Modified z-test as an alternative to Completely Randomized Design

<p>A less time-consuming method named <i>Modified z-test </i>is suggested here as an alternative to one-way ANOVA (completely randomized design) to test hypothesis manually for three or four samples with unequal and equal sample size respectively. Two illustrations taking sets of Microsoft Excel generated random numbers are solved with <i>Modified z-test</i>. Six assumptions of this suggested method are listed. Computed statistics and critical values according to this method and according to completely randomized design are compared and found to be significantly not different. </p>

Modified z-test as an alternative to Completely Randomized Design

<p>A less time-consuming method named <i>Modified z-test </i>is suggested here as an alternative to one-way ANOVA (completely randomized design) to test hypothesis manually for three or four samples with unequal and equal sample size respectively. Two illustrations taking sets of Microsoft Excel generated random numbers are solved with <i>Modified z-test</i>. Six assumptions of this suggested method are listed. Computed statistics and critical values according to this method and according to completely randomized design are compared and found to be significantly not different. </p>

An Updated Recommendation for Multiple Comparisons

Instructors of introductory and intermediate statistics courses often teach the use of analysis of variance (ANOVA) for the purpose of comparing more than two group means and pairwise comparison procedures (PCPs) to determine which group means differ from one another following a statistically significant ANOVA test. SPSS provides 18 PCPs. The purpose of this study was to determine which PCP has the best power and maintains Type I error control both when assumptions of equal sample size and equal variance are met and when they are violated, so as to provide a single resource for use in introductory and intermediate applied statistics courses. Testing the PCPs with simulated data revealed that only the four tests developed to be used under assumption violations adequately controlled Type I error, so we recommend using one of these procedures. Power results were similar for all four of these tests, but were slightly higher for the Games-Howell test than for the others.

Clinical Trial Size: Fixed versus Group-Sequential Designs

SummaryThis paper deals with the estimation of the size of clinical trials for comparing two binomial proportions in both fixed and group-sequential designs. In the fixed size approach, it focuses on 1.) equal sample size design; 2.) unequal allocation designs that either maximize the test power subject to fixed total cost or minimize the total expenditure subject to prespecified power using the simplex procedure for function minimization. Emphasis is also placed on group-sequential designs, based upon closed stopping rules, multiple testing and range of clinical equivalence. The efficiency of different allocation designs is assessed by computing the power of the exact conditional Fisher-Irwin test. Designs were applied in planning a clinical trial in which the drug Pancuronium Bromide was compared with a standard treatment to reduce intraventricular haemorrhage in preterm infants.