Replication and Evidence Factors in Observational Studies (Hardcover, 1)

I Background: Aspects of Causal Inference 1. Causal Inference in Randomized Experiments A Randomized Experiment Structure and Notation Covariates and Outcomes Causal Effects with Two Treatment Groups Inference with Random Assignment Randomization Tests for Continuous Outcomes Confidence Sets for Causal Effects The General Situation Summary: Randomization Simplifies Causal Inference Using R 2. Causal Inference in Observational Studies How Are Observational Studies Different From Experiments? Sensitivity Analysis *Another Example of Sensitivity Analysis Design Sensitivity Summary: Biased Treatment Assignment Using R Exercises 3. Replication and its Limits Biases Can Replicate Some Perspectives Replications that Disrupt Some Potential Biases Instruments and Replication Summary: Replication is Not Repetition II Evidence Factors in Practice 4. Examples of Studies with Evidence Factors Smoking and Periodontal Disease Antineoplastic Drugs and DNA Damage Lead Absorption in Children Minimum Wages and Employment Benzene and Chromosome Aberrations Summary: Mutually Supporting, Unrelated Comparisons Using R Exercises 5. Simple Analyses with Evidence Factors Structure of the Simple Analyses Antineoplastic Drugs and DNA Damage Smoking and Periodontal Disease Factors that Do Not Concur Summary: Strengthen Evidence of Cause and Effect Using R Exercises 6. Planned Analyses with Evidence Factors Closed Testing with Three Factors Confidence Intervals for Magnitudes of Effect Evidence Factors Plus An Incompatible Comparison Summary: Planned Analyses Can Accomplish More Using R Exercises III Theory of Evidence Factors 7. Dependent P-Values Dependent P-values Larger than Uniform Jointly Larger Than Uniform Creating Jointly Valid, Possibly Dependent P-values Combining Jointly Valid, Possibly Dependent P-values Summary: Dependent P-values Jointly Larger than Uniform Exercises 8. Treatment Assignments as Permutations Formalizing Intuition About Unrelated Pieces of Evidence Individuals, Strata and Treatment Positions Permutation Matrices Pick Matrices Direct Sums of Permutation Matrices Subpick Matrices Treatments with Doses Permuting Strata of the Same Size Permuting Several Permutation Matrices Doing Several Things at Once Summary: A Treatment Assignment is a Permutation Complement: Split Matrices Exercises 9. Sets of Treatment Assignments Sets of Permutation Matrices Products of Sets Unique Representation As a Product of Two Factors *Closure Summary: Factoring Sets of Treatment Assignments Exercises 10.Probability Distributions for Treatment Assignments One Distribution A Set of Distributions *Some Technical Remarks and Definitions Sensitivity Analysis Summary: Probability on a Set of Treatment Assignments Complement: Sensitivity Analysis with Doses Exercises 11.Factors Marginal and Conditional Distributions Joint Distribution of Two Sensitivity Analyses Sets of Marginal and Conditional Distributions Ignoring a Factor Conditioning on a Factor Combining Two Sensitivity Analyses Summary: Combining Two Sensitivity Analyses Complement: More Than Two Factors Exercises 12.*Groups of Permutation Matrices Why Groups? Groups Groups in Evidence Factors: Some Examples Group Products Summary: Groups Provide the Needed Factors IV Aspects of Design 13.Constructing Matched Samples with Evidence Factors Aspects of Design Nearly Optimal Complete Blocks Optimal Incomplete Block Designs Variation in Treatment Within and Between Institutions Comparing Study Designs: Which Design is Best? Summary: Build Evidence Factors into the Design Using R 14.Design Elements for Evidence Factors Some Common Design Elements *Symmetric Sets of Biases