Response rates estimated

To determine whether the skew was responsible for the taxonic findings, Gleaves et al. transformed the data using a square root or log transformation and were successful at reducing the skew of all but one indicator to less than 1.0. This is a fairly conservative test of the taxonic Conjecture, because data transformation not only reduces indicator skew, but it can also reduce indicator validities, and hence produce a nontaxonic result. Yet, this did not happen in this study. All but one plot originally rated as taxonic were still rated as taxonic after the transformation. MAMBAC base rate estimates were. 19 (SD =. 18) for transformed empirical indicators, and. 24 (SD =. 06) for transformed theoretical indicators. Nevertheless, these estimates are probably not as reliable as the original estimates because of the possible reduction in validity, which is likely to lower the precision of the estimates. 

The second parameter (number of responses per session) provides an estimate of the effects of the test substance on operant performance. If the test substance exerts marked sedative effects, the number of responses would normally be decreased. Response rate could even be increased if the test substance possessed psychostimulant effects. Interpretation of drug effects on operant performance is, however, not simple, because other factors can contribute to effects of the test substance on response rate. In the present procedure, where the effect of the reinforcement schedule (FRIO) is to produce a high rate of baseline responding, test substances with either sedative or psychostimulant effects will generally decrease the rate of responding. 

Previous clinical trials involving similar patients may provide estimates of sample size calculations, means, standard deviations, or, in the case of a binary response variable, response rates for the control group(s), as well as ideas for analysis and display of results. 

Given a postulated functional form of the dose-response relationship, the frequency of occurrence of toxic effects may be used to estimate the unknown parameters. tn addition, this estimated dose-response can be extrapolated to provide either (1) estimates of risk probabilities at lower dose levels, or (2) an estimate of the dose level associated with any particular probability of risk. Implicitly, this approach presumes that the true dose-response can be realized within the postulated functional form used in the estimation and extrapolation procedure. Although this presumption is often not critical for interpolation within the range of observed response rates, it may be extremely critical for extrapolation outside this observable range. 

These data were examined to see if the addition of data at dose levels giving low response rates would lead to a reduction in the variation of the VSD estimates. IVo extrapolation models, the multistage and the log normal, were applied to these data in a series of calculations. In each case, both models fit the observed data very well. First, the VSD s leading to an excess risk of 10 are estimated using the controls and the four higihest dose groups, 60 - 150 ppm, then the VSD s are reestimated by adding the next lower dose, one at a time. These VSD estimates are shown in Table VI. 

Both studies selected a sample of NCEs that originated within the company s U.S. research organizations. NCEs were selected from a database maintained by CSDD of new products under development. Probability samples were drawn from the universe of NCEs in the CSDD database, but some nonresponding companies could have biased the sample. Furthermore, neither study reported the within-fro response rate. If firms failed to provide data on some NCEs for which data were poor, or if they selectively reported on NCEs for some other reason, the sample of NCEs could be biased. Again, the effect of such potential biases on cost estimates cannot be judged. 

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