Response Rates In Clinical Trials Research Articles

The therapeutic effect of clinical trials: understanding placebo response rates in clinical trials – A secondary analysis

Background and purposePlacebo response rates in clinical trials vary considerably and are observed frequently. For new drugs it can be difficult to prove effectiveness superior to placebo. It is unclear what contributes to improvement in the placebo groups. We wanted to clarify, what elements of clinical trials determine placebo variability.MethodsWe analysed a representative sample of 141 published long-term trials (randomized, double-blind, placebo-controlled; duration > 12 weeks) to find out what study characteristics predict placebo response rates in various diseases. Correlational and regression analyses with study characteristics and placebo response rates were carried out.ResultsWe found a high and significant correlation between placebo and treatment response rate across diseases (r = .78; p < .001). A multiple regression model explained 79% of the variance in placebo variability (F = 59.7; p < 0.0001). Significant predictors are, among others, the duration of the study (beta = .31), the quality of the study (beta = .18), the fact whether a study is a prevention trial (beta = .44), whether dropouts have been documented (beta = -.20), or whether additional treatments have been documented (beta = -.17). Healing rates with placebo are lower in the following diagnoses; neoplasms (beta = -.21), nervous diseases (beta = -.10), substance abuse (beta = -.14). Without prevention trials the amount of variance explained is 42%.ConclusionMedication response rates and placebo response rates in clinical trials are highly correlated. Trial characteristics can explain some portion of the variance in placebo healing rates in RCTs. Placebo response in trials is only partially due to methodological artefacts and only partially dependent on the diagnoses treated.

Cholinergic nicotinic systems in Alzheimer's disease: prospects for pharmacological intervention.

Within the last few years, research into the cause and progression of Alzheimer's disease has made significant advances. Although there is still no preventative treatment or cure for this neurodegenerative illness, the development of drugs that may alleviate some of the cognitive symptoms associated with it is advancing. Cholinesterase inhibitors are at present the most effective form of treatment and have shown significant overall response rates in clinical trials. However, although some patients show substantial improvement when treated with this class of drugs, there is considerable variability in the amount of benefit gained in different individuals in terms of their cognitive and behavioural functioning. Furthermore, unfortunately some patients gain little or no benefit from these drugs. It would therefore be of great advantage to explore alternative therapeutic possibilities. This article reviews the potential involvement of the nicotinic cholinergic system in Alzheimer's disease and discusses the possibility of nicotinic pharmacotherapy. Substantial evidence indicates the involvement of the nicotinic cholinergic system in the pathology of Alzheimer's disease. Drugs targeting these sites may not only have a positive effect on cognitive function, but also have additional therapeutic benefits in terms of restoring the hypoactivity in the excitatory amino acid pyramidal system and even slowing the emergence of Alzheimer's disease pathology. The conclusion of this review is that nicotinic treatments are an important potential source of new therapeutic interventions in Alzheimer's disease.

The Joint Analysis of Survival Times and Response Rates in Clinical Trials on the Treatment of Cancer

A method is outlined which allows to analyse survival and response data from a clinical trial on the treatment of cancer when the question of interest is whether the somehow defined response to the treatment has an influence on patients’ survival times. This can be done by making use of Cox’s proportional hazards model for survival data together with time-dependent covariates. This allows an appropriate classification of non-respondors and responders by considering the time of diagnosis of response during follow-up. The resulting test can be viewed as a time-dependent logrank test. An example from a clinical trial on gastrointestinal cancer is considered.