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PubMed1991

Assessing, accommodating, and interpreting the influences of heterogeneity.

Heterogeneity, ranging from measurement error to variation among individuals or regions, influences all levels of data collected for risk assessment. In its role as a nemesis, heterogeneity can reduce the precision of estimates, change the shape of a population model, or reduce the generalizability of study results. In many contexts, however, heterogeneity is the primary object of inference. Indeed, some degree of heterogeneity in excess of a baseline amount associated with a statistical model is necessary in order to identify important determinants of response. This report outlines the causes and influences of heterogeneity, develops statistical methods used to estimate and account for it, discusses interpretations of heterogeneity, and shows how it should influence study design. Examples from dose-response modeling, identification of sensitive individuals, assessment of small area variations and meta analysis provide applied contexts.

Animals↗
PubMed2004

Realism in evidence based medicine: interpreting the randomised controlled trial.

Realism is emerging as a paradigm for research and explanation in the natural and social sciences. A realist framework is elaborated and applied to the four possible situations that may generate the observations of randomised, controlled trials. It is demonstrated that by using two realist concepts "mechanism" and "context" a number of misinterpretations of such trials from within the dominant empiricist paradigm may be rectified. Evidence based medicine should adopt realism to temper a misleading empiricism, this will involve relegating statistical arguments to their proper subsidiary place and adopting an adequate theory of causation.

Causality↗
PubMed2006

[Comparison of two or more samples of quantitative data].

In the study of the difference between the two or more data groups, first a scientific hypothesis is to be presented involving guessing, questing, and hypothesizing that motivate research. Statistical hypothesis is drawn from the scientific hypothesis, namely, the hypothesis of the researcher (which, as a rule, is affirmative). The mode of statistical hypothesis is presented so as to be valued by statistical and analytical procedures. Testing of the hypothesis is a statistical procedure that can determine whether and how reliably the available data support the given hypothesis. Testing of hypotheses, namely, testing of the significance is basically the procedure of the quantification of impressions regarding the specific hypothesis. The sequence of actions in the testing of the hypothesis: stating a null and an alternative hypothesis; the choice of the significance level (alpha); collection of relevant data based on the adequate sample of subjects; evaluation of the value of results of the statistical test specific for the null hypothesis (Ho); comparison of results of the statistical test with the values from the known value distribution specific for the given test; interpretation of the statistical test results in the probability terms (P-value). The Ho is a supposition of the absence of effect, i.e. that there is no difference between the samples in the population of interest (for example, that there is no difference in arithmetical means). This is a hypothesis of no difference. It is (mostly) made with the aim of rejection. It is either rejected or accepted. The choice of the corresponding statistical test depends on the researcher's design (dependent or independent study design of two or more data samples) as well as on the nature of data (distribution normality and variance homogeneity). Parametric tests carry more strength (the power to detect the difference if in reality it really exists) than nonparametric or distribution free tests. The latter are practical for smaller samples and situations in which the conditions for conducting parametric tests have not been satisfied.

Analysis of Variance↗
PubMed2006

A simulation study comparing properties of heterogeneity measures in meta-analyses.

The assessment of heterogeneity or between-study variance is an important issue in meta-analysis. It determines the statistical methods to be used and the interpretation of the results. Tests of heterogeneity may be misleading either due to low power for sparse data or to the detection of irrelevant amounts of heterogeneity when many studies are involved. In the former case, notable heterogeneity may remain unconsidered and an unsuitable model may be chosen and the latter case may lead to unnecessary complex analyses strategies. Measures of heterogeneity are better suited to determine appropriate analyses strategies. We review two measures with different scaling and compare them with the heterogeneity test. Estimates of the within-study variance are discussed and a new total information measure is introduced. Various properties of the quantities in question are assessed by a simulation study. Heterogeneity test and measures are not directly related to the amount of between-study variance but to the relative increase of variance due to heterogeneity. It is more favourable to base the within-study variance estimate on the squared weights of individual studies than on the sum of weights. A heterogeneity measure scaled to a fixed interval needs reference values for proper interpretation. A measure defined by the relation of between- to within-study variance has a more natural interpretation but no upper limit. Both measures are quantifications of the impact of heterogeneity on the meta-analysis result as both depend on the variance of the individual study effects and thus on the number of patients in the studies.

Biomedical Research↗
PubMed2004

Analysis of DNA microarray data.

Recent advances in DNA microarray technology have great impact on many areas of biomedical research and pharmacogenomics: discovering novel targets and genes, elucidating signatures of complex diseases, transcriptional profiling of models for diseases, and the development of individually optimized drugs based on differential gene expression patterns. Consequently, there is demand for robust methods for data analysis and the choice of adequate statistical tests. This review guides through all steps in the cDNA microarray data analysis pipeline and gives a basic understanding of the challenges in interpreting large microarray datasets.

Animals↗
PubMed2003

Influence of the normalization template on the outcome of statistical parametric mapping of PET scans.

Spatial normalization is an essential preprocessing step in statistical parametric mapping (SPM)-based analysis of PET scans. The standard template provided with the SPM99 software package was originally constructed using (15)O-H(2)O PET scans and is commonly applied regardless of the tracer actually used in the scans being analyzed. This work studies the effect of using three different normalization templates in the outcome of the statistical analysis of PET scans: (1) the standard SPM99 PET template; (2) an (18)F-FDG PET template, constructed by averaging PET scans previously normalized to the standard template; and (3) an MRI-aided (18)F-FDG PET template, constructed by averaging PET scans normalized according to the deformation parameters obtained from MRI scans. A strictly anatomical MRI normalization of each PET was used as a reference, under the rationale that a normalization based only upon MRI should provide higher spatial accuracy. The potential bias involved in the normalization process was estimated in a clinical SPM study comparing schizophrenic patients with control subjects. For each between-group comparison, three SPM maps were obtained, one for each template. To evaluate the influence of the template, these SPM maps were compared to the reference SPM map achieved using the anatomical normalization. SPMs obtained by MRI-aided normalization showed the highest spatial specificity, and also higher sensitivity when compared to the standard normalization using the SPM99 (15)O-H(2)O template. These results show that the use of the standard template under inappropriate conditions (different tracer or mental state) may lead to inconsistent interpretations of the statistical analysis.

Acute Disease↗
PubMed2004

Analysis of genetic marker-phenotype relationships by jack-knifed partial least squares regression (PLSR).

The utility of a relatively new multivariate method, bi-linear modelling by cross-validated partial least squares regression (PLSR), was investigated in the analysis of QTL. The distinguishing feature of PLSR is to reveal reliable covariance structures in data of different types with regard to the same set objects. Two matrices X (here: genetic markers) and Y (here: phenotypes) are interactively decomposed into latent variables (PLS components, or PCs) in a way which facilitates statistically reliable and graphically interpretable model building. Natural collinearities between input variables are utilized actively to stabilise the modelling, instead of being treated as a statistical problem. The importance of cross-validation/jack-knifing as an intuitively appealing way to avoid overfitting, is emphasized. Two datasets from chromosomal mapping studies of different complexity were chosen for illustration (QTL for tomato yield and for oat heading date). Results from PLSR analysis were compared to published results and to results using the package PLABQTL in these data sets. In all cases PLSR gave at least similar explained validation variances as the reported studies. An attractive feature is that PLSR allows the analysis of several traits/replicates in one analysis, and the direct visual identification of individuals with desirable marker genotypes. It is suggested that PLSR may be useful in structural and functional genomics and in marker assisted selection, particularly in cases with limited number of objects.

Crosses, Genetic↗
PubMedDate not supplied

Survey estimates of drug-use trends in urban communities: general principles and cautionary examples.

Surveys to depict substance abuse rates and monitor trends in specific areas have become increasingly important policy tools. Yet, as illustrated by two national multiwave surveys, using small sample survey data and making longitudinal comparisons is fraught with interpretative problems. In the case of the metropolitan area "oversample" of the National Household Survey on Drug Abuse, for example, interpreting apparent declines in drug use has to take account of the devastating effects of Hurricane Andrew in the Miami Metropolitan area. In the case of a 41-community survey sponsored by the Robert Wood Johnson Foundation to evaluate substance abuse prevention, the difficulty is how to interpret small differences in drug use, which seem to follow no reasonable pattern with respect to treatment or comparison sites. Inferences from such surveys are confounded with statistical anomalies and unforeseen events. They are limited by the sample size. In part, the solution to these problems is to use other survey and nonsurvey data to validate their conclusions and to note their limitations.

Confounding Factors, Epidemiologic↗
PubMed2003

Bayesian analysis of quantitative antimicrobial assays.

Quantitative antimicrobial assays are used to assess the efficacy of chemical germicides. Standard methods for statistical analysis use log reduction (LR), the difference on the log scale between average surviving microbes for control and test carriers, as an efficacy measure. These methods have several deficiencies. The LR parameter is not on the original response scale, which complicates its interpretation. The presence of two different definitions of LR makes the statistical inference even more difficult. Current statistical methods for antimicrobial assay analysis rely on asymptotic normal theory, which might not work well for small samples. In addition, they do not appropriately incorporate censored ('too numerous to be counted') observations in the analysis. To overcome those problems, a new Bayesian approach is introduced here. It has also the advantages of more flexible statistical inference, and incorporated prior information in the model.

Bacteria↗
PubMed2003

A note on non-parametric ANCOVA for covariate adjustment in randomized clinical trials.

Koch et al. recently (1998) proposed two covariate-adjusted approaches for the comparison of continuous, ordinal and binary responses in a randomized clinical trial (Statist. Med. 1998; 17: 1863-1892). The first is a randomization approach while the second assumes that the study is a sample of a population. Here, we study the second approach and consider the simplest cases of two treatments with a continuous response and with a binary response. Koch's second approach will be compared with the classical ANCOVA for a continuous response. From this relationship we demonstrate that Koch's method cannot preserve the probability of the type I error. Simulations with continuous responses as well as with binary outcomes confirm the aforementioned theoretical result on the performance of Koch's method under the null hypothesis of no treatment effect. However, this poses only a problem for relatively small to moderate sample sizes. Further, as specified in the original paper of Koch et al., the first approach does preserve the type I error for any sample size, as the P-values can be reported in an exact manner (Statist. Med. 1998; 17: 1863-1892). Finally, we propose a correction factor for Koch's test statistic that better preserves the type I error.

Computer Simulation↗
PubMed2006

An extended power of cluster detection tests.

Several tests have been proposed to detect spatial disease clustering without prior information on their locations. In order to compare the performance of these tests, most authors employ the usual power, i.e. the rejection probability of the null hypothesis of no clustering due to various reasons. However, the usual power is not always appropriate for evaluating the cluster detection tests since their purpose is to both reject the null hypothesis and identify the cluster areas accurately. In this paper, we propose an extended power of the cluster detection tests, which includes the usual power as a special case. Further, we define the profile of the extended power, which can be expected to play an important role in the evaluation and comparison of several cluster detection tests. The proposed extended power and its profile are demonstrated by two tests--Kulldorff's circular spatial scan statistic and a flexible spatial scan statistic proposed by Tango and Takahashi.

Cluster Analysis↗
PubMed1992

Statistical analysis of longitudinal quality of life data with missing measurements.

The statistical analysis of longitudinal quality of life data in the presence of missing data is discussed. In cancer trials missing data are generated due to the fact that patients die, drop out, or are censored. These missing data are problematic in the monitoring of the quality of life during the trial. However, by means of assuming that the cause of the missing data lies in the observed history of the patients and not in their unobserved future, the missing data are ignorable. Consequently, all available data can be used to estimate quality of life change patterns with time. The computations that are required are illustrated with real quality of life data and three commonly used computer packages for statistical analysis.

Analysis of Variance↗
PubMed2004

Effects of gum chewing on memory and attention: reply to Scholey (2004).

Recent studies have reported varying results regarding cognitive performance while chewing gum. The differences between the results of these studies may be explained by methodological and statistical factors. Future studies are needed to assess various aspects of cognitive functioning using extensive standardised neuropsychological test batteries, adequate experimental designs and appropriate statistical methods.

Attention↗
PubMed2006

Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping.

This paper proposes a 3D statistical model aiming at effectively capturing statistics of high-dimensional deformation fields and then uses this prior knowledge to constrain 3D image warping. The conventional statistical shape model methods, such as the active shape model (ASM), have been very successful in modeling shape variability. However, their accuracy and effectiveness typically drop dramatically in high-dimensionality problems involving relatively small training datasets, which is customary in 3D and 4D medical imaging applications. The proposed statistical model of deformation (SMD) uses wavelet-based decompositions coupled with PCA in each wavelet band, in order to more accurately estimate the pdf of high-dimensional deformation fields, when a relatively small number of training samples are available. SMD is further used as statistical prior to regularize the deformation field in an SMD-constrained deformable registration framework. As a result, more robust registration results are obtained relative to using generic smoothness constraints on deformation fields, such as Laplacian-based regularization. In experiments, we first illustrate the performance of SMD in representing the variability of deformation fields and then evaluate the performance of the SMD-constrained registration, via comparing a hierarchical volumetric image registration algorithm, HAMMER, with its SMD-constrained version, referred to as SMD+HAMMER. This SMD-constrained deformable registration framework can potentially incorporate various registration algorithms to improve robustness and stability via statistical shape constraints.

Algorithms↗
PubMed1997

Analysis and presentation. Clinical trials on prostate cancer.

OBJECTIVE: To present guidelines for the analysis and presentation of clinical trials on prostate cancer. METHODS: Textbooks in statistics and oncology were searched for information, as were separate articles on the topic. Previously published advice was fused with own experience. RESULTS: Minimum key points are given for the sections: Introduction, Materials and Methods, Results, Discussion, and Summary. The importance of 1 primary question in any clinical trial is stressed. The value of a detailed presentation of the trial design, the patient population and the inclusion/exclusion criteria, the characterization of the disease, the treatment schedules, and toxicity is underlined. Application of various statistical methods for different endpoints is suggested. Maturity of data, time for publication, and avoidance of publication bias are discussed. Some common pitfalls in the statistical analyses of clinical results are indicated. The impact of prognostic factors, proper staging procedures, and secondary treatments on the interpretation of survival analysis is pointed out. A shift from the (mis-)use of the P value in favor of confidence intervals is strongly encouraged. The use of comparing the survival of responders versus nonresponders is to be abandoned. A few practical hints concerning the presentation are offered. The minimum of data that should be presented in absolute numbers is indicated. Also, the data that should be provided in both graphic and numeric format are exemplified. Examples of essential graphic illustrations are provided. The need for improvements in the design analysis, and presentation of clinical trials is reemphasized. Finally, numerous references are listed. The article is addressed not only to authors and readers of clinical trials, but also to editors of medical journals. CONCLUSION: The suggested guidelines may be useful in the analysis, presentation, and interpretation of clinical trials on prostate cancer. Moreover, compliance with these guidelines may facilitate comparisons with other similar trials and also, the incorporation of single studies into metaanalyses.

Clinical Trials as Topic↗
PubMed1998

Differential exposure misclassification in case-control studies of environmental tobacco smoke and lung cancer.

We review some of the literature on the effects of exposure misclassification on the statistical analysis of case-control studies. In particular, we focus on evidence for exposure misclassification which may be different for cases and controls in studies of environmental tobacco smoke (ETS). For example, such misclassification could induce relative risk estimates below unity for low exposure which appears to be the case in U.S. lung cancer and ETS studies. We describe procedures for systematically examining the sensitivity of dose-response statistics on exposure misclassification. The procedures demonstrate how p-values for the null hypothesis of no dose-response trend could be adjusted to account for exposure misclassification. The adjustment procedures were applied to an example based on a recently published large study of ETS and lung cancer in which a p-value for trend was reported as 0.03. In this example it is seen that modest differential exposure misclassification can induce substantial increases in the actual p-value, changing what appears to be statistically significant to decidedly nonsignificant.

Bias↗