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Background: Seasonal patterns are assumed in many fields of medicine. However, biological processes are full of variations and the possibility of chance findings can often not be ruled out. Methods: Using simulated data we assess whether autocorrelation is helpful to minimize chance findings and test to support the presence of seasonality. Results: Autocorrelation required to cut time curves into pieces. These pieces were compared with one another using linear regression analysis. Four examples with imperfect data are given. In spite of substantial differences in the data between the first and second year of observation, and in spite of otherwise inconsistent patterns, significant positive autocorrelations were constantly demonstrated with correlation coefficients around 0.40 (SE 0.14). Conclusions: Our data suggest that autocorrelation is helpful to support the presence of seasonality of disease, and that it does so even with imperfect data. [ABSTRACT FROM AUTHOR]

Eur J Clin Invest 2010; 40 (10): 911–917 Background Item response models using exponential modelling are more sensitive than classical linear methods for making predictions from psychological questionnaires. Objective To assess whether they can also be used for making predictions from quality of life questionnaires and clinical and laboratory diagnostic-tests. Methods Of 1000 anginal patients assessed for quality of life and 1350 patients assessed for peripheral vascular disease with diagnostic laboratory tests, items response modelling was applied using the Latent Trait Analysis program -2 of Uebersax. Results The 32 different response patterns obtained from test batteries of five items produced 32 different quality of life scores ranging from 3·4% to 74·5% and 32 different levels of peripheral vascular disease ranging from 9·9% to 83·5% with overall mean scores, by definition, of 50%, whereas the classical method of analysis produced the discrete scores of only 0–5. The item response models produced an adequate fit for the data as demonstrated by chi-square goodness of fit values/degrees of freedom of 0·86 and 0·64. Conclusions 1 Quality of life assessments and diagnostic tests can be analysed through item response modelling, and provide more sensitivity than do classical linear models. 2 Item response modelling can change largely qualitative data into fairly accurate quantitative data, and can, even with limited sets of items, produce fairly accurate frequency distribution patterns of quality of life, severity of disease and other latent traits. [ABSTRACT FROM AUTHOR]

Background: Back propagation (BP) artificial neural networks are a distribution-free method for data analysis based on layers of artificial neurons that transduce imputed information. It has been recognized as having a number of advantages compared to traditional methods including the possibility to process imperfect data, and complex non-linear data. The objective of this study was to review the principles, procedures, and limitations of BP artificial neural networks for a non-mathematical readership. Methods: A real data sample of weight, height and measured body surface area from 90 individuals was used as an example. SPSS 17.0 with neural network add-on was used for the analysis. The predicted body surface from a two hidden layer BP neural network was compared to the body surface calculated by the Haycock equation. Results: Both the predicted values from the neural network and from the Haycock equation were close to the measured values. A linear regression analysis with neural network as predictor produced an r2-value of 0.983, while the Haycock equation produced an r2-value of 0.995 (r2>0.95 is a criterion for accurate diagnostic testing). Conclusions: BP neural networks may, sometimes, predict clinical diagnoses with accuracies similar to those of other methods. However, traditional statistical procedures, such as regression analyses need to be added for testing their accuracies against alternative methods. Nonetheless, BP neural networks have great potential through their ability to learn by example instead of learning by theory. Clin Chem Lab Med 2010;48:159–65. [ABSTRACT FROM AUTHOR]

Biological processes are full of variations and so are responses to therapy as measured in clinical research. Estimators of clinical efficacy are, therefore, usually reported with a measure of uncertainty, otherwise called dispersion. This study aimed to review both the flaws of data reports without measure of dispersion and those with over-dispersion. Examples of estimators commonly reported without a measure of dispersion include: 1) number needed to treat; 2) reproducibility of quantitative diagnostic tests; 3) sensitivity/specificity; 4) Markov predictors; 5) risk profiles predicted from multiple logistic models. Data with large differences between response magnitudes can be assessed for over-dispersion by goodness of fit tests. The χ² goodness of fit test allows adjustment for over-dispersion. For most clinical estimators, the calculation of standard errors or confidence intervals is possible. Sometimes, the choice is deliberately made not to use the data fully, but to skip the standard errors and to use the summary measures only. The problem with this approach is that it may suggest inflated results. We recommend that analytical methods in clinical research should always attempt to include a measure of dispersion in the data. When large differences exist in the data, the presence of over-dispersion should be assessed and appropriate adjustments made. [ABSTRACT FROM AUTHOR]

Background: Diagnostic reviews often include the sensitivity/specificity results of individual studies. A problem occurs when these data are pooled because the correlation between sensitivity and specificity is generally strongly negative, causing overestimation of the pooled results. The diagnostic odds ratio (DOR), defined as the odds of true positives vs. that of false positives, may avoid this problem. The aim of the study was to review the advantages and limitations of the DORs. Methods: A systematic review of 44 previously published diagnostic studies was used as an example. Results: DORs can be readily implemented in diagnostic research. Advantages include: (1) they adjust for the negative and curvilinear correlations between sensitivities and specificities, (2) they take account of the heterogeneity between studies with respect to the different thresholds chosen by the investigators in the original studies, and (3) it is easy to extend the model with covariates representing between-study differences in design. Limitations include: 1) the outcome parameter is a summary estimate of both sensitivity and specificity, and 2) the magnitude of the studies included is not taken into account. Conclusions: Reported sensitivities and specificities of different studies assessing similar diagnostic tests are not only negatively correlated, but also negatively correlated in a curvilinear manner. It is appropriate to take this negative curvilinear correlation into account in the data pooling of such meta-analyses. The DORs can be applied for that purpose. Clin Chem Lab Med 2009;47:1351–4. [ABSTRACT FROM AUTHOR]

Exponential models are very popular for the assessment of clinical research. Nature is unpredictable and exponential models may be adequate for simple organisms but less so for human beings. Theobjective was to review the potential advantages and limitations of this approach. The following models were reviewed: Cox proportional hazard regression, Markow modeling, polynomial regression for Laplace transformed multi-exponential models, and logistic regression. Real and hypothesized data were used as examples. In conclusion, the advantages include: the possibility to conveniently study subgroup effects, otherwise called confounders; the possibility to derive pharmacokinetic parameters; andthe possibility to make long-term predictions from observational data. Limitations include: the exponential models may not fit the data well enough; they may give rise to some serious misinterpretations of the data; most of the statistical software for exponential models assumes that confounders are homogeneous, while, in reality, this is virtually never true; and the exponential models are a major simplification for the description of research data in human beings. We underline that exponential modeling, although a very interesting tool for exploratory research, is not adequately reliable for confirmational research. It is to be hoped that this paper will help investigators to more cautiously interpret the results of their exponential data analysis. [ABSTRACT FROM AUTHOR]

Current clinical trials do not use random samples, but, instead, convenience samples. This raises the risk of non-normal data. The aim of this paper is to review and describe for a non-mathematical readership common methods for testing the normal property, as well as methods for analyzing the data in case of non-normality. With slight departures from the normal distribution, normality tests can be used even so. They include, among other tests, the normal-, t-, chi-square- tests, analysis of variance, and regression analyses. They should not be used if the chi-square goodness of fit is significant. Rank-testing is, then, an alternative, but, sometimes, distributions do not allow for this approach either. This can be checked by the Kolmogorov-Smirnov test. If the latter test is also positive, rank-testing is not warranted, and confidence intervals can be derived from the data without prior assumption about the type of frequency distribution. This can be done by calculating the range within which 95% of all possible outcomes lie. Another popular method for this purpose is bootstrapping, which resamples at random from the study's own data. This paper reviews methods to assess data for compliance with normality, and summarizes solutions for the analysis of non-normal data. We strongly believe that normality statistics, although the mainstay of statistical analysis for centuries, will rapidly be replaced with non-normal testing as the awareness of non-normal sampling distributions grows, and we hope that the paper will strengthen this awareness and affect the design and analysis of future clinical trials. [ABSTRACT FROM AUTHOR]

Background: In randomized controlled trials, prior to statistical analysis, the data are checked for outliers and erroneous data. Statistical tests are, traditionally, not very good at distinguishing between errors and outliers, but they should be able to point out main endpoint results closer to expectation than compatible with random sampling. Objectives: To explain from hypothesized and published examples why extreme p-values like p>0.95 and p<0.0001 may indicate that sampling was not completely random. Results: Extreme p-values can be readily observed in recent issues of high-impact journals. A p-value >0.95 literally means that we have a >95% chance of finding a result less close to expectation and, consequently, a <5% chance of finding a result this close or closer. Often in studies a statistical power of 80% is agreed upon, corresponding with a p-value of approximately 0.01. The ultimate p-value may then be a bit larger or smaller. However, a p-value much smaller than 0.01 will be rarely observed, because it would indicate that the study is overpowered. If the p-values can be assumed to follow a normal distribution around 0.01, then we will have a less than 5% chance of observing a p-value of <0.0001. Conclusions: In randomized controlled trials, main endpoint p-values larger than p=0.95 will be rare, because they would indicate similarities closer than compatible with a normal distribution of random data samples. Also very low p-values like p<0.0001 will be rarely encountered, because it would mean that the trial was overpowered and should have had a smaller sample size. It would seem appropriate, therefore, to require investigators to explain such results and to consider rejecting the research involved. So far, in randomized controlled trials the null-hypothesis is generally rejected at p<0.05. Perhaps we should consider rejecting the entire study if the main endpoint p-values are >0.95 or <0.0001. [ABSTRACT FROM AUTHOR]

Background. In pharmacology the clinical e3cacy of a compound is generally strongly dependent on baseline characteristics of the test subjects. In recent HMG -CoA reductase inhibitor (statin) studies this was not so. However, these studies used subgroup analysis, and subgroup analysis may not be sensitive to demonstrate a linear relationship between baseline LDL-cholesterol levels and responsiveness to statin treatment. Methods. We used regression analysis of previously published statin data to assess relationship between baseline LDL-cholesterol and responsiveness to statin treatment. Also we looked into the data of recent statin studies for mechanisms that tend to make comparisons more similar. Results. Regression unlike subgroup analysis is able to demonstrate a highly significant (p < 0.0001) positive correlation between baseline LDL-cholesterol and responsiveness to statin treatment. In recent randomized statin studies reporting independence between responsiveness and baseline levels, the following mechanisms tended to make comparisons more similar: (1) the use of unadjusted results, (2) no test for the left tail of the chi-square distribution, and (3) the use of unrandomized endpoint variables. Conclusions. Although patients with low LDL-cholesterol levels may tend to benefit from statins, the amount of benefit must be considerably less than that of patients with high baseline values. Better benefit-risk analyses are required for the patients with low LDL-cholesterol levels before consensuses should dictate their routine use. [ABSTRACT FROM AUTHOR]

Autonomic control of blood pressure appears to decline with age giving rise to an increased risk of orthostatic hypotension and major hypotensive reactions to antihypertensive drugs. In the past few years, many workers have assessed autonomic function in the elderly and sometimes found controversial results. Baroreflex sensitivity, as measured by the steepness of the heart rate/mean pressure curve, decreases with age. However, this phenomenon does not correlate well with orthostatic impairment. Sympathetic dysfunction might be more responsible for syncopal symptoms in the elderly, a finding supported by the fact that elderly with orthostatic symptoms never collapse within a few seconds, but do so after 1 or more minutes of standing. However, the results of sympathetic function testing in the elderly indicate that sympathetic function in most elderly is not impaired and that sympathetic activity, as measured by circulating levels of catecholamines, is usually increased rather than decreased. In various populations with increased sympathetic activity, but not in the elderly, β-adrenoceptor antagonists (β-blockers) have been demonstrated to cause pressor effects, presumably due to α-adrenoceptor-mediated vasoconstriction unopposed by β-receptor-mediated vasodilation. In the past year, large studies have been completed indicating that the same is true for the elderly, and that the depressor effect on pulse pressure upon standing in this category of patients can be offset and turned into a pressor effect by long-term β-blocker treatment. This phenomenon could not be demonstrated with non-β-blocker antihypertensive drugs, including ACE inhibitors, calcium channel antagonists, diuretics and angiotensin II receptor antagonists. In elderly patients β-blockers may, therefore, be the most appropriate antihypertensive agents as they protect the elderly from orthostatic impairment. [ABSTRACT FROM AUTHOR]

Presents a meta-analysis of double-blind, placebo-controlled trials of the efficacy and safety of second-generation dihydropyridine calcium channel blockers In heart failure. Mortality and morbidity of patients with chronic heart failure undergoing vasodilator therapy; Measurement of cardiac index; Improvements in left ventricular ejection fraction; Changes in exercise treadmill duration.

This meta-analysis suggests that homocysteine may not be as harmful for the heart as it seems. At the same time, however, homocysteine may be an indicator for unhealthy lifestyles, and therefore, an important variable for cardiologists to take into account when assessing coronary artery disease. [ABSTRACT FROM AUTHOR]

Focuses on the clinical efficacy and safety of isosorbide mononitrate immediate-release sustained-release (IR-SR) formulation to see whether theoretical advantages translate into better clinical effectiveness. Formulations advantage over conventional isosorbide mononitrate or dinitrate; Rapid onset action of IR-SR; Antianginal effects.

Examines the effectiveness of long-acting dihydropyridine calcium channel blocker lercanidipine in treating diabetes with hypertension. Replacement of beta-blockers with calcium channel blockers; Mechanism of action of calcium channel blockers; Occurrence of rebound hypertension following replacement therapies.

Khat is a centuries old substance of abuse produced on a large scale in Yemen. Beta-ketoamphetamine, otherwise called cathinone, is the main contributor to the stimulant effect of khat. A recent unpublished study from Yemen found that the percentage of native users may be as large as 70%. The objective of the current study was to evaluate this figure, to study users' personal characteristics in order to find out whether there are subgroups, particularly, at risk of abuse, and to assess the immediate adverse effects of the compound as well as the main motivations for abuse. The entire population of a randomly selected district of Yemen's capital city Sana'a was used. Self-administered questionnaires were applied. Of 600 questionnaires distributed only 200 were completed and usable. One hundred fifty nine persons (80%) were users. Eighty-six percent of the males and 50% of the females were users ( p < 0.001). The majority of the users were between 15 and 30 years old. The numbers of females and males in different age classes were similarly distributed. Daily adverse effects included tiredness, gastrointestinal and cardiac complaints, and headache occurring in between 38 and 85% of the users (95% confidence interval). The main reasons for chewing khat was for accomplishing study and hard work in the males and attending social events in the females (difference between genders at p < 0.05). Our study shows that about 80% of the Yemeni people use khat on a daily basis. The proportion of male users is larger than that of females. This may be a consequence of the dominant role of the males in the Yemeni society. Both time and money are lost among users, which must affect their lifes profoundly. This is particularly serious because the majority of users are between 13 and 30 years and thus in an active age of life. Khat has a wide range of unpleasant daily side effects and enhances the risks of infectious diseases. Studying serious adverse effects was not the objective of our survey but many case reports are currently being published. [ABSTRACT FROM AUTHOR]

One of the flaws of modern statistics is that it can produce statistically significant results even if treatment effects are very small. The objective of this work was to provide examples of studies that have been published as unequivocally positive, although the treatment effects were substantially smaller than expected; and 2 to introduce superiority-testing as a novel statistical approach avoiding the risk of statistically significant but clinically irrelevant results. We from recent volumes of the Lancet six original articles of controlled clinical trials that were reported as being positive studies, although they did not meet their expected power. The studies produced only 53 to 83% of the statistical power expected, while the new treatments produced only 46 to 86% of the magnitude of response expected. Instead of a p-value of 0.05 as cut-off criterion for demonstrating superiority a stricter criterion seems to be needed. For that purpose, similar to equivalence-testing and non-inferiority-testing, prior boundaries of superiority have to be defined in the protocol. If the 95% interval of the study turns out to be entirely within these boundaries, then superiority is accepted. Nowadays, too many borderline significant studies are being reported as convincingly positive studies. This is misleading, as it produces overestimated expectations from new treatments. Superiority-testing, as introduced in this paper, is a simple method to avoid this problem. [ABSTRACT FROM AUTHOR]

So far, few physicians have followed the scientific method for answering practical questions they simply do not know the answer of. The scientific method is, in a nutshell, reformulate your question into a hypothesis and try to test this hypothesis against control observations. The goal of this article is to give simple examples of how the scientific method can be implied in a physician's daily life. Of nine unanswered daily questions, four qualified for simple statistical assessments that were very clarifying for the physicians involved. Additional advantages of the scientific method include the following: (1) because the scientific method has been recognized to produce the best evidence you can get from your observations, physicians applying it serve their population in the best possible way; (2) being actively involved in the scientific method is a strong antidote against the hazards of accepting published studies from others with too little scepticism or rejecting them with too much of it. Limitations of the scientific method include (1) type I and II errors; (2) misinterpretations due to different frequency distributions; (3) lack of leisure time on the part of the physicians to write a study protocol; and (4) the risk of a damaged patient-doctor relationship. [ABSTRACT FROM AUTHOR]

Determines the factors that contribute to the effect of isosorbide mononitrate for patients with angina pectoris. Benefit of slow release formulation on antianginal effect and quality of life; Influence of the New York Heart Association classification, obesity, smoking, hypercholesterolemia and diabetes mellitus on the beneficial effect of nitrates.

Background: Qualitative diagnostic tests commonly produce false positive and false negative results. Smooth receiver operated characteristic (ROC) curves are used for assessing the performance of a new test against a standard test. This method, called c-statistic (concordance) has limitations. The aim of this study was to assess whether logistic regression with the odds of disease as an outcome and the test scores as covariate, can be used as an alternative approach, and to compare the performance of either of the two methods. Methods: Using as examples simulated by vascular laboratory scores we assessed the performance of logistic regression as compared to c-statistics. Results: The c-statistics produced areas under the curve (AUCs) of respectively 0.954 and 0.969 (standard errors 0.007 and 0.005), means difference 0.015 with a pooled standard error of 0.0086. This meant that the new test was not significantly different from the standard test at p=0.08. Logistic regression of these data with presence of disease as a dependent and vascular laboratory scores as an independent variable produced regression coefficients of 0.45 and 0.58 with standard errors of respectively 0.04 and 0.05. This meant that the new test was a significantly better predictor of disease than the standard test at p=0.04. Conclusions: Logistic regression with presence of disease as a dependent and test scores as an independent variable was better than c-statistics for assessing qualitative diagnostic tests. This may be relevant to future diagnostic research. [ABSTRACT FROM AUTHOR]