What do case-control studies estimate? Survey of methods and assumptions in published case-control research

To evaluate strategies used to select cases and controls and how reported odds ratios are interpreted, the authors examined 150 case-control studies published in leading general medicine, epidemiology, and clinical specialist journals from 2001 to 2007. Most of the studies (125/150; 83%) were based on incident cases; among these, the source population was mostly dynamic (102/125; 82%). A minority (23/125; 18%) sampled from a fixed cohort. Among studies with incident cases, 105 (84%) could interpret the odds ratio as a rate ratio. Fifty-seven (46% of 125) required the source population to be stable for such interpretation, while the remaining 48 (38% of 125) did not need any assumptions because of matching on time or concurrent sampling. Another 17 (14% of 125) studies with incident cases could interpret the odds ratio as a risk ratio, with 16 of them requiring the rare disease assumption for this interpretation. The rare disease assumption was discussed in 4 studies but was not relevant to any of them. No investigators mentioned the need for a stable population. The authors conclude that in current case-control research, a stable exposure distribution is much more frequently needed to interpret odds ratios than the rare disease assumption. At present, investigators conducting case-control studies rarely discuss what their odds ratios estimate.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Body contouring surgery following bariatric surgery and dietetically induced massive weight reduction: a risk analysis

BACKGROUND: This study analyzed the impact of weight reduction method, preoperative, and intraoperative variables on the outcome of reconstructive body contouring surgery following massive weight reduction. METHODS: All patients presenting with a maximal BMI >/=35 kg/m(2) before weight reduction who underwent body contouring surgery of the trunk following massive weight loss (excess body mass index loss (EBMIL) >/= 30%) between January 2002 and June 2007 were retrospectively analyzed. Incomplete records or follow-up led to exclusion. Statistical analysis focused on weight reduction method and pre-, intra-, and postoperative risk factors. The outcome was compared to current literature results. RESULTS: A total of 104 patients were included (87 female and 17 male; mean age 47.9 years). Massive weight reduction was achieved through bariatric surgery in 62 patients (59.6%) and dietetically in 42 patients (40.4%). Dietetically achieved excess body mass index loss (EBMIL) was 94.20% and in this cohort higher than surgically induced reduction EBMIL 80.80% (p < 0.01). Bariatric surgery did not present increased risks for complications for the secondary body contouring procedures. The observed complications (26.9%) were analyzed for risk factors. Total tissue resection weight was a significant risk factor (p < 0.05). Preoperative BMI had an impact on infections (p < 0.05). No impact on the postoperative outcome was detected in EBMIL, maximal BMI, smoking, hemoglobin, blood loss, body contouring technique or operation time. Corrective procedures were performed in 11 patients (10.6%). The results were compared to recent data. CONCLUSION: Bariatric surgery does not increase risks for complications in subsequent body contouring procedures when compared to massive dietetic weight reduction.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.

[Treatment of the aortic valve and ascending aorta. The significance of reconstructive methods on the aortic root]

BACKGROUND AND OBJECTIVE: The standard surgical repair of disease of the aortic valve and the ascending aorta has been combined replacement, which includes the disadvantage of inserting a mechanical valve. We have investigated an individualized approach which preserves the native valve. PATIENTS AND METHODS: Between October 1995 and October 1997, a consecutive total of 101 patients (72 men, 29 women, aged 21-83 years) underwent operations for disease of the ascending aorta: aortic dissection type A in 34 patients, aneurysmal dilatation in 67. Dilatation of the aortic arch was associated with aortic regurgitation in 58 patients. There were 11 patients with aortic valve stenosis or previously implanted aortic valve prosthesis among a total of 46 whose aortic valve was replaced (group II). Supracommissural aortic replacement with a Dacron tube was performed in 16 patients (group I) with normal valve cusps and an aortic root diameter < 3.5 cm. In 28 patients with an aortic root diameter of 3.5-5.0 cm the aortic root was remodelled (group III). Resuspension of the native aortic valve was undertaken in 11 patients with aortic root dilatation of > 5.0 cm (group IV). RESULTS: Operative intervention was electively performed in 72 patients, without any death. Of 29 patients operated as an emergency for acute type A dissection four died (14%). In 55 of the 58 patients with aortic regurgitation in proved possible to preserve native aortic valve (95%). In the early postoperative phase and after an average follow-up time of 11.8 months, transthoracic echocardiography demonstrated good aortic valve function, except in one patient each of groups III and IV who developed aortic regurgitation grades I or II. CONCLUSION: The described individualized approach makes it possible to preserve the native aortic valve in most patients with aortic regurgitation, at a low risk. Follow-up observations so far indicate good results of the reconstruction.

Culture, risk factors and mortality: can Switzerland add missing pieces to the European puzzle?

BACKGROUND: The aim was to compare cause-specific mortality, self-rated health (SRH) and risk factors in the French and German part of Switzerland and to discuss to what extent variations between these regions reflect differences between France and Germany. METHODS: Data were used from the general population of German and French Switzerland with 2.8 million individuals aged 45-74 years, contributing 176 782 deaths between 1990 and 2000. Adjusted mortality risks were calculated from the Swiss National Cohort, a longitudinal census-based record linkage study. Results were contrasted with cross-sectional analyses of SRH and risk factors (Swiss Health Survey 1992/3) and with cross-sectional national and international mortality rates for 1980, 1990 and 2000. RESULTS: Despite similar all-cause mortality, there were substantial differences in cause-specific mortality between Swiss regions. Deaths from circulatory disease were more common in German Switzerland, while causes related to alcohol consumption were more prevalent in French Switzerland. Many but not all of the mortality differences between the two regions could be explained by variations in risk factors. Similar patterns were found between Germany and France. CONCLUSION: Characteristic mortality and behavioural differentials between the German- and the French-speaking parts of Switzerland could also be found between Germany and France. However, some of the international variations in mortality were not in line with the Swiss regional comparison nor with differences in risk factors. These could relate to peculiarities in assignment of cause of death. With its cultural diversity, Switzerland offers the opportunity to examine cultural determinants of mortality without bias due to different statistical systems or national health policies.

The lifestyle of our kids (LOOK) project: outline of methods

This methods paper outlines the overall design of a community-based multidisciplinary longitudinal study with the intent to stimulate interest and communication from scientists and practitioners studying the role of physical activity in preventive medicine. In adults, lack of regular exercise is a major risk factor in the development of chronic degenerative diseases and is a major contributor to obesity, and now we have evidence that many of our children are not sufficiently active to prevent early symptoms of chronic disease. The lifestyle of our kids (LOOK) study investigates how early physical activity contributes to health and development, utilizing a longitudinal design and a cohort of eight hundred and thirty 7-8-year-old (grade 2) school children followed to age 11-12 years (grade 6), their average family income being very close to that of Australia. We will test two hypotheses, that (a) the quantity and quality of physical activity undertaken by primary school children will influence their psychological and physical health and development; (b) compared with existing practices in primary schools, a physical education program administered by visiting specialists will enhance health and development, and lead to a more positive perception of physical activity. To test the first hypothesis we will monitor all children longitudinally over the 4 years. To test the second we will involve an intervention group of 430 children who receive two 50min physical education classes every week from visiting specialists and a control group of 400 who continue with their usual primary school physical education with their class-room teachers. At the end of grades 2, 4, and 6 we will measure several areas of health and development including blood risk factors for chronic disease, cardiovascular structure and function, physical fitness, psychological characteristics and perceptions of physical activity, bone structure and strength, motor control, body composition, nutritional intake, influence of teachers and family, and academic performance.

Validation of the estimation of physiologic ability and surgical stress (E-PASS) score in liver surgery

BACKGROUND: The estimation of physiologic ability and surgical stress (E-PASS) has been used to produce a numerical estimate of expected mortality and morbidity after elective gastrointestinal surgery. The aim of this study was to validate E-PASS in a selected cohort of patients requiring liver resections (LR). METHODS: In this retrospective study, E-PASS predictor equations for morbidity and mortality were applied to the prospective data from 243 patients requiring LR. The observed rates were compared with predicted rates using Fisher's exact test. The discriminative capability of E-PASS was evaluated using receiver-operating characteristic (ROC) curve analysis. RESULTS: The observed and predicted overall mortality rates were both 3.3% and the morbidity rates were 31.3 and 26.9%, respectively. There was a significant difference in the comprehensive risk scores for deceased and surviving patients (p = 0.043). However, the scores for patients with or without complications were not significantly different (p = 0.120). Subsequent ROC curve analysis revealed a poor predictive accuracy for morbidity. CONCLUSIONS: The E-PASS score seems to effectively predict mortality in this specific group of patients but is a poor predictor of complications. A new modified logistic regression might be required for LR in order to better predict the postoperative outcome.

Low bone mineral density, renal dysfunction, and fracture risk in HIV infection: a cross-sectional study

BACKGROUND: Reduced bone mineral density (BMD) is common in adults infected with human immunodeficiency virus (HIV). The role of proximal renal tubular dysfunction (PRTD) and alterations in bone metabolism in HIV-related low BMD are incompletely understood. METHODS: We quantified BMD (dual-energy x-ray absorptiometry), blood and urinary markers of bone metabolism and renal function, and risk factors for low BMD (hip or spine T score, -1 or less) in an ambulatory care setting. We determined factors associated with low BMD and calculated 10-year fracture risks using the World Health Organization FRAX equation. RESULTS: We studied 153 adults (98% men; median age, 48 years; median body mass index, 24.5; 67 [44%] were receiving tenofovir, 81 [53%] were receiving a boosted protease inhibitor [PI]). Sixty-five participants (42%) had low BMD, and 11 (7%) had PRTD. PI therapy was associated with low BMD in multivariable analysis (odds ratio, 2.69; 95% confidence interval, 1.09-6.63). Tenofovir use was associated with increased osteoblast and osteoclast activity (P< or = .002). The mean estimated 10-year risks were 1.2% for hip fracture and 5.4% for any major osteoporotic fracture. CONCLUSIONS: In this mostly male population, low BMD was significantly associated with PI therapy. Tenofovir recipients showed evidence of increased bone turnover. Measurement of BMD and estimation of fracture risk may be warranted in treated HIV-infected adults.

High level methods for scene exploration

Virtual worlds exploration techniques are used in a wide variety of domains — from graph drawing to robot motion. This paper is dedicated to virtual world exploration techniques which have to help a human being to understand a 3D scene. An improved method of viewpoint quality estimation is presented in the paper, together with a new off-line method for automatic 3D scene exploration, based on a virtual camera. The automatic exploration method is working in two steps. In the first step, a set of “good” viewpoints is computed. The second step uses this set of points of view to compute a camera path around the scene. Finally, we define a notion of semantic distance between objects of the scene to improve the approach.

A chrysophyte stomatocyst-based reconstruction of cold-season air temperature from Alpine Lake Silvaplana (AD 1500–2003); methods and concepts for quantitative inferences

Relatively little is known about past cold-season temperature variability in high-Alpine regions because of a lack of natural cold-season temperature proxies as well as under-representation of high-altitude sites in meteorological, early-instrumental and documentary data sources. Recent studies have shown that chrysophyte stomatocysts, or simply cysts (sub-fossil algal remains of Chrysophyceae and Synurophyceae), are among the very few natural proxies that can be used to reconstruct cold-season temperatures. This study presents a quantitative, high-resolution (5-year), cold-season (Oct–May) temperature reconstruction based on sub-fossil chrysophyte stomatocysts in the annually laminated (varved) sediments of high-Alpine Lake Silvaplana, SE Switzerland (1,789 m a.s.l.), since AD 1500. We first explore the method used to translate an ecologically meaningful variable based on a biological proxy into a simple climate variable. A transfer function was applied to reconstruct the ‘date of spring mixing’ from cyst assemblages. Next, statistical regression models were tested to convert the reconstructed ‘dates of spring mixing’ into cold-season surface air temperatures with associated errors. The strengths and weaknesses of this approach are thoroughly tested. One much-debated, basic assumption for reconstructions (‘stationarity’), which states that only the environmental variable of interest has influenced cyst assemblages and the influence of confounding variables is negligible over time, is addressed in detail. Our inferences show that past cold-season air-temperature fluctuations were substantial and larger than those of other temperature reconstructions for Europe and the Alpine region. Interestingly, in this study, recent cold-season temperatures only just exceed those of previous, multi-decadal warm phases since AD 1500. These findings highlight the importance of local studies to assess natural climate variability at high altitudes.

A tutorial on data-driven methods for statistically assessing ERP topographies

Dynamic changes in ERP topographies can be conveniently analyzed by means of microstates, the so-called "atoms of thoughts", that represent brief periods of quasi-stable synchronized network activation. Comparing temporal microstate features such as on- and offset or duration between groups and conditions therefore allows a precise assessment of the timing of cognitive processes. So far, this has been achieved by assigning the individual time-varying ERP maps to spatially defined microstate templates obtained from clustering the grand mean data into predetermined numbers of topographies (microstate prototypes). Features obtained from these individual assignments were then statistically compared. This has the problem that the individual noise dilutes the match between individual topographies and templates leading to lower statistical power. We therefore propose a randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data. In addition, we propose a new criterion to select the optimal number of microstate prototypes based on cross-validation across subjects. After a formal introduction, the method is applied to a sample data set of an N400 experiment and to simulated data with varying signal-to-noise ratios, and the results are compared to existing methods. In a first comparison with previously employed statistical procedures, the new method showed an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing. We conclude that the proposed method is well-suited for the assessment of timing differences in cognitive processes. The increased statistical power allows identifying more subtle effects, which is particularly important in small and scarce patient populations.

Are clustering effects accounted for in statistical analysis in leading dental specialty journals?

OBJECTIVES In dental research multiple site observations within patients or taken at various time intervals are commonplace. These clustered observations are not independent; statistical analysis should be amended accordingly. This study aimed to assess whether adjustment for clustering effects during statistical analysis was undertaken in five specialty dental journals. METHODS Thirty recent consecutive issues of Orthodontics (OJ), Periodontology (PJ), Endodontology (EJ), Maxillofacial (MJ) and Paediatric Dentristry (PDJ) journals were hand searched. Articles requiring adjustment accounting for clustering effects were identified and statistical techniques used were scrutinized. RESULTS Of 559 studies considered to have inherent clustering effects, adjustment for this was made in the statistical analysis in 223 (39.1%). Studies published in the Periodontology specialty accounted for clustering effects in the statistical analysis more often than articles published in other journals (OJ vs. PJ: OR=0.21, 95% CI: 0.12, 0.37, p<0.001; MJ vs. PJ: OR=0.02, 95% CI: 0.00, 0.07, p<0.001; PDJ vs. PJ: OR=0.14, 95% CI: 0.07, 0.28, p<0.001; EJ vs. PJ: OR=0.11, 95% CI: 0.06, 0.22, p<0.001). A positive correlation was found between increasing prevalence of clustering effects in individual specialty journals and correct statistical handling of clustering (r=0.89). CONCLUSIONS The majority of studies in 5 dental specialty journals (60.9%) examined failed to account for clustering effects in statistical analysis where indicated, raising the possibility of inappropriate decreases in p-values and the risk of inappropriate inferences.

Multiscale methods for shape constraints in deconvolution: Confidence statements for qualitative features

We derive multiscale statistics for deconvolution in order to detect qualitative features of the unknown density. An important example covered within this framework is to test for local monotonicity on all scales simultaneously. We investigate the moderately ill-posed setting, where the Fourier transform of the error density in the deconvolution model is of polynomial decay. For multiscale testing, we consider a calibration, motivated by the modulus of continuity of Brownian motion. We investigate the performance of our results from both the theoretical and simulation based point of view. A major consequence of our work is that the detection of qualitative features of a density in a deconvolution problem is a doable task, although the minimax rates for pointwise estimation are very slow.

Patient-reported outcomes in meta-analyses - part 2: methods for improving interpretability for decision-makers

Systematic reviews and meta-analyses of randomized trials that include patient-reported outcomes (PROs) often provide crucial information for patients, clinicians and policy-makers facing challenging health care decisions. Based on emerging methods, guidance on improving the interpretability of meta-analysis of patient-reported outcomes, typically continuous in nature, is likely to enhance decision-making. The objective of this paper is to summarize approaches to enhancing the interpretability of pooled estimates of PROs in meta-analyses. When differences in PROs between groups are statistically significant, decision-makers must be able to interpret the magnitude of effect. This is challenging when, as is often the case, clinical trial investigators use different measurement instruments for the same construct within and between individual randomized trials. For such cases, in addition to pooling results as a standardized mean difference, we recommend that systematic review authors use other methods to present results such as relative (relative risk, odds ratio) or absolute (risk difference) dichotomized treatment effects, complimented by presentation in either: natural units (e.g. overall depression reduced by 2.4 points when measured on a 50-point Hamilton Rating Scale for Depression); minimal important difference units (e.g. where 1.0 unit represents the smallest difference in depression that patients, on average, perceive as important the depression score was 0.38 (95%CI 0.30 to 0.47) units less than the control group); or a ratio of means (e.g. where the mean in the treatment group is divided by the mean in the control group, the ratio of means is 1.27, representing a 27%relative reduction in the mean depression score).