Frequency analyses can be improved by a modified t-test in sample-based preclinical efficacy studies

摘要

Sample-based preclinical drug efficacy studies compare frequency (proportion) or incidences of successes within respective samples of test and control groups. The word success in principle refers to a protected (e.g., due to vaccination), recovered, or surviving animal, depending on the particular experiment. We introduce here a modified t-test for two independent groups, aimed at statistical analysis of the difference between frequencies of successes in sample based preclinical studies. The test is applicable whenever the study is based on repeating replicate experiments, as required by certain procedures such as validation. Such experiments are based on constant drug dose and performed under identical conditions and protocol. The proposed test combines the computational rules of t-test for two independent groups and analysis of variance. In the initial steps, incidences are transformed to proportions, and variance between proportions in samples of the jth group (S?pj 2 ), is then transformed into theoretical weighted variance within the ith repetition (sample) of the j th group (S?i, j 2 ). The variance of proportions in samples of the size of the whole group (SEj 2) is then calculated. The t-statistic is computed according to the rules of t-test for two independent groups. Significance is calculated using (N1 - 1) + (N2 - 1) degrees of freedom, where N j denotes the total number of animals in the jth group. The proposed model offers an important advantage over incidence or proportion distribution models, such as chi-square or normal approximation of binomial distribution, respectively, because it considers variance between replicate experiments. It moreover offers important flexibility by limiting the requirement for identical sample sizes only to samples within the control or test group. A difference between groups in sample sizes, number of samples, or both, preventing application of block designs or the standard formats of t-test, may still exist. Theoretical considerations and working examples are provided.