Clinical trials: active control vs placebo — What is ethical?

The quest for effective medicines is very old. In modern times two important tools have been developed to evaluate efficacy of drugs: superiority and non-inferiority types of clinical trials. The former tests the null hypothesis of μ (the difference between a tested drug and comparator) ≤ 0 against μ > 0; the latter tests the null hypothesis of μ ≤ - Δ against, μ > - Δ, where Δ is the clinical difference from the comparator. In a superiority trial, a new drug is tested against a placebo; in a non-inferiority trial, a new drug is tested against active treatment. In this paper, arguments are presented to show that a superiority trial against a placebo is scientifically sound but ethically unacceptable, whereas a non-inferiority trial against active treatment is ethically sound but scientifically not reliable. Switching from a superiority type of trial with placebo to a non-inferiority trial with an active-control — following the latest revision of Declaration of Helsinki — is in practice switching from the violation of the uncertainty principle to uncertainty of results. Given human and financial resources, it appears an academic question as to which is more unethical: to violate patients’ rights or to produce results without scientific value. All presented considerations lead to the conclusion that the use of a superiority trial of design with an active control instead of placebo will satisfy scientific needs, expectation of patients, and the ancient quest for effective medicines. In the era of Good (Clinical, Laboratory, Manufacture) Practice, the attention of those performing clinical trials is focused on the procedure, not always on its essence. However even the excellent performance of a trial which is not worth doing is fruitless. An earlier version of this paper was presented at an international conference, “Placebo: Its Action and Place in Health Research Today,” held in Warsaw, Poland on 12–13 April, 2003.     

Prediction of driving ability after stroke and the effect of behind-the-wheel training

The aim of this study was to examine the predictive value of a neuropsychological test battery relating to an on-the-road driving evaluation and to determine whether patients who failed the driving test could improve their driving through behind-the-wheel training. Thirty-four stroke patients were compared with 20 healthy, matched controls. Patients who failed the driving test were offered driving practice at a driving school and were then reassessed (neuropsychologically and practically). On most of the cognitive tests, patients performed significantly less well than control subjects. Almost 50% of the controls and the patients failed the driving evaluation. None of the neuropsychological tests was able to predict the driving outcome. Of the patients who failed the first driving evaluation, 85% passed the second evaluation after driving practice. There are few controlled studies focusing on the stroke population and the effect of behind-the-wheel training. It is suggested that more controlled studies are needed with more homogenous patient-groups and reliable and quantitative outcome measures.     

Effects of Parent Emotion Socialization on the Neurobiology Underlying Adolescent Emotion Processing: A Multimethod fMRI Study

Research on Child and Adolescent Psychopathology - Parents’ emotion socialization (ES) practices impact socioemotional development throughout adolescence. Little is known, however, regarding...     

Revisiting confidence intervals for repeated measures designs

Loftus and Masson (1994) proposed a method for computing confidence intervals (CIs) in repeated measures (RM) designs and later proposed that RM CIs for factorial designs should be based on number of observations rather than number of participants (Masson & Loftus, 2003). However, determining the correct number of observations for a particular effect can be complicated, given that its value depends on the relation between the effect and the overall design. To address this, we recently defined a general number-of-observations principle, explained why it obtains, and provided step-by-step instructions for constructing CIs for various effect types (Jarmasz & Hollands, 2009). In this note, we provide a brief summary of our approach.