Download Analysis of Clinical Trials Using SAS: A Practical Guide by Alex Dmitrienko PDF

By Alex Dmitrienko

In research of scientific Trials utilizing SAS: a pragmatic consultant, Alex Dmitrienko, Geert Molenberghs, Christy Chuang-Stein, and Walter Offen bridge the space among glossy statistical technique and real-world scientific trial functions. step by step directions illustrated with examples from genuine trials and case reports serve to outline a statistical approach and its relevance in a medical trials atmosphere and to demonstrate how one can enforce the tactic quickly and successfully utilizing the ability of SAS software program. themes mirror the foreign convention on Harmonization (ICH) directions for the pharmaceutical and deal with vital statistical difficulties encountered in medical trials, together with research of stratified info, incomplete facts, a number of inferences, matters coming up in safeguard and efficacy tracking, and reference durations for severe defense and diagnostic measurements. medical statisticians, learn scientists, and graduate scholars in biostatistics will drastically enjoy the many years of scientific examine event compiled during this booklet. quite a few ready-to-use SAS macros and instance code are incorporated.

This e-book is a part of the SAS Press software.

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Chapter 1 Analysis of Stratified Data 21 Since the results and their interpretation may be affected by the measure of association used in the analysis, it is important to clearly specify whether the inferences are based on risk differences, relative risks or odds ratios. EXAMPLE: Severe Sepsis Trial Statistical methods for the analysis of stratified clinical trials with a binary endpoint will be illustrated using the following data. A placebo-controlled clinical trial was conducted on 1690 patients to examine the effect of an experimental drug on 28-day all-cause mortality in patients with severe sepsis.

Patients in Strata 3 and 4). 1 Asymptotic Randomization-Based Tests Fleiss (1981, Chapter 10) described a general method for performing stratified analyses that goes back to Cochran (1954a) and applied it to the case of binary outcomes. Let a j denote the estimate of a certain measure of association between the treatment and binary outcome in the jth stratum, and let s 2j be the sample variance of this estimate. Assume that the measure of association is chosen in such a way that it equals 0 when the treatment difference is 0.

When choosing an appropriate inferential method for stratified categorical data, it is important to remember that most of the popular procedures (both randomization- and model-based) need to be used with caution in sparse stratifications. The presence of a large number of under-represented strata either causes these procedures to break down or has a deleterious effect on their statistical power. A commonly used rule of thumb states that one generally needs at least five observations per treatment group per stratum to avoid spurious results.

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