Experimental confounding

A relatively simple example of a confounded reactor is a nonisothermal batch reactor where the assumption of perfect mixing is reasonable but the temperature varies with time or axial position. The experimental data are fit to a model using Equation (7.8), but the model now requires a heat balance to be solved simultaneously with the component balances. For a batch reactor. 

Another category of potential confounding variables includes dosing issues. Caffeine researchers have administered a wide variety of experimental dosages, an issue related obviously to physiological effects but also of interest to those concerned with caffeine doping. The timing of the... 

Inhalation studies at the U.S. Air Force Aerospace Medical Research Laboratory showed an increased tumor response (hemangiosarcomas and Kupffer cell sarcomas) in mice exposed at 5 ppm, 6 h/d, 5 d/w for 6 mon (MacEwen and Vernot 1977, and Flaun 1977, reviewed in Trochimowicz 1994). Rats similarly exposed at 5 ppm exhibited increased incidences of squamous cell carcinomas of the lung and hepatocellular carcinomas. Hamsters subjected to a similar experimental protocol failed to show an increased incidence of tumors (MacEwen and Vernot 1975). It must be noted that the 1,1-dimethylhydrazine used in these studies contained 0.12% dimethylnitrosamine, which could be a significant confounder. 

Behavioral Evaluation. Behavioral assessment of nonrodents is generally more difficult than evaluation of rodents because of their larger size, difficulties associated with handling and manipulation, and their greater awareness of and reactivity to the experimenter. Such factors can confound detection and/or interpretation of more subtle test compound-related behavioral changes. 

Many factors may confound the assessment of the D DI potential of early discovery compounds [93], Limited or no solubility data exist to understand the likelihood that the compound will precipitate out of an in vitro incubation. The compounds have generally not been analyzed from a spectroscopic perspective their characteristics may interfere with a fluorogenic DDI assay. Metabolism data are typically not available. The binding of a compound to plasma proteins or microsomal incubation constituents is not well understood, which may lead to underprediction of its inhibitory potential. The compounds are typically delivered in DMSO, which may cause solvent-related inhibition of the enzymatic assay. Also, since little is known about in vivo concentrations or projected dose, framing the consequences of an early DDI in vitro experiment may be difficult. With these factors in mind, general experimental paradigms have been developed to help minimize their potential impact. 

Despite neuropharmacolgical and animal data to support sedative and anxiolytic effects of passionflower, there have not been any such controlled studies in humans. Two studies have been published that examined the effects of combined herbal extracts on anxiety, including passionflower (Bourin et al. 1997). Although there were significant and experimentally controlled effects, a combined herbal treatment confounds the ability to selectively identify the effects of passionflower. A second controlled study was similarly confounded by the use of a three-herb combination (Gerhard et al. 1991). 

Most of the data regarding inhalation exposure to chloroform in humans were obtained from clinical reports describing health effects in patients under anesthesia. In some instances, the results may have been confounded by the concurrent administration of other drugs with chloroform or by artificial respiration of patients under chloroform anesthesia. Furthermore, most of the studies did not provide any information regarding actual exposure levels for observed effects. Nonetheless, chloroform-induced effects in humans are supported by those observed in animals under experimental conditions. The human studies cited in the profile provide qualitative information on chloroform toxicity in humans. 

Unfortunately, two experiments at two different levels of a single factor cannot provide an estimate of the purely experimental uncertainty. The difference in the two observed responses might be due to experimental uncertainty, or it might be caused by a sloping response surface, or it might be caused by both. For this particular experimental design, the effects are confused (or confounded) and there is no way to separate the relative importance of these two sources of variation. 

Because the sum of all the fractions must equal unity (or 100%), only - 1 of the components can be specified independently the remaining component is a dependent or slack variable [Snee (1973)]. Such a system is said to have n - I degrees of freedom. It is impossible to increase the fraction of one component in the mixture without decreasing the fraction of at least one other component. Placing one or more equality constraints on a system often leads to a confounded (or confused) interpretation of experimental data. Care must be exercised when assigning component effects to a particular response. In mixtures, for example, an observed change in response could be attributed to the increase in one component or to the concomitant decrease in one or more of the other components. 

In this chapter we discuss the multifactor concepts of confounding and randomization. The ideas underlying these concepts are then used to develop experimental designs for discrete or qualitative variables. 

---