A bi-dimensional finite mixture model for longitudinal data subject to dropout

In longitudinal studies, subjects may be lost to follow-up, or miss some of the planned visits, leading to incomplete response sequences. When the probability of non-response, conditional on the available covariates and the observed responses, still depends on unobserved outcomes, the dropout mechanism is said to be non ignorable. A common objective is to build a reliable association structure to account for dependence between the longitudinal and the dropout processes. Starting from the existing literature, we introduce a random coefficient based dropout model where the association between outcomes is modeled through discrete latent effects. These effects are outcome-specific and account for heterogeneity in the univariate profiles. Dependence between profiles is introduced by using a bi-dimensional representation for the corresponding distribution. In this way, we define a flexible latent class structure which allows to efficiently describe both dependence within the two margins of interest and dependence between them. By using this representation we show that, unlike standard (unidimensional) finite mixture models, the non ignorable dropout model properly nests its ignorable counterpart. We detail the proposed modeling approach by analyzing data from a longitudinal study on the dynamics of cognitive functioning in the elderly. Further, the effects of assumptions about non ignorability of the dropout process on model parameter estimates are (locally) investigated using the index of (local) sensitivity to non-ignorability

Comparative analysis of genome-wide association studies signals for lipids, diabetes, and coronary heart disease: Cardiovascular Biomarker Genetics Collaboration

AIMS: To evaluate the associations of emergent genome-wide-association study-derived coronary heart disease (CHD)-associated single nucleotide polymorphisms (SNPs) with established and emerging risk factors, and the association of genome-wide-association study-derived lipid-associated SNPs with other risk factors and CHD events. METHODS AND RESULTS: Using two case–control studies, three cross-sectional, and seven prospective studies with up to 25 000 individuals and 5794 CHD events we evaluated associations of 34 genome-wide-association study-identified SNPs with CHD risk and 16 CHD-associated risk factors or biomarkers. The Ch9p21 SNPs rs1333049 (OR 1.17; 95% confidence limits 1.11–1.24) and rs10757274 (OR 1.17; 1.09–1.26), MIA3 rs17465637 (OR 1.10; 1.04–1.15), Ch2q36 rs2943634 (OR 1.08; 1.03–1.14), APC rs383830 (OR 1.10; 1.02, 1.18), MTHFD1L rs6922269 (OR 1.10; 1.03, 1.16), CXCL12 rs501120 (OR 1.12; 1.04, 1.20), and SMAD3 rs17228212 (OR 1.11; 1.05, 1.17) were all associated with CHD risk, but not with the CHD biomarkers and risk factors measured. Among the 20 blood lipid-related SNPs, LPL rs17411031 was associated with a lower risk of CHD (OR 0.91; 0.84–0.97), an increase in Apolipoprotein AI and HDL-cholesterol, and reduced triglycerides. SORT1 rs599839 was associated with CHD risk (OR 1.20; 1.15–1.26) as well as total- and LDL-cholesterol, and apolipoprotein B. ANGPTL3 rs12042319 was associated with CHD risk (OR 1.11; 1.03, 1.19), total- and LDL-cholesterol, triglycerides, and interleukin-6. CONCLUSION: Several SNPs predicting CHD events appear to involve pathways not currently indexed by the established or emerging risk factors; others involved changes in blood lipids including triglycerides or HDL-cholesterol as well as LDL-cholesterol. The overlapping association of SNPs with multiple risk factors and biomarkers supports the existence of shared points of regulation for these phenotypes

Method for the analysis of incomplete longitudinal data

Unplanned missing data commonly arise in longitudinal trials. When the mechanism driving the missing data process is related to the outcome under investigation, traditional methods of analysis may yield seriously biased parameter estimates. Motivated by data from two clinical trials, this thesis explores various approaches to dealing with data incompleteness. In the first part, a Monte Carlo EM algorithm is developed and used to fit so called random-co efficient-based dropout models; these models relate the probability of a patient's dropout in follow-up studies to some subject-specific characteristics such as their deviation from the average rate of progression of the disease over time. The approach is used to model incomplete data from a 5-year study of patients with Parkinson's disease. The validity of the results obtained using these methods however, depends in general on distributional and modelling assumptions about the missing data that are inherently untestable as no data were collected. For this reason, many have advocated the need for a sensitivity analysis aimed at assessing the robustness of the conclusions from an analysis that ignores the missing data mechanism. In the second part of the thesis we address these issues. In particular, we present results from sensitivity analyses based on local influence and sampling-based methods used in conjunction with the random-coefficient-based dropout model described in the first part. Recently, a more formal approach to sensitivity analysis for missing data problems has been proposed whereby traditional point estimates are replaced by intervals encoding our lack of knowledge due to incompleteness of the data. In the third part of the thesis, we extend these methods to longitudinal ordinal data. Also, for cross-sectional discrete data having distribution belonging to the exponential family, we propose using the proportion of possible estimates of a parameter of interest, over all solutions corresponding to all sample completions, as a measure of ignorance. We develop a computationally efficient algorithm to calculate this proportion and illustrate our methods using data from a dental pain trial

Positive end-expiratory pressure affects the value of intra-abdominal pressure in acute lung injury/acute respiratory distress syndrome patients: a pilot study

International audienceIntroduction: To examine the effects of positive end-expiratory pressure (PEEP) on intra-abdominal pressure (IAP) in patients with acute lung injury (ALI).Methods: Thirty sedated and mechanically ventilated patients with ALI or acute respiratory distress syndrome (ARDS) admitted to a sixteen-bed surgical medical ICU were included. All patients were studied with sequentially increasing PEEP (0, 6 and 12 cmH2O) during a PEEP-trial.Results: Age was 55 ± 17 years, weight was 70 ± 17 kg, SAPS II was 44 ± 14 and PaO2/FIO2 was 192 ± 53 mmHg. The IAP was 12 ± 5 mmHg at PEEP 0 (zero end-expiratory pressure, ZEEP), 13 ± 5 mmHg at PEEP 6 and 15 ± 6 mmHg at PEEP 12 (P < 0.05 vs ZEEP). In the patients with intra-abdominal hypertension defined as IAP ≥ 12 mmHg (n = 15), IAP significantly increased from 15 ± 3 mmHg at ZEEP to 20 ± 3 mmHg at PEEP 12 (P < 0.01). Whereas in the patients with IAP < 12 mmHg (n = 15), IAP did not significantly change from ZEEP to PEEP 12(8 ± 2 vs 10 ± 3 mmHg). In the 13 patients in whom cardiac output was measured, increase in PEEP from 0 to 12 cmH2O did not significantly change cardiac output, nor in the 8 out of 15 patients of the high-IAP group. The observed effects were similar in both ALI (n = 17) and ARDS (n = 13) patients.Conclusions: PEEP is a contributing factor that impacts IAP values. It seems necessary to take into account the level of PEEP whilst interpreting IAP values in patients under mechanical ventilation

Bayesian survival analysis in genetic association studies.

MOTIVATION: Large-scale genetic association studies are carried out with the hope of discovering single nucleotide polymorphisms involved in the etiology of complex diseases. There are several existing methods in the literature for performing this kind of analysis for case-control studies, but less work has been done for prospective cohort studies. We present a Bayesian method for linking markers to censored survival outcome by clustering haplotypes using gene trees. Coalescent-based approaches are promising for LD mapping, as the coalescent offers a good approximation to the evolutionary history of mutations. RESULTS: We compare the performance of the proposed method in simulation studies to the univariate Cox regression and to dimension reduction methods, and we observe that it performs similarly in localizing the causal site, while offering a clear advantage in terms of false positive associations. Moreover, it offers computational advantages. Applying our method to a real prospective study, we observe potential association between candidate ABC transporter genes and epilepsy treatment outcomes. AVAILABILITY: R codes are available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Integrated associations of genotypes with multiple blood biomarkers linked to coronary heart disease risk

Individuals at risk of coronary heart disease (CHD) show multiple correlations across blood biomarkers. Single nucleotide polymorphisms (SNPs) indexing biomarker differences could help distinguish causal from confounded associations because of their random allocation prior to disease. We examined the association of 948 SNPs in 122 candidate genes with 12 CHD-associated phenotypes in 2775 middle aged men (a genic scan). Of these, 140 SNPs indexed differences in HDL- and LDL-cholesterol, triglycerides, C-reactive protein, fibrinogen, factor VII, apolipoproteins AI and B, lipoprotein-associated phospholipase A2, homocysteine or folate, some with large effect sizes and highly significant P-values (e.g. 2.15 standard deviations at P = 9.2 × 10−140 for F7 rs6046 and FVII levels). Top ranking SNPs were then tested for association with additional biomarkers correlated with the index phenotype (phenome scan). Several SNPs (e.g. in APOE, CETP, LPL, APOB and LDLR) influenced multiple phenotypes, while others (e.g. in F7, CRP and FBB) showed restricted association to the index marker. SNPs influencing six blood proteins were used to evaluate the nature of the associations between correlated blood proteins utilizing Mendelian randomization. Multiple SNPs were associated with CHD-related quantitative traits, with some associations restricted to a single marker and others exerting a wider genetic ‘footprint’. SNPs indexing biomarkers provide new tools for investigating biological relationships and causal links with disease. Broader and deeper integrated analyses, linking genomic with transcriptomic, proteomic and metabolomic analysis, as well as clinical events could, in principle, better delineate CHD causing pathways amenable to treatment

Unraveling the directional link between adiposity and inflammation: a bidirectional mendelian randomization approach

&lt;b&gt;Context&lt;/b&gt;: Associations between adiposity and circulating inflammation markers are assumed to be causal, although the direction of the relationship has not been proven. &lt;b&gt;Objective&lt;/b&gt;: The aim of the study was to explore the causal direction of the relationship between adiposity and inflammation using a bidirectional Mendelian randomization approach. &lt;b&gt;Methods&lt;/b&gt;: In the PROSPER study of 5804 elderly patients, we related C-reactive protein (CRP) single nucleotide polymorphisms (SNPs) (rs1800947 and rs1205) and adiposity SNPs (FTO and MC4R) to body mass index (BMI) as well as circulating levels of CRP and leptin. We gave each individual two allele scores ranging from zero to 4, counting each pair of alleles related to CRP levels or BMI. &lt;b&gt;Results&lt;/b&gt;: With increasing CRP allele score, there was a stepwise decrease in CRP levels (P for trend &#60; 0.0001) and a 1.98 mg/liter difference between extremes of the allele score distribution, but there was no associated change in BMI or leptin levels (P ≥ 0.89). By contrast, adiposity allele score was associated with 1) an increase in BMI (1.2 kg/m2 difference between extremes; P for trend 0.002); 2) an increase in circulating leptin (5.77 ng/ml difference between extremes; P for trend 0.0027); and 3) increased CRP levels (1.24 mg/liter difference between extremes; P for trend 0.002). &lt;b&gt;Conclusions&lt;/b&gt;: Greater adiposity conferred by FTO and MC4R SNPs led to higher CRP levels, with no evidence for any reverse pathway. Future studies should extend our findings to other circulating inflammatory parameters. This study illustrates the potential power of Mendelian randomization to dissect directions of causality between intercorrelated metabolic factors

Interdisciplinariedad e Intersectorialidad para la producción social del hábitat. Diseño participativo de un Asentamiento en Resistencia, Chaco.

El trabajo presenta una experiencia de práctica profesional interdisciplinaria, iniciada a partir de la firma de acuerdos de cooperación entre la Facultad de Arquitectura y Urbanismo de la Universidad Nacional del Nordeste (FAU-UNNE), la Facultad de Derecho, Ciencias Sociales y Políticas y el Instituto Superior de Servicio Social Remedios de Escalada de San Martín (ISSS). La propuesta de intervención surge a partir de la solicitud de un dirigente barrial del Centro de Promoción y Participación Comunitaria (CPyPC) del Asentamiento Soberanía, ubicado en la zona sur de la ciudad de Resistencia, capital de la Provincia del Chaco, a la cátedra Gestión y Desarrollo de la Vivienda Popular (GDVP) de la FAU, para la realización del reloteo[1] del asentamiento mencionado. La metodología participativa utilizada se implementa a través de talleres con el objetivo de: consolidar las relaciones vecinales, compartiendo y trabajando con los habitantes la problemática de la configuración espacial espontánea del barrio y los inconvenientes que eso conlleva para su inserción en la ciudad, logrando los consensos necesarios para el diseño del reloteo del sector. La coordinación de la intervención presenta carácter intersectorial, debido a la necesaria articulación por un lado, con el sector gubernamental, representado en el Instituto Provincial de Desarrollo Urbano y Vivienda (IPDUV), que implementa en nuestra provincia el Programa de Mejoramiento Barrial (PROMEBA) y, por otro, con el CPyPC y las 91 familias que integran el asentamiento. La experiencia tiene incidencia directa sobre tres aspectos: el acceso a la propiedad de la tierra, al posibilitar la regulación definitiva de cada lote; el acceso en carácter de beneficiarios al PROMEBA, al cumplimentar con las condiciones mínimas que exige el municipio para incorporarlo al catastro; y el fortalecimiento de la organización comunitaria del asentamiento. La vinculación con la producción social del hábitat en un contexto de extrema necesidad y conflictos sociales, atravesado por la práctica del trabajo interdisciplinario y por los procesos administrativos de los Organismos Públicos, implica la constante revisión de estrategias, que orienten procesos sociales superadores de los intereses particulares. [1] En Colombia, se denomina reloteo a la autorización para dividir, redistribuir o modificar el loteo de uno o más predios previamente urbanizados, para un mayor aprovechamiento, de conformidad con las normas que para el efecto establezcan el Plan de Ordenamiento Territorial y los instrumentos que lo desarrollen y complementen. En nuestro caso, es utilizado con una definición similar, pero para intervenir en predios con distintos grados de urbanización, debido a la ausencia de planificación de la ciudad por parte de las autoridades (Ministerio de Ambiente, Vivienda y Desarrollo Territorial, Decreto nº 564 de 2006, Licencias Urbanísticas)

Gene-Based Tests of Association

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%–50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis