Causal inference for continuous-time processes when covariates are observed only at discrete times

Most of the work on the structural nested model and g-estimation for causal inference in longitudinal data assumes a discrete-time underlying data generating process. However, in some observational studies, it is more reasonable to assume that the data are generated from a continuous-time process and are only observable at discrete time points. When these circumstances arise, the sequential randomization assumption in the observed discrete-time data, which is essential in justifying discrete-time g-estimation, may not be reasonable. Under a deterministic model, we discuss other useful assumptions that guarantee the consistency of discrete-time g-estimation. In more general cases, when those assumptions are violated, we propose a controlling-the-future method that performs at least as well as g-estimation in most scenarios and which provides consistent estimation in some cases where g-estimation is severely inconsistent. We apply the methods discussed in this paper to simulated data, as well as to a data set collected following a massive flood in Bangladesh, estimating the effect of diarrhea on children's height. Results from different methods are compared in both simulation and the real application.Comment: Published in at http://dx.doi.org/10.1214/10-AOS830 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Defining and Estimating Intervention Effects for Groups that will Develop an Auxiliary Outcome

It has recently become popular to define treatment effects for subsets of the target population characterized by variables not observable at the time a treatment decision is made. Characterizing and estimating such treatment effects is tricky; the most popular but naive approach inappropriately adjusts for variables affected by treatment and so is biased. We consider several appropriate ways to formalize the effects: principal stratification, stratification on a single potential auxiliary variable, stratification on an observed auxiliary variable and stratification on expected levels of auxiliary variables. We then outline identifying assumptions for each type of estimand. We evaluate the utility of these estimands and estimation procedures for decision making and understanding causal processes, contrasting them with the concepts of direct and indirect effects. We motivate our development with examples from nephrology and cancer screening, and use simulated data and real data on cancer screening to illustrate the estimation methods.Comment: Published at http://dx.doi.org/10.1214/088342306000000655 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Estimating the distribution of dynamic invariants: illustrated with an application to human photo-plethysmographic time series

Dynamic invariants are often estimated from experimental time series with the aim of differentiating between different physical states in the underlying system. The most popular schemes for estimating dynamic invariants are capable of estimating confidence intervals, however, such confidence intervals do not reflect variability in the underlying dynamics. We propose a surrogate based method to estimate the expected distribution of values under the null hypothesis that the underlying deterministic dynamics are stationary. We demonstrate the application of this method by considering four recordings of human pulse waveforms in differing physiological states and show that correlation dimension and entropy are insufficient to differentiate between these states. In contrast, algorithmic complexity can clearly differentiate between all four rhythms

Detecting periodicity in experimental data using linear modeling techniques

Fourier spectral estimates and, to a lesser extent, the autocorrelation function are the primary tools to detect periodicities in experimental data in the physical and biological sciences. We propose a new method which is more reliable than traditional techniques, and is able to make clear identification of periodic behavior when traditional techniques do not. This technique is based on an information theoretic reduction of linear (autoregressive) models so that only the essential features of an autoregressive model are retained. These models we call reduced autoregressive models (RARM). The essential features of reduced autoregressive models include any periodicity present in the data. We provide theoretical and numerical evidence from both experimental and artificial data, to demonstrate that this technique will reliably detect periodicities if and only if they are present in the data. There are strong information theoretic arguments to support the statement that RARM detects periodicities if they are present. Surrogate data techniques are used to ensure the converse. Furthermore, our calculations demonstrate that RARM is more robust, more accurate, and more sensitive, than traditional spectral techniques.Comment: 10 pages (revtex) and 6 figures. To appear in Phys Rev E. Modified styl

Identifying the starting point of a spreading process in complex networks

When dealing with the dissemination of epidemics, one important question that can be asked is the location where the contamination began. In this paper, we analyze three spreading schemes and propose and validate an effective methodology for the identification of the source nodes. The method is based on the calculation of the centrality of the nodes on the sampled network, expressed here by degree, betweenness, closeness and eigenvector centrality. We show that the source node tends to have the highest measurement values. The potential of the methodology is illustrated with respect to three theoretical complex network models as well as a real-world network, the email network of the University Rovira i Virgili

Reform in Canadian Universities

This article reports a survey of academic vice-presidents and deans of Canadian universities which was undertaken in 1991. The focal topic was reform (defined as significant change), and perceptions of reform occurring over the past three years were derived by means of a questionnaire. Many changes were reported, giving the impression of highly responsive institutions, but these reforms were seen to be modest rather than bold in nature and reactive rather than pro-active. The major environmental influence impelling change was the level of funding. The reforms perceived to be most frequent and significant were mandate changes arising from strategic planning, responses to funding constraints, curriculum expansion, coping with increased student numbers, changes in administrative structure especially at the vice-presidency, and more democratic decision-making. Respondents were generally supportive of the goals which were perceived to lie behind the reforms but were not convinced that significant progress towards goals was actually being achieved. A comparison with reform in other parts of the world revealed that Canadian universities follow the decentralized ad hoc pattern found in federal nations, but there is reason to hope that strategic planning will produce a clearer sense of direction than has been typical elsewhere. The report concludes that the claim that universities are not responsive to changing societal needs is unwarranted, but that more significant lasting reforms are needed.Cet article rapporte les résultats d'une enquête effectuée en 1991 auprès de vice-recteurs et de doyens d'universités canadiennes. Le but de cette enquête était d'évaluer les changements importants s'étant opérés dans les établissements durant les trois dernières années ainsi que les perceptions qu 'en avaient les répondant(e)s à l'enquête. Les personnes interrogées ont fait part d'un grand nombre de cas donnant l'impression que les établissements s'adaptaient résolument au changement. Mais après analyse, il appert que les changements identifiés sont plus modestes que décisifs et de nature davantage réactive que pro-active. L'élément déterminant ayant déclenché la plupart des actions entreprises se rapportait au financement des établissements. Les changements les plus fréquents et importants observés touchaient les mandats découlant de la planification stratégique, des coupures budgétaires, des croissances de clientèle étudiantes, des changements dans les structures administratives, particulièrement au niveau de la vice-présidence, ou des processus plus démocratiques de prise de décision. Les personnes interrogées appuyaient, de façon générale, les objectifs poursuivis mais ne croyaient pas que des changements en profondeur s'étaient opérés par rapport à ces objectifs. Une comparaison avec d'autres systèmes d'éducation ailleurs dans le monde révèle que les universités canadiennes présentent des modes de gestion décentralisée et ad hoc semblables à ceux observés ailleurs dans les nations constituées en fédération, mais qu'il y a lieu d'espérer que la planification stratégique amènera les établissements à mieux clarifier leurs orientations. Cette étude conclut que les universités tentent réellement de répondre aux attentes de leur milieux, mais que des changements plus substantiels sont nécessaires