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Moodie EEM, Saarela O, Stephens DA. A doubly robust weighting estimator of the average treatment effect on the treated. Stat (Int Stat Inst) 2018. [DOI: 10.1002/sta4.205] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Villandré L, Labbe A, Brenner B, Roger M, Stephens DA. DM-PhyClus: a Bayesian phylogenetic algorithm for infectious disease transmission cluster inference. BMC Bioinformatics 2018; 19:324. [PMID: 30217139 PMCID: PMC6137936 DOI: 10.1186/s12859-018-2347-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 08/29/2018] [Indexed: 12/24/2022] Open
Abstract
Background Conventional phylogenetic clustering approaches rely on arbitrary cutpoints applied a posteriori to phylogenetic estimates. Although in practice, Bayesian and bootstrap-based clustering tend to lead to similar estimates, they often produce conflicting measures of confidence in clusters. The current study proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as DM-PhyClus (Dirichlet-Multinomial Phylogenetic Clustering), that identifies sets of sequences resulting from quick transmission chains, thus yielding easily-interpretable clusters, without using any ad hoc distance or confidence requirement. Results Simulations reveal that DM-PhyClus can outperform conventional clustering methods, as well as the Gap procedure, a pure distance-based algorithm, in terms of mean cluster recovery. We apply DM-PhyClus to a sample of real HIV-1 sequences, producing a set of clusters whose inference is in line with the conclusions of a previous thorough analysis. Conclusions DM-PhyClus, by eliminating the need for cutpoints and producing sensible inference for cluster configurations, can facilitate transmission cluster detection. Future efforts to reduce incidence of infectious diseases, like HIV-1, will need reliable estimates of transmission clusters. It follows that algorithms like DM-PhyClus could serve to better inform public health strategies. Electronic supplementary material The online version of this article (10.1186/s12859-018-2347-3) contains supplementary material, which is available to authorized users.
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Wallace MP, Moodie EEM, Stephens DA. Reward ignorant modeling of dynamic treatment regimes. Biom J 2018; 60:991-1002. [PMID: 29845644 DOI: 10.1002/bimj.201700322] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 04/05/2018] [Accepted: 04/10/2018] [Indexed: 11/09/2022]
Abstract
Personalized medicine optimizes patient outcome by tailoring treatments to patient-level characteristics. This approach is formalized by dynamic treatment regimes (DTRs): decision rules that take patient information as input and output recommended treatment decisions. The DTR literature has seen the development of increasingly sophisticated causal inference techniques that attempt to address the limitations of our typically observational datasets. Often overlooked, however, is that in practice most patients may be expected to receive optimal or near-optimal treatment, and so the outcome used as part of a typical DTR analysis may provide limited information. In light of this, we propose considering a more standard analysis: ignore the outcome and elicit an optimal DTR by modeling the observed treatment as a function of relevant covariates. This offers a far simpler analysis and, in some settings, improved optimal treatment identification. To distinguish this approach from more traditional DTR analyses, we term it reward ignorant modeling, and also introduce the concept of multimethod analysis, whereby different analysis methods are used in settings with multiple treatment decisions. We demonstrate this concept through a variety of simulation studies, and through analysis of data from the International Warfarin Pharmacogenetics Consortium, which also serve as motivation for this work.
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Moodie EEM, Stephens DA, Alam S, Zhang MJ, Logan B, Arora M, Spellman S, Krakow EF. A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients. Biom J 2018; 61:442-453. [PMID: 29766558 DOI: 10.1002/bimj.201700181] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 02/26/2018] [Accepted: 03/23/2018] [Indexed: 11/11/2022]
Abstract
Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft-versus-host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two stages of treatment may lead to a cure has not yet been considered. Using a sample of 9563 patients treated for blood and bone cancers by allogeneic hematopoietic cell transplantation drawn from the Center for Blood and Marrow Transplant Research database, we provide a case study of a novel approach to Q-learning for survival data in the presence of a potentially curative treatment, and demonstrate the results differ substantially from an implementation of Q-learning that fails to account for the cure-rate.
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Shohoudi A, Stephens DA, Khairy P. Bayesian adaptive trials for rare cardiovascular conditions. Future Cardiol 2018; 14:143-150. [PMID: 29405070 DOI: 10.2217/fca-2017-0040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Escalating costs of cardiovascular trials are limiting medical innovations, prompting the development of more efficient and flexible study designs. The Bayesian paradigm offers a framework conducive to adaptive trial methodologies and is well suited for the study of small populations. Bayesian adaptive trials provide a statistical structure for combining prior information with accumulating data to compute probabilities of unknown quantities of interest. Adaptive design features are useful in modifying randomization schemes, adjusting sample sizes and providing continuous surveillance to guide decisions on dropping study arms or premature trial interruption. Advantages include greater efficiency, minimization of risks, inclusion of knowledge as it is generated, cost savings and more intuitive interpretability. Extensive high-level computations are facilitated by an expanding armamentarium of available tools.
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Wallace MP, Stewart CE, Moseley MJ, Stephens DA, Fielder AR. Treatment of Amblyopia Using Personalized Dosing Strategies: Statistical Modelling and Clinical Implementation. Strabismus 2017; 24:161-168. [PMID: 27929726 DOI: 10.1080/09273972.2016.1242638] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
PURPOSE To generate a statistical model for personalizing a patient's occlusion therapy regimen. METHODS Statistical modelling was undertaken on a combined data set of the Monitored Occlusion Treatment of Amblyopia Study (MOTAS) and the Randomized Occlusion Treatment of Amblyopia Study (ROTAS). This exercise permits the calculation of future patients' total effective dose (TED)-that predicted to achieve their best attainable visual acuity. Daily patching regimens (hours/day) can be calculated from the TED. RESULTS Occlusion data for 149 study participants with amblyopia (anisometropic in 50, strabismic in 43, and mixed in 56) were analyzed. Median time to best observed visual acuity was 63 days (25% and 75% quartiles; 28 and 91 days). Median visual acuity in the amblyopic eye at start of occlusion was 0.40 logMAR (quartiles 0.22 and 0.68 logMAR) and at end of occlusion was 0.12 (quartiles 0.025 and 0.32 logMAR). Median lower and upper estimates of TED were 120 hours (quartiles 34 and 242 hours), and 176 hours (quartiles 84 and 316 hours). The data suggest a piecewise linear relationship (P = 0.008) between patching dose-rate (hours/day) and TED with a single breakpoint estimated at 2.16 (standard error 0.51) hours/day, suggesting doses below 2.16 hours/day are less effective. CONCLUSION We introduce the concept of TED of occlusion. Predictors for TED are visual acuity deficit, amblyopia type, and age at start of occlusion therapy. Dose-rates prescribed within the model range from 2.5 to 12 hours/day and can be revised dynamically throughout treatment in response to recorded patient compliance: a personalized dosing strategy.
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Khalili A, Chen J, Stephens DA. Regularization and selection in Gaussian mixture of autoregressive models. CAN J STAT 2017. [DOI: 10.1002/cjs.11332] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Wallace MP, Moodie EEM, Stephens DA. Model validation and selection for personalized medicine using dynamic-weighted ordinary least squares. Stat Methods Med Res 2017; 26:1641-1653. [DOI: 10.1177/0962280217708665] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Model assessment is a standard component of statistical analysis, but it has received relatively little attention within the dynamic treatment regime literature. In this paper, we focus on the dynamic-weighted ordinary least squares approach to optimal dynamic treatment regime estimation, introducing how its double-robustness property may be leveraged for model assessment, and how quasilikelihood may be used for model selection. These ideas are demonstrated through simulation studies, as well as through application to data from the sequenced treatment alternatives to relieve depression study.
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Wallace MP, Moodie EEM, Stephens DA. Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg. J Stat Softw 2017. [DOI: 10.18637/jss.v080.i02] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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Powell GA, Luo YT, Verma A, Stephens DA, Buckeridge DL. Multivariate and Longitudinal Health System Indicators. Stud Health Technol Inform 2017; 235:266-270. [PMID: 28423795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Within population health information systems, indicators are commonly presented as independent, cross-sectional measures, neglecting the multivariate, longitudinal nature of disease progression, health care use, and profiles of performance. We use administrative claims data of Montreal, Canada to identify patterns across indicators and over time in chronic obstructive pulmonary disease patients. We first cluster regions based on four health service indicators. Our second approach discovers individual-level trajectories based on a hidden Markov model using the same four indicators. Both approaches offer additional insights by facilitating the discovery and interpretation of indicators, such as a dual interpretation of low use of general practitioner services. These approaches to the analysis and visualization of health indicators can provide a foundation for information displays that will help decision makers identify areas of concern, predict future disease burden, and implement appropriate policies.
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Rich B, Moodie EEM, A Stephens D. Influence Re-weighted G-Estimation. Int J Biostat 2016; 12:157-77. [PMID: 26234949 DOI: 10.1515/ijb-2015-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Individualized medicine is an area that is growing, both in clinical and statistical settings, where in the latter, personalized treatment strategies are often referred to as dynamic treatment regimens. Estimation of the optimal dynamic treatment regime has focused primarily on semi-parametric approaches, some of which are said to be doubly robust in that they give rise to consistent estimators provided at least one of two models is correctly specified. In particular, the locally efficient doubly robust g-estimation is robust to misspecification of the treatment-free outcome model so long as the propensity model is specified correctly, at the cost of an increase in variability. In this paper, we propose data-adaptive weighting schemes that serve to decrease the impact of influential points and thus stabilize the estimator. In doing so, we provide a doubly robust g-estimator that is also robust in the sense of Hampel (15).
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Villandre L, Stephens DA, Labbe A, Günthard HF, Kouyos R, Stadler T. Assessment of Overlap of Phylogenetic Transmission Clusters and Communities in Simple Sexual Contact Networks: Applications to HIV-1. PLoS One 2016; 11:e0148459. [PMID: 26863322 PMCID: PMC4749335 DOI: 10.1371/journal.pone.0148459] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 01/18/2016] [Indexed: 02/06/2023] Open
Abstract
Background Transmission patterns of sexually-transmitted infections (STIs) could relate to the structure of the underlying sexual contact network, whose features are therefore of interest to clinicians. Conventionally, we represent sexual contacts in a population with a graph, that can reveal the existence of communities. Phylogenetic methods help infer the history of an epidemic and incidentally, may help detecting communities. In particular, phylogenetic analyses of HIV-1 epidemics among men who have sex with men (MSM) have revealed the existence of large transmission clusters, possibly resulting from within-community transmissions. Past studies have explored the association between contact networks and phylogenies, including transmission clusters, producing conflicting conclusions about whether network features significantly affect observed transmission history. As far as we know however, none of them thoroughly investigated the role of communities, defined with respect to the network graph, in the observation of clusters. Methods The present study investigates, through simulations, community detection from phylogenies. We simulate a large number of epidemics over both unweighted and weighted, undirected random interconnected-islands networks, with islands corresponding to communities. We use weighting to modulate distance between islands. We translate each epidemic into a phylogeny, that lets us partition our samples of infected subjects into transmission clusters, based on several common definitions from the literature. We measure similarity between subjects’ island membership indices and transmission cluster membership indices with the adjusted Rand index. Results and Conclusion Analyses reveal modest mean correspondence between communities in graphs and phylogenetic transmission clusters. We conclude that common methods often have limited success in detecting contact network communities from phylogenies. The rarely-fulfilled requirement that network communities correspond to clades in the phylogeny is their main drawback. Understanding the link between transmission clusters and communities in sexual contact networks could help inform policymaking to curb HIV incidence in MSMs.
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Gough EK, Stephens DA, Moodie EEM, Prendergast AJ, Stoltzfus RJ, Humphrey JH, Manges AR. Erratum to: Linear growth faltering in infants is associated with Acidaminococcus sp. and community-level changes in the gut microbiota. MICROBIOME 2016; 4:5. [PMID: 26801625 PMCID: PMC4724103 DOI: 10.1186/s40168-016-0149-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 01/18/2016] [Indexed: 05/06/2023]
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Wallace MP, Moodie EEM, Stephens DA. Model assessment in dynamic treatment regimen estimation via double robustness. Biometrics 2016; 72:855-64. [PMID: 26756122 DOI: 10.1111/biom.12468] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 10/01/2015] [Accepted: 11/01/2015] [Indexed: 11/27/2022]
Abstract
Dynamic treatment regimens (DTRs) recommend treatments based on evolving subject-level data. The optimal DTR is that which maximizes expected patient outcome and as such its identification is of primary interest in the personalized medicine setting. When analyzing data from observational studies using semi-parametric approaches, there are two primary components which can be modeled: the expected level of treatment and the expected outcome for a patient given their other covariates. In an effort to offer greater flexibility, the so-called doubly robust methods have been developed which offer consistent parameter estimators as long as at least one of these two models is correctly specified. However, in practice it can be difficult to be confident if this is the case. Using G-estimation as our example method, we demonstrate how the property of double robustness itself can be used to provide evidence that a specified model is or is not correct. This approach is illustrated through simulation studies as well as data from the Multicenter AIDS Cohort Study.
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Stephens DA. Discussion of “Deductive derivation and turing-computerization of semiparametric efficient estimation” by Frangakis et al. Biometrics 2015; 71:880. [DOI: 10.1111/biom.12364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Rich B, Moodie EEM, Stephens DA. Optimal individualized dosing strategies: A pharmacologic approach to developing dynamic treatment regimens for continuous-valued treatments. Biom J 2015; 58:502-17. [PMID: 26537297 DOI: 10.1002/bimj.201400244] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 04/10/2015] [Accepted: 07/09/2015] [Indexed: 11/06/2022]
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Vrbik I, Stephens DA, Roger M, Brenner BG. The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data. BMC Bioinformatics 2015; 16:355. [PMID: 26538192 PMCID: PMC4634160 DOI: 10.1186/s12859-015-0791-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 10/22/2015] [Indexed: 11/29/2022] Open
Abstract
Background In the context of infectious disease, sequence clustering can be used to provide important insights into the dynamics of transmission. Cluster analysis is usually performed using a phylogenetic approach whereby clusters are assigned on the basis of sufficiently small genetic distances and high bootstrap support (or posterior probabilities). The computational burden involved in this phylogenetic threshold approach is a major drawback, especially when a large number of sequences are being considered. In addition, this method requires a skilled user to specify the appropriate threshold values which may vary widely depending on the application. Results This paper presents the Gap Procedure, a distance-based clustering algorithm for the classification of DNA sequences sampled from individuals infected with the human immunodeficiency virus type 1 (HIV-1). Our heuristic algorithm bypasses the need for phylogenetic reconstruction, thereby supporting the quick analysis of large genetic data sets. Moreover, this fully automated procedure relies on data-driven gaps in sorted pairwise distances to infer clusters, thus no user-specified threshold values are required. The clustering results obtained by the Gap Procedure on both real and simulated data, closely agree with those found using the threshold approach, while only requiring a fraction of the time to complete the analysis. Conclusions Apart from the dramatic gains in computational time, the Gap Procedure is highly effective in finding distinct groups of genetically similar sequences and obviates the need for subjective user-specified values. The clusters of genetically similar sequences returned by this procedure can be used to detect patterns in HIV-1 transmission and thereby aid in the prevention, treatment and containment of the disease. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0791-x) contains supplementary material, which is available to authorized users.
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Saarela O, Arjas E, Stephens DA, Moodie EEM. Predictive Bayesian inference and dynamic treatment regimes. Biom J 2015; 57:941-58. [PMID: 26259996 DOI: 10.1002/bimj.201400153] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Revised: 01/04/2015] [Accepted: 05/27/2015] [Indexed: 11/06/2022]
Abstract
While optimal dynamic treatment regimes (DTRs) can be estimated without specification of a predictive model, a model-based approach, combined with dynamic programming and Monte Carlo integration, enables direct probabilistic comparisons between the outcomes under the optimal DTR and alternative (dynamic or static) treatment regimes. The Bayesian predictive approach also circumvents problems related to frequentist estimators under the nonregular estimation problem. However, the model-based approach is susceptible to misspecification, in particular of the "null-paradox" type, which is due to the model parameters not having a direct causal interpretation in the presence of latent individual-level characteristics. Because it is reasonable to insist on correct inferences under the null of no difference between the alternative treatment regimes, we discuss how to achieve this through a "null-robust" reparametrization of the problem in a longitudinal setting. Since we argue that causal inference can be entirely understood as posterior predictive inference in a hypothetical population without covariate imbalances, we also discuss how controlling for confounding through inverse probability of treatment weighting can be justified and incorporated in the Bayesian setting.
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Moseley MJ, Wallace MP, Stephens DA, Fielder AR, Smith LC, Stewart CE. Personalized versus standardized dosing strategies for the treatment of childhood amblyopia: study protocol for a randomized controlled trial. Trials 2015; 16:189. [PMID: 25906974 PMCID: PMC4414426 DOI: 10.1186/s13063-015-0711-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 04/08/2015] [Indexed: 11/17/2022] Open
Abstract
Background Amblyopia is the commonest visual disorder of childhood in Western societies, affecting, predominantly, spatial visual function. Treatment typically requires a period of refractive correction (‘optical treatment’) followed by occlusion: covering the nonamblyopic eye with a fabric patch for varying daily durations. Recent studies have provided insight into the optimal amount of patching (‘dose’), leading to the adoption of standardized dosing strategies, which, though an advance on previous ad-hoc regimens, take little account of individual patient characteristics. This trial compares the effectiveness of a standardized dosing strategy (that is, a fixed daily occlusion dose based on disease severity) with a personalized dosing strategy (derived from known treatment dose-response functions), in which an initially prescribed occlusion dose is modulated, in a systematic manner, dependent on treatment compliance. Methods/design A total of 120 children aged between 3 and 8 years of age diagnosed with amblyopia in association with either anisometropia or strabismus, or both, will be randomized to receive either a standardized or a personalized occlusion dose regimen. To avoid confounding by the known benefits of refractive correction, participants will not be randomized until they have completed an optical treatment phase. The primary study objective is to determine whether, at trial endpoint, participants receiving a personalized dosing strategy require fewer hours of occlusion than those in receipt of a standardized dosing strategy. Secondary objectives are to quantify the relationship between observed changes in visual acuity (logMAR, logarithm of the Minimum Angle of Resolution) with age, amblyopia type, and severity of amblyopic visual acuity deficit. Discussion This is the first randomized controlled trial of occlusion therapy for amblyopia to compare a treatment arm representative of current best practice with an arm representative of an entirely novel treatment regimen based on statistical modelling of previous trial outcome data. Should the personalized dosing strategy demonstrate superiority over the standardized dosing strategy, then its adoption into routine practice could bring practical benefits in reducing the duration of treatment needed to achieve an optimal outcome. Trial registration ISRCTN ISRCTN12292232.
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Ertefaie A, Asgharian M, Stephens DA. Double bias: estimation of causal effects from length-biased samples in the presence of confounding. Int J Biostat 2015; 11:69-89. [PMID: 25803086 DOI: 10.1515/ijb-2014-0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Length bias in survival data occurs in observational studies when, for example, subjects with shorter lifetimes are less likely to be present in the recorded data. In this paper, we consider estimating the causal exposure (treatment) effect on survival time from observational data when, in addition to the lack of randomization and consequent potential for confounding, the data constitute a length-biased sample; we hence term this a double-bias problem. We develop estimating equations that can be used to estimate the causal effect indexing the structural Cox proportional hazard and accelerated failure time models for point exposures in double-bias settings. The approaches rely on propensity score-based adjustments, and we demonstrate that estimation of the propensity score must be adjusted to acknowledge the length-biased sampling. Large sample properties of the estimators are established and their small sample behavior is studied using simulations. We apply the proposed methods to a set of, partly synthesized, length-biased survival data collected as part of the Canadian Study of Health and Aging (CSHA) to compare survival of subjects with dementia among institutionalized patients versus those recruited from the community and depict their adjusted survival curves.
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Weston DJ, Russell RA, Batty E, Jensen K, Stephens DA, Adams NM, Freemont PS. New quantitative approaches reveal the spatial preference of nuclear compartments in mammalian fibroblasts. J R Soc Interface 2015; 12:20140894. [PMID: 25631564 PMCID: PMC4345468 DOI: 10.1098/rsif.2014.0894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The nuclei of higher eukaryotic cells display compartmentalization and certain nuclear compartments have been shown to follow a degree of spatial organization. To date, the study of nuclear organization has often involved simple quantitative procedures that struggle with both the irregularity of the nuclear boundary and the problem of handling replicate images. Such studies typically focus on inter-object distance, rather than spatial location within the nucleus. The concern of this paper is the spatial preference of nuclear compartments, for which we have developed statistical tools to quantitatively study and explore nuclear organization. These tools combine replicate images to generate 'aggregate maps' which represent the spatial preferences of nuclear compartments. We present two examples of different compartments in mammalian fibroblasts (WI-38 and MRC-5) that demonstrate new knowledge of spatial preference within the cell nucleus. Specifically, the spatial preference of RNA polymerase II is preserved across normal and immortalized cells, whereas PML nuclear bodies exhibit a change in spatial preference from avoiding the centre in normal cells to exhibiting a preference for the centre in immortalized cells. In addition, we show that SC35 splicing speckles are excluded from the nuclear boundary and localize throughout the nucleoplasm and in the interchromatin space in non-transformed WI-38 cells. This new methodology is thus able to reveal the effect of large-scale perturbation on spatial architecture and preferences that would not be obvious from single cell imaging.
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Saarela O, Stephens DA, Moodie EEM, Klein MB. On Bayesian estimation of marginal structural models. Biometrics 2015; 71:279-88. [PMID: 25677103 DOI: 10.1111/biom.12269] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 08/01/2014] [Accepted: 08/01/2014] [Indexed: 11/27/2022]
Abstract
The purpose of inverse probability of treatment (IPT) weighting in estimation of marginal treatment effects is to construct a pseudo-population without imbalances in measured covariates, thus removing the effects of confounding and informative censoring when performing inference. In this article, we formalize the notion of such a pseudo-population as a data generating mechanism with particular characteristics, and show that this leads to a natural Bayesian interpretation of IPT weighted estimation. Using this interpretation, we are able to propose the first fully Bayesian procedure for estimating parameters of marginal structural models using an IPT weighting. Our approach suggests that the weights should be derived from the posterior predictive treatment assignment and censoring probabilities, answering the question of whether and how the uncertainty in the estimation of the weights should be incorporated in Bayesian inference of marginal treatment effects. The proposed approach is compared to existing methods in simulated data, and applied to an analysis of the Canadian Co-infection Cohort.
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Saarela O, Stephens DA, Moodie EEM, Klein MB. Rejoinder "On Bayesian estimation of marginal structural models". Biometrics 2015; 71:299-301. [PMID: 25652412 DOI: 10.1111/biom.12274] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Gough EK, Stephens DA, Moodie EE, Prendergast AJ, Stoltzfus RJ, Humphrey JH, Manges AR. Linear growth faltering in infants is associated with Acidaminococcus sp. and community-level changes in the gut microbiota. MICROBIOME 2015; 3:24. [PMID: 26106478 PMCID: PMC4477476 DOI: 10.1186/s40168-015-0089-2] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 06/04/2015] [Indexed: 05/20/2023]
Abstract
BACKGROUND Chronic malnutrition, termed stunting, is defined as suboptimal linear growth, affects one third of children in developing countries, and leads to increased mortality and poor developmental outcomes. The causes of childhood stunting are unknown, and strategies to improve growth and related outcomes in children have only had modest impacts. Recent studies have shown that the ecosystem of microbes in the human gut, termed the microbiota, can induce changes in weight. However, the specific changes in the gut microbiota that contribute to growth remain unknown, and no studies have investigated the gut microbiota as a determinant of chronic malnutrition. RESULTS We performed secondary analyses of data from two well-characterized twin cohorts of children from Malawi and Bangladesh to identify bacterial genera associated with linear growth. In a case-control analysis, we used the graphical lasso to estimate covariance network models of gut microbial interactions from relative genus abundances and used network analysis methods to select genera associated with stunting severity. In longitudinal analyses, we determined associations between these selected microbes and linear growth using between-within twin regression models to adjust for confounding and introduce temporality. Reduced microbiota diversity and increased covariance network density were associated with stunting severity, while increased relative abundance of Acidaminococcus sp. was associated with future linear growth deficits. CONCLUSIONS We show that length growth in children is associated with community-wide changes in the gut microbiota and with the abundance of the bacterial genus, Acidaminococcus. Larger cohorts are needed to confirm these findings and to clarify the mechanisms involved.
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