Hal S. Stern

The Prevention and Treatment of Missing Data in Clinical Trials

Missing data in clinical trials can have a major effect on the validity of the inferences that can be drawn from the trial. This article reviews methods for preventing missing data and, failing that, dealing with data that are missing.

The use of multiple imputation for the analysis of missing data.

This article provides a comprehensive review of multiple imputation (MI), a technique for analyzing data sets with missing values. Formally, MI is the process of replacing each missing data point with a set of m > 1 plausible values to generate m complete data sets. These complete data sets are then analyzed by standard statistical software, and the results combined, to give parameter estimates and standard errors that take into account the uncertainty due to the missing data values. This article introduces the idea behind MI, discusses the advantages of MI over existing techniques for addressing missing data, describes how to do MI for real problems, reviews the software available to implement MI, and discusses the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI.

LOGISTIC REGRESSION FOR EMPIRICAL STUDIES OF MULTIVARIATE SELECTION

Understanding the mechanics of adaptive evolution requires not only knowing the quantitative genetic bases of the traits of interest but also obtaining accurate measures of the strengths and modes of selection acting on these traits. Most recent empirical studies of multivariate selection have employed multiple linear regression to obtain estimates of the strength of selection. We reconsider the motivation for this approach, paying special attention to the effects of nonnormal traits and fitness measures. We apply an alternative statistical method, logistic regression, to estimate the strength of selection on multiple phenotypic traits. First, we argue that the logistic regression model is more suitable than linear regression for analyzing data from selection studies with dichotomous fitness outcomes. Subsequently, we show that estimates of selection obtained from the logistic regression analyses can be transformed easily to values that directly plug into equations describing adaptive microevolutionary change. Finally, we apply this methodology to two published datasets to demonstrate its utility. Because most statistical packages now provide options to conduct logistic regression analyses, we suggest that this approach should be widely adopted as an analytical tool for empirical studies of multivariate selection.

P50 sensory gating ratios in schizophrenics and controls: A review and data analysis

Many studies have found that the P50 sensory gating ratio in a paired click task is smaller in normal control subjects than in patients with schizophrenia, indicating more effective sensory gating. However, a wide range of gating ratios has been reported in the literature for both groups. The purpose of this study was to compile these findings and to compare reported P50 gating ratios in controls and patients with schizophrenia. Current data collected from individual controls in eight studies from the University of California, Irvine (UCI), Indiana University (IU), and Yale University also are reported. The IU, UCI, and Yale data showed that approximately 40% of controls had P50 ratios within 1 S.D. below the mean of means for patients with schizophrenia. The meta-analysis rejected the null hypothesis that all studies showed no effect. The meta-analysis also showed that the differences were not the same across all studies. The mean ratios in 45 of the 46 group comparisons were smaller for controls than for patients, and the observed difference in means was significant for 35 of those studies. Reported gating ratios for controls from two laboratories whose findings were reported in the literature differed from all the other control groups. Variables affecting the gating ratio included band pass filter setting, rules regarding the inclusion of P30, sex, and age. Standards of P50 collection and measurement would help determine whether the gating ratio can be sufficiently reliable to be labeled an endophenotype, and suggestions are made toward this goal.

Test-retest and between-site reliability in a multicenter fMRI study.

In the present report, estimates of test–retest and between‐site reliability of fMRI assessments were produced in the context of a multicenter fMRI reliability study (FBIRN Phase 1, www.nbirn.net). Five subjects were scanned on 10 MRI scanners on two occasions. The fMRI task was a simple block design sensorimotor task. The impulse response functions to the stimulation block were derived using an FIR‐deconvolution analysis with FMRISTAT. Six functionally‐derived ROIs covering the visual, auditory and motor cortices, created from a prior analysis, were used. Two dependent variables were compared: percent signal change and contrast‐to‐noise‐ratio. Reliability was assessed with intraclass correlation coefficients derived from a variance components analysis. Test–retest reliability was high, but initially, between‐site reliability was low, indicating a strong contribution from site and site‐by‐subject variance. However, a number of factors that can markedly improve between‐site reliability were uncovered, including increasing the size of the ROIs, adjusting for smoothness differences, and inclusion of additional runs. By employing multiple steps, between‐site reliability for 3T scanners was increased by 123%. Dropping one site at a time and assessing reliability can be a useful method of assessing the sensitivity of the results to particular sites. These findings should provide guidance toothers on the best practices for future multicenter studies. Hum Brain Mapp, 2008.

Neural networks in applied statistics

Artificial neural networks are computer algorithms or computer programs derived in part from attempts to model the activity of nerve cells. They have been applied to pattern recognition, classification, and optimization problems in the physical and chemical sciences, as well as in other fields. We introduce the principles of the multilayer feedforward network that is among the most commonly used neural networks in practical problems. The relevance of neural network models for the applied statistician is considered using a time series prediction problem as an example. The multilayer feedforward neural network uses a nonlinear function of the predictors to obtain predictions for future time series values. We illustrate the considerations involved in specifying a neural network model and evaluate the accuracy of neural network models relative to the accuracy obtained using other computer-intensive, nonmodel-based techniques.

Posterior Predictive Assessment of Item Response Theory Models.

Model checking in item response theory (IRT) is an underdeveloped area. There is no universally accepted tool for checking IRT models. The posterior predictive model-checking method is a popular Bayesian model-checking tool because it has intuitive appeal, is simple to apply, has a strong theoretical basis, and can provide graphical or numerical evidence about model misfit. An important issue with the application of the posterior predictive model-checking method is the choice of a discrepancy measure (which plays a role like that of a test statistic in traditional hypothesis tests). This article examines the performance of a number of discrepancy measures for assessing different aspects of fit of the common IRT models and makes specific recommendations about what measures are most useful in assessing model fit. Graphical summaries of model-checking results are demonstrated to provide useful insights about model fit.

Posterior predictive model checks for disease mapping models

Disease incidence or disease mortality rates for small areas are often displayed on maps. Maps of raw rates, disease counts divided by the total population at risk, have been criticized as unreliable due to non-constant variance associated with heterogeneity in base population size. This has led to the use of model-based Bayes or empirical Bayes point estimates for map creation. Because the maps have important epidemiological and political consequences, for example, they are often used to identify small areas with unusually high or low unexplained risk, it is important that the assumptions of the underlying models be scrutinized. We review the use of posterior predictive model checks, which compare features of the observed data to the same features of replicate data generated under the model, for assessing model fitness. One crucial issue is whether extrema are potentially important epidemiological findings or merely evidence of poor model fit. We propose the use of the cross-validation posterior predictive distribution, obtained by reanalyzing the data without a suspect small area, as a method for assessing whether the observed count in the area is consistent with the model. Because it may not be feasible to actually reanalyze the data for each suspect small area in large data sets, two methods for approximating the cross-validation posterior predictive distribution are described.

Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies.

This report provides practical recommendations for the design and execution of multicenter functional MRI (MC‐fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC‐fMRI studies. The introduction briefly discusses the advantages and complexities of MC‐fMRI studies. Prerequisites for MC‐fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC‐fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC‐fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery. J. Magn. Reson. Imaging 2012;36:39–54.

Fragmentation and Unpredictability of Early-Life Experience in Mental Disorders

Maternal sensory signals in early life play a crucial role in programming the structure and function of the developing brain, promoting vulnerability or resilience to emotional and cognitive disorders. In rodent models of early-life stress, fragmentation and unpredictability of maternally derived sensory signals provoke persistent cognitive and emotional dysfunction in offspring. Similar variability and inconsistency of maternal signals during both gestation and early postnatal human life may influence development of emotional and cognitive functions, including those that underlie later depression and anxiety.