Measuring drug response in cancer cell lines is essential for studying mechanisms of drug action and identifying genetic variants associated with sensitivity and resistance (preclinical pharmacogenomics). The conventional approach involves computing relative viability based on the ratio of cells in control and drug-treated cultures at the end of a fixed time period. We have recently shown that relative viability and the response metrics derived from it, such as IC50 (drug concentration resulting in 50% relative viability) and area under the dose–response curve (AUC), are confounded by variation in cell proliferation rates and assay duration, potentially explaining discrepancies among large-scale drug response data sets. Here we show that use of relative viability as a measure of drug response results in false-positive and false-negative pharmacogenomic associations. An alternative approach, based on computing normalized growth rate (GR) inhibition minimizes these artifactual associations by scoring drug sensitivity on a per-division basis. GR50, a measure of potency, is the concentration of drug that reduces cell proliferation rate by one-half; GRmax, a measure of efficacy, is the maximum effect of a drug at the highest tested concentration; and GRAOC combines these in an integrated 'area over the curve' value. The signs of GR values and GRmax relate directly to response phenotype: positive for partial growth inhibition, zero for complete cytostasis and negative for cell death. By contrast, Emax (relative viability at the highest tested concentration) values do not have this property as they are strongly confounded by proliferation rates. Collecting GR values requires only modest changes in experimental approach and calculations can be performed online (http://www.GRcalculator.org/).