Confounding factors minimizing

In the second part, they selected 18 growth studies that included minimal criteria, such as selected control group, measured height by stadiometry, and at least a 12-month duration they compared the design attributes of these studies with the described recommendations (137). Of the 18 selected studies, 17 were susceptible to one or more important confounding factors nevertheless, the outcomes of all 18 studies were considered to be... 

There were brief periods of high carbon disulfide exposures due to infrequent periods of breakdown in production. An appropriate exclusion of confounding factors (diabetes, alcohol consumption, blood lead levels) was used. The decrease in MCV constitutes a LOAEL of 7.6 ppm (24 mg/cu.m) which was considered minimal because the MCVs, although reduced compared to controls, were still within a range of clinically normal values. The study authors and the MRL workgroup considered these effects indicative of minimal neurotoxicity. 

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. 

We are not certain which comorbid risk factors cause mortality independent of sleep effects, and therefore, we cannot be certain whether we controlled too much or too little for comorbidities. For example, since short sleep or long sleep may cause a person to be sick at present or to get little exercise or to have heart disease (17), diabetes (18), etc., controlling for these possible mediating variables may have incorrectly minimized the hazards associated with sleep durations. This would be overcontrol. The hazard ratios for participants who were rather healthy at the time of the initial questionnaires were unlikely to be overcontrolled for initial illness. Since the 32-covariate models and the hazard ratios for initially healthy participants were similar, this similarity reduced concern that the 32-covariate models were overcontrolled. On the other hand, there may have been residual confounding processes that caused both short or long sleep and early death that we could not adequately control in the CPSII data set, either because available control variables did not adequately measure the confound or because the disease did not yet manifest itself. Depression, sleep apnea, and dysregulation of cytokines are plausible confounders that were not adequately controlled. It may be impossible to be confident that all conceivable confounds are adequately controlled in epidemiological studies of sleep. 

Random error can over- or underestimate risk and is generally not as severe as bias. Moreover, the magnitude of error can be estimated with statistical techniques. Assessment of confounding, synergism, or effect modification can be accomplished in the analytical phase (by stratification or multivariate modeling), providing sufficient data have been collected on those factors. Restriction or randomization procedures also can be used in the design phase to minimize confounders. 

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