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    Primer of biostatistics / Stanton A. Glantz.

    • Title:Primer of biostatistics / Stanton A. Glantz.
    •    
    • Author/Creator:Glantz, Stanton A.
    • Published/Created:New York : McGraw-Hill, Medical Pub. Division, ©2002.
    • Holdings

       
    • Library of Congress Subjects:Biometry.
      Medical statistics.
    • Medical Subjects: Biometry.
    • Edition:5th ed.
    • Description:xviii, 489 p. : ill. ; 21 cm.
    • Notes:Includes bibliographical references and index.
    • ISBN:0071379460
    • Contents:1. Biostatistics and Clinical Practice. The Changing Medical Environment. What Do Statistical Procedures Tell You? Why Not Depend on the Journals? Why Has the Problem Persisted?
      2. How to Summarize Data. The Mean. Measures of Variability. The Normal Distribution. Percentiles. How to Make Estimates from a Limited Sample. How Good Are These Estimates?
      3. How to Test for Difference between Groups. The General Approach. Two Different Estimates of the Population Variance. What Is a "Big" F? Three Examples
      4. The Special Case of Two Groups: The t Test. The General Approach. The Standard Deviation of a Difference or a Sum. Use of t to Test Hypotheses about Two Groups. What If the Two Samples Are Not the Same Size? The Examples Revisited. The t Test Is an Analysis of Variance. Common Errors in the Use of the t Test and How to Compensate for Them. How to Use t Tests to Isolate Differences between Groups in Analysis of Variance. Other Approaches to Multiple Comparison Testing: The Student-Newman-Keuls Test. Which Multiple Comparison Procedure Should You Use? Multiple Comparisons against a Single Control. The Meaning of P
      5. How to Analyze Rates and Proportions. Back to Mars. Estimating Proportions from Samples. Hypothesis Tests for Proportions. Another Approach to Testing Nominal Data: Analysis of Contingency Tables. Chi-Square Applications to Experiments with More Than Two Treatments or Outcomes. The Fisher Exact Test. Measures of Association Between Two Nominal Variables
      6. What Does "Not Significant" Really Mean? An Effective Diuretic. Two Types of Errors. What Determines a Test's Power? Power and Sample Size for Analysis of Variance. Power and Sample Size for Comparing Two Proportions. Power and Sample Size for Relative Risk and Odds Ratio. Power and Sample Size for Contingency Tables. Practical Problems in Using Power. What Difference Does It Make?
      7. Confidence Intervals. The Size of the Treatment Effect Measured as the Difference of Two Means. The Effective Diuretic. What Does "Confidence" Mean? Confidence Intervals Can Be Used to Test Hypotheses. Confidence Interval for the Population Mean. The Size of the Treatment Effect Measured as the Difference of Two Rates or Proportions. Confidence Interval for Rates and Proportions. Confidence Intervals for Relative Risk and Odds Ratio. Confidence Interval for the Entire Population
      8. How to Test for Trends. More about the Martians. How to Estimate the Trend from a Sample. How to Compare Two Regression Lines. Correlation and Correlation Coefficients. The Spearman Rank Correlation Coefficient. Power and Sample Size in Regression and Correlation. Comparing Two Different Measurements of the Same Thing: The Bland-Altman Method
      9. Experiments When Each Subject Receives More than One Treatment. Experiments When Subjects Are Observed before and after a Single Treatment: The Paired t Test. Another Approach to Analysis of Variance. Experiments When Subjects Are Observed after Many Treatments: Repeated-Measures Analysis of Variance. Experiments When Outcomes Are Measured on a Nominal Scale: McNemar's Test
      10. Alternatives to Analysis of Variance and the t Test Based on Ranks. How to Choose between Parametric and Nonparametric Methods. Two Different Samples: The Mann-Whitney Rank-Sum Test. Each Subject Observed before and after One Treatment: The Wilcoxon Signed-Rank Test. Experiments with Three or More Groups When Each Group Contains Different Individuals: The Kruskal-Wallis Statistic. Experiments in Which Each Subject Receives More than One Treatment: The Friedman Test
      11. How to Analyze Survival Data. Censoring on Pluto. Estimating the Survival Curve. Comparing Two Survival Curves. Gehan's Test. Power and Sample Size
      12. What Do the Data Really Show? When to Use Which Test. Randomize and Control. Does Randomization Ensure Correct Conclusions? Problems with the Population. How You Can Improve Things.
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