Search PubMed⌕ Search

Biomedical subjects

E M Laska

Publications and source records attributed to E M Laska.

38 records · Page 3Linked to original sources

An analytic approach to quantifying pain scores.

Statistical problems in clinical trials frequently involve fitting regression lines when the underlying data are categorical or ordinal response variables. Usually an ad hoc a priori quantification is used to assign values to these ordinal responses. For pain intensity data collected in analgesic trials, the usual approach is to set none equal to 0, mild equal to 1, moderate equal to 2, and severe equal to 3. While this scheme has been generally accepted, on the basis that for similar clinical trials reasonably similar results are obtained by different investigators, concern exists that the distances between pain scores are probably not equal. A method is presented for quantifying categorical responses so that the resulting scores maximize the simultaneous fit of the dose-response regression lines. The optimal scores derived by this technique may then be used in a bioassay analysis to estimate the relative potency of 2 compounds. As illustrative examples, this method was applied to data from 2 clinical trials and the results were compared to the usual method.

Analysis of Variance↗

Power and sample size in cost-effectiveness analysis.

For resource allocation under a constrained budget, optimal decision rules for mutually exclusive programs require that the treatment with the highest incremental cost-effectiveness ratio (ICER) below a willingness-to-pay (WTP) criterion be funded. This is equivalent to determining the treatment with the smallest net health cost. The designer of a cost-effectiveness study needs to select a sample size so that the power to reject the null hypothesis, the equality of the net health costs of two treatments, is high. A recently published formula derived under normal distribution theory overstates sample-size requirements. Using net health costs, the authors present simple methods for power analysis based on conventional normal and on nonparametric statistical theory.

Bias↗