Practical Statistics for Medical ResearchPractical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. Using real data and including dozens of interesting data sets, this bestselling text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research. |
Contents
| 1 | |
| 10 | |
| 19 | |
Theoretical distributions | 48 |
Designing research | 74 |
Using a computer | 107 |
Preparing to analyse data | 122 |
Principles of statistical analysis | 152 |
Relation between several variables | 325 |
Analysis of survival times | 365 |
Some common problems in medical research | 396 |
Clinical trials | 440 |
The medical literature | 477 |
Appendix A Mathematical notation | 505 |
Appendix B Statistical tables | 514 |
Answers to exercises | 546 |
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Common terms and phrases
allocation Altman analysis of variance approach assess association assumption bilirubin Binomial distribution blood glucose blood pressure calculate cancer case-control study centiles Chapter Chi squared test clinical trials comparison confidence interval correlation coefficient data in Table degrees of freedom described in section discussed disease effect estimate example expected frequencies formula give given hazard ratio histogram hypothesis is true hypothesis test important indicate individuals interest interpretation linear logrank test mean and standard measurements median medical research multiple regression non-parametric Normal distribution Normal plot null hypothesis obtained outcome paired patients placebo population possible predict probability problem proportion random ranks reasonable regression analysis regression model relation residuals risk scores serum albumin shown in Figure shows skewed squared test standard deviation standard error standard Normal statistical analysis statistical methods statistically significant test statistic tion Total treatment trend usually values variables variation women