Nikolina Icitovic | PubMed Health

PubMed2005

Modelling clinical trials in heterogeneous samples.

Individual variation in genetic, phenotypic and environmental factors could lead to significant differences in rates of drug metabolism, in clinical responses to drugs, and in drug side effects. The impact of population heterogeneity on treatment effect estimation and on assessment and application of clinical trial findings has been less than fully studied. In this paper, we studied the properties of models that reflect population heterogeneity of clinical trial samples by unmeasured covariates such as genetic susceptibility. The impact of heterogeneity on the estimation of treatment effect in a two-armed placebo or active-controlled clinical trial was quantified using logistic regression models. We also proposed a two-stage clinical trial, where the effects of individual drug-response-related covariates were estimated in a 'training data set' in the first stage of the trial. In the second stage of the trial, a subgroup of individuals with enhanced (or reduced) sensitivity to drug treatments in a heterogeneous risk cohort was identified based on information about their drug-related characteristics. Only these 'responders' were included in the subsequent efficacy test. Our simulation results showed that population heterogeneity could lead to biased estimation of treatment effect not only in the contaminated groups but also in the uncontaminated groups. A two-stage trial could greatly increase the power of an efficacy test over a 'full trial' even with fewer individuals enrolled in the trial; results would apply to a more highly specified population. The bigger the covariate effects, the more efficient the two-stage trial is compared to a 'full trial'.

Biometry↗

PubMed2004

The impact of population heterogeneity on risk estimation in genetic counseling.

BACKGROUND: Genetic counseling has been an important tool for evaluating and communicating disease susceptibility for decades, and it has been applied to predict risks for a wide class of hereditary disorders. Most diseases are complex in nature and are affected by multiple genes and environmental conditions; it is highly likely that DNA tests alone do not define all the genetic factors responsible for a disease, so that persons classified into the same risk group by DNA testing actually could have different disease susceptibilities. Ignorance of population heterogeneity may lead to biased risk estimates, whereas additional information on population heterogeneity may improve the precision of such estimates. METHODS: Although DNA tests are widely used, few studies have investigated the accuracy of the predicted risks. We examined the impact of population heterogeneity on predicted disease risks by simulation of three different heterogeneity scenarios and studied the precision and accuracy of the risks estimated from a logistic regression model that ignored population heterogeneity. Moreover, we also incorporated information about population heterogeneity into our original model and investigated the resulting improvement in the accuracy of risk estimation. RESULTS: We found that heterogeneity in one or more categories could lead to biased estimates not only in the "contaminated" categories but also in other homogeneous categories. Incorporating information about population heterogeneity into the original model greatly improved the accuracy of risk estimation. CONCLUSIONS: Our findings imply that without thorough knowledge about genetic basis of the disease, risks estimated from DNA tests may be misleading. Caution should be taken when evaluating the predicted risks obtained from genetic counseling. On the other hand, the improved accuracy of risk estimates after incorporating population heterogeneity information into the model did point out a promising direction for genetic counseling, since more and more new techniques are being invented and disease etiology is being better understood.

Bias↗