Clinical trials: robust tests are wonderful for imperfect data.

Robust tests are tests that can handle the inclusion into a data file of some outliers without largely changing the overall test results. Despite the risk of non-Gaussian data in clinical trials, robust tests are virtually never performed. The objective of this study was to review important robust tests and to assess whether they provide better sensitivity of testing than standard tests do.

Multistage regression, a novel method for making better predictions from your efficacy data.

Multistage regression is rarely used in therapeutic research, despite the multistage pattern of many medical conditions. Using an example of an efficacy study of a new laxative, path analysis and the 2-stage least square method were compared with standard linear regression. Standard linear regression showed a significant effect of the predictor "noncompliance" on drug efficacy at P=0.

Clinical Trials: Spline Modeling is Wonderful for Nonlinear Effects.

Traditionally, nonlinear relationships like the smooth shapes of airplanes, boats, and motor cars were constructed from scale models using stretched thin wooden strips, otherwise called splines. In the past decades, mechanical spline methods have been replaced with their mathematical counterparts. The objective of the study was to study whether spline modeling can adequately assess the relationships between exposure and outcome variables in a clinical trial and also to study whether it can detect patterns in a trial that are relevant but go unobserved with simpler regression models.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used.

Machine Learning in Therapeutic Research: The Hard Work of Outlier Detection in Large Data.

With large data files, outlier recognition requires a more sophisticated approach than the traditional data plots and regression lines. In addition, the number of outliers tends to rise linearly with the data's sample size. The objective of this study was to examine whether balanced iterative reducing and clustering using hierarchies (BIRCH) clustering is able to detect previously unrecognized outlier data.

Assessing seasonality in clinical research.

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 auto correlation is helpful to minimize chance findings and test to support the presence of seasonality.

Potassium treatment for hypertension in patients with high salt intake: a meta-analysis.

Background: Previous metaanalyses of potassium supplementation in patients with hypertension observed little or no benefit, but failed to account the amount of salt intake.

Objective: To assess the effect on blood pressure of potassium treatment in patients with high salt intake.

Methods: We meta-analyzed studies of patient populations with both high salt and potassium intake.

Clinical research: benefits of Bhattacharya modeling.

Background: Bhattacharya modeling is a Gaussian method recommended by the Food and Agricultural Organization of the United Nations Guidelines for analyzing the eco-system. It is rarely used in clinical research.

Objective: To investigate the performance of Bhattacharya modeling for clinical data analysis.

C-statistics versus logistic regression for assessing the performance of qualitative diagnostic tests.

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.

Clinical research: a novel approach to regression substitution for handling missing data.

In clinical research, missing data are common. Imputed data are not real data but constructed values that should increase the sensitivity of testing. Regression substitution for the purpose of data imputation often did not provide a better sensitivity than did other methods.

Fuzzy modeling, a novel approach to studying pharmacodynamics.

Fuzzy logic can handle questions to which the answers may be "yes" at one time and "no" at the other, or may be partially true and untrue. Pharmacodynamic data deal with questions such as "Does a patient respond to a particular drug dose or not," or "Does a drug cause the same effects at the same time in the same subject or not." Such questions are typically of a fuzzy nature and might, therefore, benefit from an analysis based on fuzzy logic.

Clinical research: a novel approach to the analysis of repeated measures.

In current clinical research, repeated measures in a single subject are common. The problem with repeated measures is that they are closer to one another than unrepeated measures. If this is not taken into account, then data analysis will lose power.

A novel approach to noninferiority testing: documented proof rather than arbitrary margins.

Noninferiority trials have been criticized for their wide margins of noninferiority, making it virtually impossible to reject noninferiority. Recommendations have been given to replace the practice of arbitrarily set margins. The objective of this study was to review various alternative methods of assessment based on statistical reasoning.

Item response modelling for clinical and laboratory testing.

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.

Handling categories properly: a novel objective of clinical research.

A major objective of clinical research is to study outcome effects in subgroups. Such effects generally have stepping functions that are not strictly linear. Analyzing stepping functions in linear models thus raises the risk of underestimating the effects.

Cardiovascular research: data dispersion issues.

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.

Practitioners and nondrug treatment of hypertension, effects of participating in a survey.

A nationwide survey in the Netherlands among 600 randomly sampled practitioners revealed that the advice (1) quit smoking, (2) reduce alcohol, (3) healthy diet, and (4) physical activities was only given by 76%, 26%, 44%, and 61% of the practitioners. To confirm these data, and to study the effects of the personal characteristics of the practitioners, and the effect of their participation in a survey. All general practitioners in the areas of Dordrecht in the Netherlands, with 350,000 inhabitants, were invited to participate.

Time-dependent predictors in clinical research, performance of a novel method.

Individual patients' predictors of survival may change across time, because people may change their lifestyles. Standard statistical methods do not allow adjustments for time-dependent predictors. In the past decade, time-dependent factor analysis has been introduced as a novel approach adequate for the purpose.

Artificial intelligence for diagnostic purposes: principles, procedures and limitations.

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.

Combination Therapy with Olmesartan and Amlodipine in the Treatment of Hypertension.

Background: Combination therapy with antihypertensive agents utilises different mechanisms of action and may be responsible for a more effective decrease in blood pressure.

Objective: To review the recently published trials on efficacy and safety of the combination therapy with olmesartan and amlodipine.

Results: The double-blind American COACH (Combination of Olmesartan Medoxomil and Amlopdine Besylate in Controlling High Blood Pressure) study (2008) showed in 1,940 patients that after eight weeks of treatment the BP goals were most frequently achieved in the 'combination therapy group', with 56.

Meta-analyses of diagnostic studies.

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.

Heart failure due to systolic dysfunction and mortality in diabetes: pooled analysis of 39,505 subjects.

Background: Despite recent successes in improving mortality from congestive heart failure (CHF) with drugs and devices, several reports suggest increased mortality among CHF subjects with diabetes. Our objective was to conduct a meta-analysis to determine aggregate risk of mortality and hospitalization in CHF from systolic dysfunction and diabetes.

Methods And Results: Observational and randomized trials reporting on CHF and mortality in diabetes since 2001 were identified through MEDLINE and Cochrane database searches and hand searching of cross-references.

Determinants of perceived severity of hypertension and drug-compliance in hypertensive patients.

Background: Severity of illness is not an important determinant of drug-compliance. In this paper we hypothesize that the perceived severity of illness rather than the true severity of illness is a determinant of drug-compliance. If this is true, then it will be worthwhile for physicians to look for factors determining this perceived severity of illness.

Validating diagnostic tests, correct and incorrect methods, new developments.

Background: Clinical developments of new treatments are impossible without adequate diagnostic tests. Several working parties including the Consolidated Standard Randomized Trials (CONSORT) movement and the Standard for Reporting Diagnostic Accuracy (STARD) group have launched quality criteria for diagnostic tests. Particularly, accuracy-, reproducibility- and precision-assessments have been recommended, but methods of assessment have not been defined so far.

Clinical trials: how to assess confounding and why so.

Background: In large randomized controlled trials the risk of random imbalance of the covariates is mostly negligible. However, with smaller studies it may be substantial. In the latter situation assessment and adjustment for confounders is a requirement in order to reduce a biased assessment of the treatment comparison.