<.001/p=.005). Intriguingly, accumulation of hemorrhagic cord debris inside or dorsal to a distended and ruptured central canal in segments distant to the epicenter of the lesion was observed exhibiting a wave-like pattern on longitudinal assessment. The occurrence of this debris accumulation was associated with high degrees of tissue destruction (all p<.001). CONCLUSIONS Tissue liquefaction and increased intramedullary pressure associated with hemorrhage are involved in the progression of spinal cord destruction in a canine model of spinal cord injury and ascending or descending myelomalacia. Functional and dynamic studies are needed to investigate this concept further.

Improving residency training with the help of multisource feedback: Generation of a model of influencing factors

Background: Feedback is considered to be one of the most important drivers of learning. One form of structured feedback used in medical settings is multisource feedback (MSF). This feedback technique provides the opportunity to gain a differentiated view on a doctor’s performance from several perspectives using a questionnaire and a facilitating conversation, in which learning goals are formulated. While many studies have been conducted on the validity, reliability and feasibility of the instrument, little is known about the impact of factors that might influence the effects of MSF on clinical performance. Summary of Work: To study under which circumstances MSF is most effective, we performed a literature review on Google Scholar with focus on MSF and feedback in general. Main key-words were: MSF, multi-source-feedback, multi source feedback, and feedback each combined with influencing/ hindering/ facilitating factors, effective, effectiveness, doctors-intraining, and surgery. Summary of Results: Based on the literature, we developed a preliminary model of facilitating factors. This model includes five main factors influencing MSF: questionnaire, doctor-in-training, group of raters, facilitating supervisor, and facilitating conversation. Discussion and Conclusions: Especially the following points that might influence MSF have not yet been sufficiently studied: facilitating conversation with the supervisor, individual aspects of doctors-in-training, and the causal relations between influencing factors. Overall there are only very few studies focusing on the impact of MSF on actual and long-term performance. We developed a preliminary model of hindering and facilitating factors on MSF. Further studies are needed to better understand under which circumstances MSF is most effective. Take-home messages: The preliminary model might help to guide further studies on how to implement MSF to use it at its full potential.

Evaluation of the causal pathways through which subjective norms influence mammography intention and stage of change in a national sample of women veterans

Few studies have investigated causal pathways linking psychosocial factors to each other and to screening mammography. Conflicting hypotheses exist in the theoretic literature regarding the role and importance of subjective norms, a person's perceived social pressure to perform the behavior and his/her motivation to comply. The Theory of Reasoned Action (TRA) hypothesizes that subjective norms directly affect intention; while the Transtheoretical Model (TTM) hypothesizes that attitudes mediate the influence of subjective norms on stage of change. No one has examined which hypothesis best predicts the effect of subjective norms on mammography intention and stage of change. Two statistical methods are available for testing mediation, sequential regression analysis (SRA) and latent variable structural equation modeling (LVSEM); however, software to apply LVSEM to dichotomous variables like intention has only recently become available. No one has compared the methods to determine whether or not they yield similar results for dichotomous variables. ^ Study objectives were to: (1) determine whether the effect of subjective norms on mammography intention and stage of change are mediated by pros and cons; and (2) compare mediation results from the SRA and LVSEM approaches when the outcome is dichotomous. We conducted a secondary analysis of data from a national sample of women veterans enrolled in Project H.O.M.E. (H&barbelow;ealthy O&barbelow;utlook on the M&barbelow;ammography E&barbelow;xperience), a behavioral intervention trial. ^ Results showed that the TTM model described the causal pathways better than the TRA one; however, we found support for only one of the TTM causal mechanisms. Cons was the sole mediator. The mediated effect of subjective norms on intention and stage of change by cons was very small. These findings suggest that interventionists focus their efforts on reducing negative attitudes toward mammography when resources are limited. ^ Both the SRA and LVSEM methods provided evidence for complete mediation, and the direction, magnitude, and standard errors of the parameter estimates were very similar. Because SRA parameter estimates were not biased toward the null, we can probably assume negligible measurement error in the independent and mediator variables. Simulation studies are needed to further our understanding of how these two methods perform under different data conditions. ^

Exploring a model of skillful engagement in nursing practice

Prominent challenges facing nurse leaders are the growing shortage of nurses and the increasingly complex care required by acutely ill patients. In organizations that shortage is exacerbated by turnover and intent to leave. Unsatisfactory working conditions are cited by nurses when they leave their current jobs. Disengagement from the job leads to plateaued performance, decreased organizational commitment, and increased turnover. Solutions to these challenges include methods both to retain and to increase the effectiveness of each nurse. ^ The specific aim of this study was to examine the relationships among organizational structures thought to foster the clinical development of the nurse, with indicators of the development of clinical expertise, resulting in outcomes of positive job attitudes and effectiveness. Causal loop modeling is incorporated as a systems tool to examine developmental cycles both for an organization and for an individual nurse to look beyond singular events and investigate deeper patterns that emerge over time. ^ The setting is an academic specialty-care institution, and the sample in this cross-sectional study consists of paired data from 225 RNs and their nurse managers. Two panels of survey instruments were created based on the model's theoretical variables, one completed by RNs and the other by their Nurse Managers. The RN survey panel examined the variables of structural empowerment, magnet essentials, knowledge as identified by the Benner developmental stage, psychological empowerment, job stage, engagement, intent to leave, job satisfaction and the early recognition of patient complications. The nurse manager survey panel examined the Benner developmental stage, job stage, and overall level of nursing performance. ^ Four regression models were created based on the outcome variables. Each model identified significant organizational and individual characteristics that predicted higher job satisfaction, decreased intent to leave, more effectiveness as measured by early recognition and acting upon subtle patient complications, and better job performance. ^ Implications for improving job attitudes and effectiveness focus on ways that nursing leaders can foster a more empowering and healthy work environment. ^

Utilization behavior of the elderly in an HMO: A causal analysis

Planning and providing health care services for the elderly represents a major challenge to the health care system. One part of that challenge is the identification of those factors which determine the utilization of services by this population. The purpose of this study is to explain the use of health care services by elderly subscribers in a prepaid group health plan, using the theoretical framework developed by Andersen and Aday. The impact of the predisposing, enabling and need factors on utilization was modelled through a structural equation approach using LISREL. The data were derived from Kaiser-Permanente's Medicare Prospective Payment Project, August 1980-December 1982. Need factors, in general, were the most significant determinants of utilization, with the predisposing and enabling factors found to be secondary but necessary links in the causal chain. The model was fitted to the data from the youngest age group (65-74 years) and then evaluated for goodness of fit in the two older groups (75-84 and 85+ years). Implications of the study's findings and suggestions for further modelling the utilization behavior of the elderly are discussed. ^

Instrumental variable method for the causal relationship between soluble receptor for advanced glycation end-products (sRAGE) and risk of pancreatic cancer

This thesis project is motivated by the potential problem of using observational data to draw inferences about a causal relationship in observational epidemiology research when controlled randomization is not applicable. Instrumental variable (IV) method is one of the statistical tools to overcome this problem. Mendelian randomization study uses genetic variants as IVs in genetic association study. In this thesis, the IV method, as well as standard logistic and linear regression models, is used to investigate the causal association between risk of pancreatic cancer and the circulating levels of soluble receptor for advanced glycation end-products (sRAGE). Higher levels of serum sRAGE were found to be associated with a lower risk of pancreatic cancer in a previous observational study (255 cases and 485 controls). However, such a novel association may be biased by unknown confounding factors. In a case-control study, we aimed to use the IV approach to confirm or refute this observation in a subset of study subjects for whom the genotyping data were available (178 cases and 177 controls). Two-stage IV method using generalized method of moments-structural mean models (GMM-SMM) was conducted and the relative risk (RR) was calculated. In the first stage analysis, we found that the single nucleotide polymorphism (SNP) rs2070600 of the receptor for advanced glycation end-products (AGER) gene meets all three general assumptions for a genetic IV in examining the causal association between sRAGE and risk of pancreatic cancer. The variant allele of SNP rs2070600 of the AGER gene was associated with lower levels of sRAGE, and it was neither associated with risk of pancreatic cancer, nor with the confounding factors. It was a potential strong IV (F statistic = 29.2). However, in the second stage analysis, the GMM-SMM model failed to converge due to non- concaveness probably because of the small sample size. Therefore, the IV analysis could not support the causality of the association between serum sRAGE levels and risk of pancreatic cancer. Nevertheless, these analyses suggest that rs2070600 was a potentially good genetic IV for testing the causality between the risk of pancreatic cancer and sRAGE levels. A larger sample size is required to conduct a credible IV analysis.^

Age determination and model of sediment core RAPiD-15-4P

Greenland ice core records indicate that the last deglaciation (~7-21 ka) was punctuated by numerous abrupt climate reversals involving temperature changes of up to 5°C-10°C within decades. However, the cause behind many of these events is uncertain. A likely candidate may have been the input of deglacial meltwater, from the Laurentide ice sheet (LIS), to the high-latitude North Atlantic, which disrupted ocean circulation and triggered cooling. Yet the direct evidence of meltwater input for many of these events has so far remained undetected. In this study, we use the geochemistry (paired Mg/Ca-d18O) of planktonic foraminifera from a sediment core south of Iceland to reconstruct the input of freshwater to the northern North Atlantic during abrupt deglacial climate change. Our record can be placed on the same timescale as ice cores and therefore provides a direct comparison between the timing of freshwater input and climate variability. Meltwater events coincide with the onset of numerous cold intervals, including the Older Dryas (14.0 ka), two events during the Allerød (at ~13.1 and 13.6 ka), the Younger Dryas (12.9 ka), and the 8.2 ka event, supporting a causal link between these abrupt climate changes and meltwater input. During the Bølling-Allerød warm interval, we find that periods of warming are associated with an increased meltwater flux to the northern North Atlantic, which in turn induces abrupt cooling, a cessation in meltwater input, and eventual climate recovery. This implies that feedback between climate and meltwater input produced a highly variable climate. A comparison to published data sets suggests that this feedback likely included fluctuations in the southern margin of the LIS causing rerouting of LIS meltwater between southern and eastern drainage outlets, as proposed by Clark et al. (2001, doi:10.1126/science.1062517).

Myanmar's non-resource export potential after the lifting of economic sanctions : a gravity model analysis

Easing of economic sanctions by Western countries in 2012 augmented the prospect that Myanmar will expand its exports. On the other hand, a sharp rise in natural resource exports during the sanctions brings in a concern about the "Dutch disease". This study projects Myanmar's export potential by calculating counterfactual export values with an augmented gravity model that takes into account the effects of natural resource exports on non-resource exports. Without taking into account the effects of natural resource exports, the counterfactual predicted values of non-resource exports during 2004–2011 are more than five times larger than the actual exports. If we take into account the effects, however, the predicted values are smaller than the actual exports. The empirical results imply that the "Dutch disease" is at stake in Myanmar than any other Southeast Asian countries.

Generating multimedia presentations that summarize the behavior of dynamic systems using a model-based approach

This article describes a knowledge-based method for generating multimedia descriptions that summarize the behavior of dynamic systems. We designed this method for users who monitor the behavior of a dynamic system with the help of sensor networks and make decisions according to prefixed management goals. Our method generates presentations using different modes such as text in natural language, 2D graphics and 3D animations. The method uses a qualitative representation of the dynamic system based on hierarchies of components and causal influences. The method includes an abstraction generator that uses the system representation to find and aggregate relevant data at an appropriate level of abstraction. In addition, the method includes a hierarchical planner to generate a presentation using a model with dis- course patterns. Our method provides an efficient and flexible solution to generate concise and adapted multimedia presentations that summarize thousands of time series. It is general to be adapted to differ- ent dynamic systems with acceptable knowledge acquisition effort by reusing and adapting intuitive rep- resentations. We validated our method and evaluated its practical utility by developing several models for an application that worked in continuous real time operation for more than 1 year, summarizing sen- sor data of a national hydrologic information system in Spain.

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.