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PubMed2004

Assessing teratogenicity of antiretroviral drugs: monitoring and analysis plan of the Antiretroviral Pregnancy Registry.

This paper describes the Antiretroviral Pregnancy Registry's (APR) monitoring and analysis plan. APR is overseen by a committee of experts in obstetrics, pediatrics, teratology, infectious diseases, epidemiology and biostatistics from academia, government and the pharmaceutical industry. APR uses a prospective exposure-registration cohort design. Clinicians voluntarily register pregnant women with prenatal exposures to any antiretroviral therapy and provide fetal/neonatal outcomes. A birth defect is any birth outcome > or = 20 weeks gestation with a structural or chromosomal abnormality as determined by a geneticist. The prevalence is calculated by dividing the number of defects by the total number of live births and is compared to the prevalence in the CDC's population-based surveillance system. Additionally, first trimester exposures, in which organogenesis occurs, are compared with second/third trimester exposures. Statistical inference is based on exact methods for binomial proportions. Overall, a cohort of 200 exposed newborns is required to detect a doubling of risk, with 80% power and a Type I error rate of 5%. APR uses the Rule of Three: immediate review occurs once three specific defects are reported for a specific exposure. The likelihood of finding three specific defects in a cohort of < or = 600 by chance alone is less than 5% for all but the most common defects. To enhance the assurance of prompt, responsible, and appropriate action in the event of a potential signal, APR employs the strategy of 'threshold'. The threshold for action is determined by the extent of certainty about the cases, driven by statistical considerations and tempered by the specifics of the cases.

Abnormalities, Drug-Induced↗
PubMedDate not supplied

Methodological approaches to the analysis of hierarchical studies of air pollution and respiratory health--examples from the CESAR study. Central European Study on Air pollution and Respiratory Health.

OBJECTIVES: Many studies of air pollution and health are carried out over several geographical areas, and sometimes over several countries. This paper explores three approaches to analysis in such studies: a non hierarchical model, a two-stage analysis, and multilevel modelling. Illustrations are given using a preliminary subset of data from the CESAR study. DESIGN: The Central European Study on Air pollution and Respiratory Health (CESAR) was conducted in 25 areas within six Central European countries, enrolling 20,271 schoolchildren. Pollution averages were calculated for each area. Associations between pollution and health outcomes were estimated under different models. MAIN RESULTS: A regression analysis of log FVC (forced vital capacity) on PM10, ignoring the geographical hierarchy, estimated a significant mean drop in FVC (adjusted for confounders) of 2.2% (95% CI 0.5% to 1.3%), p=0.007, from the area with the lowest PM10 to that with the highest. A multilevel model (mlm), using data for all children, but with random effects at area and country level, estimated a drop of 2.8% (-0.6% to 6.1%), p=0.110. A two-stage analysis (mean log FVC, adjusted for confounders, was estimated for each area using regression, and these means then regressed on PM10) estimated a drop of 2.6% (-0.5% to 5.5%), p=0.101. Simulation exercises showed the non hierarchical method to be very inadequate in the context of the CESAR study, with only half of all 95% confidence intervals for the estimated PM10 slope containing the true value (i.e., that used to create the simulated data). The two-stage and multilevel modelling methods gave results which were substantially better, though both underperformed slightly. All three methods appeared to give unbiased slope estimates. CONCLUSIONS: Acknowledgement of hierarchical structures is essential in statistical inference--standard errors can be substantially incorrect when they are ignored. Multilevel, random-effects models correctly address hierarchical structures, though having few units at higher levels can cause problems in convergence, especially where complex modelling is required. Two-stage analyses, acknowledging hierarchy, provide simple alternatives to random-effects models.

Air Pollutants↗
PubMed2004

A resident research director can improve internal medicine resident research productivity.

BACKGROUND: Resident participation in research projects is felt to be an important component of internal medicine residency training, and accreditation organizations require that residency programs show that their residents and faculty participate in scholarly activity. PURPOSE: To determine the impact of a Resident Research Director (RRD) on scholarly productivity of our internal medicine residents. METHODS: We reviewed the number of presentations and publications of all residents from our institution over a 10-year study period (1992-2001). We used a historical control, comparing resident presentations and publications 5 years before (1992-1996) and after (1997-2001) implementation of the RRD position. We compared cohorts in terms of number of individuals in Alpha Omega Alpha and the number of individuals coming from a top 50 medical school as baseline measurements. We also compared these cohorts in regards to faculty to learner ratio, percentage of residents applying for fellowship, and American Board of Internal Medicine Certifying Examination performance. The Mann-Whitney U test was used for statistical inferences. Eighty-nine residents trained at our institution during the study period. RESULTS: There was a significant increase in the number of regional and national presentations as well as publications after instituting the RRD position. CONCLUSION: Our analysis suggests that an RRD can enhance resident scholarly productivity.

Attitude of Health Personnel↗
PubMed1998

Increased efficiency of analyses: cumulative logistic regression vs ordinary logistic regression.

The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead of a dichotomous specification for an inherently continuous response. Ordinary (OLR) and cumulative logistic regression (CLR) modeling were used to test the hypothesis that the risk of alveolar bone loss over 2 years is greater for subjects with poorer control of non-insulin-dependent diabetes mellitus (NIDDM) than for those who do not have diabetes or have better controlled NIDDM. There were 359 subjects; 21 of whom had NIDDM. Analysis of main effects using OLR for the dichotomous outcome (no change in radiographic bone loss vs any change) produced parameter estimates for better control and poorer control that were not statistically significant. CLR analysis of main effects using a 4-category ordinal specification for radiographic bone loss also produced a parameter estimate for better control that was not statistically significant, but which estimated poorer control to have a significant effect. The fit of this CLR model was significantly better at P < 0.05 than that for the OLR. While an OLR model testing the interaction between age and control status did not converge after 100 iterations, the CLR interaction model converged without difficulty and estimated a significant effect for interaction between age and poorer control. Results from the CLR analysis, in contrast to the OLR model, would lead one to conclude that the risk for more severe bone loss progression after 2 years is greater in subjects with poorer controlled NIDDM and that subjects with better controlled NIDDM may not have greater risk of bone loss progression than those without diabetes. The use of an ordinal instead of a dichotomous specification for an inherently continuous response provided increased power, more precise parameter estimates, and a significantly better fitting model. In estimating parameter estimates for odds ratios or risks, it is important to consider using ordinal logistic regression where the response is inherently continuous or ordinal.

Adolescent↗
PubMed2004

Statistical estimation of resistance/conductance by electrical impedance tomography measurements.

This paper is built upon the assumption that in electrical impedance tomography, vectors of voltages and currents are linearly dependent through a resistance matrix. This linear relationship was confirmed experimentally and may be derived analytically under certain assumptions regarding electrodes (Isaacson, 1991). Given measurement data consisting of voltages and currents, we treat this relationship as a linear statistical model. Thus, our goal is not to reconstruct the image but directly estimate its electromagnetic properties reflected in the resistance and/or conductance matrix using electrical impedance tomography (EIT) measurements of voltages and currents on the periphery of the body. Since no inverse problem is involved the algorithm for estimation merely reduces to one matrix inversion. We estimate the impedance resistance matrix using well established statistical inference techniques for linear regression models. We provide a comprehensive treatment for a two-dimensional homogeneous body of a circular shape, by which many concepts of electrical impedance tomography, such as width of electrodes, the difference between voltage-current and current-voltage systems are illustrated. Our theory may be applied to various tests including EIT hardware calibration and whether the medium is homogeneous. These tests are illustrated on phantom agar data.

Agar↗
PubMed2001

The stability of corneal topography in the post-blink interval.

PURPOSE: Videokeratoscopes provide a wealth of information about the topography of the ocular surface. Although there have been numerous studies of the accuracy and precision of videokeratoscopes with inanimate test objects, little information exists on their precision (repeatability) for real eyes. METHODS: To investigate the stability of the ocular surface in the inter-blink period, 10 patients were recruited for videokeratoscopy. Tear break-up time was measured and videokeratographs were acquired immediately post-blink and again at 4, 8, and 12 seconds post-blink. To permit statistical inferences to be drawn from the data, we acquired 24 videokeratographs for each of the four post-blink intervals. The videokeratograph data were interpolated (bilinear) to a common grid, and average and standard deviation (SD) maps were derived for each post-blink condition. t Tests were used to test the significance of changes observed in the topography. RESULTS: The instantaneous power SD maps showed increasing variation toward the periphery, with most maps showing less than +/-0.5 diopters (D) of SD in the central 4 to 5 mm and variation in the periphery often reaching more than +/-1 D SD at the edge of an 8-mm diameter. When the 4-, 8-, and 12-second average maps were subtracted from the average map acquired immediately after blinking, regions of statistically significant ( p < 0.001) change were apparent in the upper and lower regions of the maps. The upper and lower bands of change were found to correlate with the natural position of the patients' lid margins. CONCLUSIONS: For normal eyes, the central regions of videokeratographs show high stability in the inter-blink period. However, the upper and lower edges of 8-mm diameter maps show statistically significant variability, which appears to be related to the effects of eyelid pressure.

Adult↗
PubMed2005

A Bayesian approach to modeling dynamic effective connectivity with fMRI data.

A state-space modeling approach for examining dynamic relationship between multiple brain regions was proposed in Ho, Ombao and Shumway (Ho, M.R., Ombao, H., Shumway, R., 2005. A State-Space Approach to Modelling Brain Dynamics to Appear in Statistica Sinica). Their approach assumed that the quantity representing the influence of one neuronal system over another, or effective connectivity, is time-invariant. However, more and more empirical evidence suggests that the connectivity between brain areas may be dynamic which calls for temporal modeling of effective connectivity. A Bayesian approach is proposed to solve this problem in this paper. Our approach first decomposes the observed time series into measurement error and the BOLD (blood oxygenation level-dependent) signals. To capture the complexities of the dynamic processes in the brain, region-specific activations are subsequently modeled, as a linear function of the BOLD signals history at other brain regions. The coefficients in these linear functions represent effective connectivity between the regions under consideration. They are further assumed to follow a random walk process so to characterize the dynamic nature of brain connectivity. We also consider the temporal dependence that may be present in the measurement errors. ML-II method (Berger, J.O., 1985. Statistical Decision Theory and Bayesian Analysis (2nd ed.). Springer, New York) was employed to estimate the hyperparameters in the model and Bayes factor was used to compare among competing models. Statistical inference of the effective connectivity coefficients was based on their posterior distributions and the corresponding Bayesian credible regions (Carlin, B.P., Louis, T.A., 2000. Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). Chapman and Hall, Boca Raton). The proposed method was applied to a functional magnetic resonance imaging data set and results support the theory of attentional control network and demonstrate that this network is dynamic in nature.

Attention↗
PubMed1996

Randomization test for coupled data.

Coupled data arise in perceptual research when subjects are contributing two scores to the data pool. These two scores, it can be reasonably argued, cannot be assumed to be independent of one another; therefore, special treatment is needed when performing statistical inference. This paper shows how the Type I error rate of randomization-based inference is affected by coupled data. It is demonstrated through Monte Carlo simulation that a randomization test behaves much like its parametric counterpart except that, for the randomization test, a negative correlation results in an inflation in the Type I error rate. A new randomization test, the couplet-referenced randomization test, is developed and shown to work for sample sizes of 8 or more observations. An example is presented to demonstrate the computation and interpretation of the new randomization test.

Bias↗
PubMed2000

Inflammation after sclerocorneal versus clear corneal tunnel phacoemulsification.

OBJECTIVE: To compare the postoperative inflammation after phacoemulsification followed by intraocular lens (IOL) implantation by means of sclerocorneal versus clear corneal tunnel incision. DESIGN: Randomized controlled clinical trial. PARTICIPANTS: One hundred eyes of 100 patients were examined at a German University eye hospital. INTERVENTION: One hundred eyes with cataract necessitating phacoemulsification with posterior chamber IOL implantation were randomly assigned to receive a temporal sclerocorneal or clear corneal tunnel incision by a single surgeon. MAIN OUTCOME MEASURES: Preoperative and postoperative inflammation was evaluated by measurement of flare using laser flare photometry. Statistical inference was mainly based on nonparametric group comparisons by use of two sample Wilcoxon tests. RESULTS: Mean anterior chamber flare in the group with sclerocorneal tunnel increased from 7.5 photon counts/ms preoperatively to 19.6 at 6 hours postoperatively and decreased to 11.1 (day 1), 11.7 (day 2), 11.6 (day 3), and 9.2 (5 months) during the postoperative course. The mean flare in the clear corneal tunnel incision group increased from 7.7 preoperatively to 12.9 at 6 hours postoperatively and then decreased to 9.2 (day 1), 9.8 (day 2), 9.1 (day 3), and 9.2 (5 months). Individual postoperative flare changes were significantly lower in the clear corneal tunnel group at the day of surgery (P<0.0001), as well as at day 1 (P = 0.0011), day 2 (P = 0.0079), and day 3 (P = 0.0020). After 5 months, no statistically significant difference was found. CONCLUSIONS: After phacoemulsification and foldable IOL implantation, postoperative alteration in the blood-aqueous barrier was statistically significantly lower with the clear corneal tunnel incision group compared with the sclerocorneal incision group, in the first 3 days postoperatively.

Adult↗
PubMed2005

Tutorial: using confidence curves in medical research.

Confidence intervals represent a routinely used standard method to document the uncertainty of estimated effects. In most cases, for the calculation of confidence intervals the conventional fixed 95% confidence level is used. Confidence curves represent a graphical illustration of confidence intervals for confidence levels varying between 0 and 100%. Although such graphs have been repeatedly proposed under different names during the last 40 years, confidence curves are rarely used in medical research. In this paper, we introduce confidence curves and present a short historical review. We draw attention to the different interpretation of one- and two-sided statistical inference. It is shown that these two options also have influence on the plotting of appropriate confidence curves. We illustrate the use of one- and two-sided confidence curves and explain their correct interpretation. In medical research more emphasis on the choice between the one- and two-sided approaches should be given. One- and two-sided confidence curves are useful complements to the conventional methods of presenting study results.

Animals↗
PubMed2003

Hypothesis testing I: proportions.

Statistical inference involves two analysis methods: estimation and hypothesis testing, the latter of which is the subject of this article. Specifically, Z tests of proportion are highlighted and illustrated with imaging data from two previously published clinical studies. First, to evaluate the relationship between nonenhanced computed tomographic (CT) findings and clinical outcome, the authors demonstrate the use of the one-sample Z test in a retrospective study performed with patients who had ureteral calculi. Second, the authors use the two-sample Z test to differentiate between primary and metastatic ovarian neoplasms in the diagnosis and staging of ovarian cancer. These data are based on a subset of cases from a multiinstitutional ovarian cancer trial conducted by the Radiologic Diagnostic Oncology Group, in which the roles of CT, magnetic resonance imaging, and ultrasonography (US) were evaluated. The statistical formulas used for these analyses are explained and demonstrated. These methods may enable systematic analysis of proportions and may be applied to many other radiologic investigations.

Diagnostic Imaging↗
PubMed2002

Bayesian analysis: a new statistical paradigm for new technology.

Full Bayesian analysis is an alternative statistical paradigm, as opposed to traditionally used methods, usually called frequentist statistics. Bayesian analysis is controversial because it requires assuming a prior distribution, which can be arbitrarily chosen; thus there is a subjective element, which is considered to be a major weakness. However, this could also be considered a strength since it provides a formal way of incorporating prior knowledge. Since it is flexible and permits repeated looks at evolving data, Bayesian analysis is particularly well suited to the evaluation of new medical technology. Bayesian analysis can refer to a range of things: from a simple, noncontroversial formula for inverting probabilities to an alternative approach to the philosophy of science. Its advantages include: (1) providing direct probability statements--which are what most people wrongly assume they are getting from conventional statistics; (2) formally incorporating previous information in statistical inference of a data set, a natural approach which we follow in everyday reasoning; and (3) flexible, adaptive research designs allowing multiple looks at accumulating study data. Its primary disadvantage is the element of subjectivity which some think is not scientific. We discuss and compare frequentist and Bayesian approaches and provide three examples of Bayesian analysis: (1) EKG interpretation, (2) a coin-tossing experiment, and (3) assessing the thromboembolic risk of a new mechanical heart valve.

Bayes Theorem↗
PubMed1992

Relative persistence capacity of BCG substrains in mouse spleen. Computerized statistical analysis. Multiple comparison.

The relative persistence capacity in mouse spleen of 10 and 9 BCG substrains from liquid and dried vaccines, respectively, was evaluated in two studies. Recoverable BCG colony counts from mouse spleen were determined at given days on solid medium in the two studies during a period of 1-360 and 1-345 days, respectively, after the intravenous BCG vaccination, performed with two different viable units. From 36,000 (study 1) and 21,600 (study 2) recoverable BCG colony counts, 180 and 108 mean relative persistence capacity values were estimated to test the residual virulence during the follow-up time, using computerized statistical analysis. The early and late trends of mean relative persistence capacity of the BCG substrains in mouse spleen were tested by linear regression analysis and analysis of variance and covariance; then with ranked adjusted group mean relative persistence capacity, Gabriel's simultaneous test procedure was performed for multiple comparison to diminish type 1 error in statistical inference and in objective interpretation of the experimental results. The associations of the ranked mean relative persistence capacity of the BCG substrains at the different sacrifice days of mice were also analyzed by Kendall's test of concordance. The early, late, and overall relative persistence capacity reflects the residual virulence of the BCG substrains and provides information on the required protective efficacy (immunogenicity) and adverse reactions (reactogenicity), allowing the appropriate vaccination dose, expressed in viable units of the substrain used, to be determined.

Animals↗
PubMed2005

Handling of dioxin measurement data in the presence of non-detectable values: overview of available methods and their application in the Seveso chloracne study.

Exposure measurements of concentrations that are non-detectable or near the detection limit (DL) are common in environmental research. Proper statistical treatment of non-detects is critical to avoid bias and unnecessary loss of information. In the present work, we present an overview of possible statistical strategies for handling non-detectable values, including deletion, simple substitution, distributional methods, and distribution-based imputation. Simple substitution methods (e.g., substituting 0, DL/2, DL/ radical2, or DL for the non-detects) are the most commonly applied, even though the EPA Guidance for Data Quality Assessment discouraged their use when the percentage of non-detects is >15%. Distribution-based multiple imputation methods, also known as robust or "fill-in" procedures, may produce dependable results even when 50-70% of the observations are non-detects and can be performed using commonly available statistical software. Any statistical analysis can be conducted on the imputed datasets. Results properly reflect the presence of non-detectable values and produce valid statistical inference. We describe the use of distribution-based multiple imputation in a recent investigation conducted on subjects from the Seveso population exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), in which 55.6% of plasma TCDD measurements were non-detects. We suggest that distribution-based multiple imputation be the preferred method to analyze environmental data when substantial proportions of observations are non-detects.

Acne Vulgaris↗
PubMed1986

Considerations in the statistical analysis of clinical trials in periodontitis.

Adult periodontitis has been described as a chronic infectious process exhibiting sporadic, acute exacerbations which cause quantal, localized losses of dental attachment. Many analytic problems of periodontal trials are similar to those of other chronic diseases. However, the episodic, localized, infrequent, and relatively unpredictable behavior of exacerbations, coupled with measurement error difficulties, cause some specific problems. Considerable controversy exists as to the proper selection and treatment of multiple site data from the same patient for group comparisons for epidemiologic or therapeutic evaluative purposes. This paper comments, with varying degrees of emphasis, on several issues pertinent to the analysis of periodontal trials. Considerable attention is given to the ways in which measurement variability may distort analytic results. Statistical treatments of multiple site data for descriptive summaries are distinguished from treatments for formal statistical inference to validate therapeutic effects. Evidence suggesting that sites behave independently is contested. For inferential analyses directed at therapeutic or preventive effects, analytic models based on site independence are deemed unsatisfactory. Methods of summarization that may yield more powerful analyses than all-site mean scores, while retaining appropriate treatment of inter-site associations, are suggested. Brief comments and opinions on an assortment of other issues in clinical trial analysis are preferred.

Analysis of Variance↗
PubMed2004

dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data.

MOTIVATION: Oligonucleotide microarrays allow genotyping of thousands of single-nucleotide polymorphisms (SNPs) in parallel. Recently, this technology has been applied to loss-of-heterozygosity (LOH) analysis of paired normal and tumor samples. However, methods and software for analyzing such data are not fully developed. RESULT: Here, we report automated methods for pooling SNP array replicates to make LOH calls, visualizing SNP and LOH data along chromosomes in the context of genes and cytobands, making statistical inference to identify shared LOH regions, clustering samples based on LOH profiles and correlating the clustering results to clinical variables. Application of these methods to prostate and breast cancer datasets generates biologically important results. AVAILABILITY: The software module dChipSNP implementing these methods is available at http://biosun1.harvard.edu/complab/dchip/snp/ SUPPLEMENTARY INFORMATION: The breast cancer data are provided by Andrea L. Richardson, Zhigang C. Wang and James D. Iglehart.

Algorithms↗
PubMed2005

Robust estimating functions and bias correction for longitudinal data analysis.

Robust methods are useful in making reliable statistical inferences when there are small deviations from the model assumptions. The widely used method of the generalized estimating equations can be "robustified" by replacing the standardized residuals with the M-residuals. If the Pearson residuals are assumed to be unbiased from zero, parameter estimators from the robust approach are asymptotically biased when error distributions are not symmetric. We propose a distribution-free method for correcting this bias. Our extensive numerical studies show that the proposed method can reduce the bias substantially. Examples are given for illustration.

Age Factors↗
PubMed1999

Classification trees and logistic regression applied to prognostic studies: a comparison using meningococcal disease as an example.

The authors used logistic regression and classification trees to develop prediction models for fatal outcomes in meningococcal disease in a cohort of 829 children hospitalized for meningococcal disease during 1989-1990 in Rio de Janeiro. The area under the receiver operator characteristic (ROC) curve was 92 per cent for logistic regression and 88 per cent for classification trees. Logistic regression may be preferred when the main objective is to obtain explicit measures for statistical inference and measures of the force of the association between each variable and the outcome. However, estimation of the probability of dying for each patient involves manipulation of the logistic regression formula, which would not easily be done in an emergency room. Classification trees provided comparable discrimination between fatal and non-fatal outcomes, and yielded a graphical display of the results that is easier to understand and is straightforward to apply in clinical settings.

Adolescent↗