LEADER 05268nam 2200721Ia 450 001 9911019779903321 005 20200520144314.0 010 $a9786612307461 010 $a9781282307469 010 $a1282307460 010 $a9780470316498 010 $a0470316497 010 $a9780470317204 010 $a0470317205 035 $a(CKB)1000000000687556 035 $a(EBL)468655 035 $a(SSID)ssj0000343230 035 $a(PQKBManifestationID)11264960 035 $a(PQKBTitleCode)TC0000343230 035 $a(PQKBWorkID)10288730 035 $a(PQKB)10184521 035 $a(MiAaPQ)EBC468655 035 $a(OCoLC)264626247 035 $a(PPN)159342384 035 $a(Perlego)2776384 035 $a(EXLCZ)991000000000687556 100 $a20791220d1980 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical methods for comparative studies $etechniques for bias reduction /$fSharon Anderson ... [et al.] 210 $aNew York $cWiley$dc1980 215 $a1 online resource (309 p.) 225 1 $aWiley series in probability and mathematical statistics 300 $aDescription based upon print version of record. 311 08$a9780471048381 311 08$a0471048380 320 $aIncludes bibliographies and index. 327 $aStatistical Methods for Comparative Studies; Contents; 1. INTRODUCTION; 1.1 Problems of Comparative Studies: An Overview,; 1.2 Plan of the Book,; 1.3 Notes on Terminology,; 2. CONFOUNDING FACTORS; 2.1 Adjustment for a Confounding Factor,; 2.2 Bias, Precision, and Statistical Significance,; 2.3 Some Qualitative Considerations,; Appendix 2A Bias, Precision, and Mean Squared Error; References,; 3. EXPRESSING THE TREATMENT EFFECT; 3.1 Measures of Treatment Effect,; 3.2 What Happens when there is Confounding,; 3.3 Treatment Effect Dependent on a Background Factor,; References, 327 $a4. RANDOMIZED AND NONRANDOMIZED STUDIES4.1 Definition of Randomization,; 4.2 Properties of Randomization,; 4.3 Further Points on Randomization,; 4.4 Reasons for the Use of Nonrandomized Studies,; 4.5 Types of Comparative Studies,; 4.6 Our Attitude toward Nonrandomized Studies,; Appendix 4A The Odds Ratio and the Relative Risk in Case-Control Studies; References,; 5. SOME GENERAL CONSIDERATIONS IN CONTROLLING BIAS; 5.1 Omitted Confounding Variables,; 5.2 Measurement Error,; 5.3 The Regression Effect,; 5.4 Specifying a Mathematical Model,; 5.5 Sampling Error, 327 $a5.6 Separation of Groups on a Confounding Factor,5.7 Summary,; References,; 6. MATCHING; 6.1 Effect of Noncomparability,; 6.2 Factors Influencing Bias Reduction,; 6.3 Assumptions,; 6.4 Caliper Matching,; 6.5 Nearest Available Matching,; 6.6 Stratified Matching,; 6.7 Frequency Matching,; 6.8 Mean Matching,; 6.9 Estimation and Tests o Significance,; 6.10 Multivariate Matching,; 6.11 Multiple Comparison Subjects,; 6.12 Other Considerations,; 6.13 Conclusions,; Appendix 6A Some Mathematical Details,; References,; 7. STANDARDIZATION AND STRATIFICATION 327 $a7.1 Standardization-Example and Basic Information,7.2 Choice of Standard Population,; 7.3 Choice of Standardization Procedure,; 7.4 Statistical Considerations for Standardization,; 7.5 Extension of Standardization to Case-Control Studies,; 7.6 Stratification,; 7.7 Standardization and Stratification for Numerical Outcome Variables,; 7.8 Extension to More Than One Confounding Factor,; 7.9 Hypothesis Testing,; Appendix 7A Mathematical Details of Standardization,; Appendix 7B Stratified Estimators of the Odds Ratio,; References,; 8. ANALYSIS OF COVARIANCE; 8.1 Background, 327 $a8.2 Example: Nutrition Study Comparing Urban and Rural Children,8.3 The General ANCOVA Model and Method,; 8.4 Assumptions Underlying the Use of ANCOVA,; 8.5 Dealing with Departures from the Assumptions,; Appendix 8A Formulas for Analysis of Covariance Calculations,; References.; 9. LOCIT ANALYSIS; 9.1 Developing the Logit Analysis Model,; 9.2 Use of Logit Analysis to Control for a Confounding Variable,; 9.3 Parameter Estimation by Maximum Likelihood,; 9.4 Other Parameter Estimation Procedures,; 9.5 Hypothesis Testing,; 9.6 Case-Control Studies,; 9.7 Checking the Model, 327 $a9.8 Multiple Confounding Factors, 330 $aBrings together techniques for the design and analysis of comparative studies. Methods include multivariate matching, standardization and stratification, analysis of covariance, logit analysis, and log linear analysis. Quantitatively assesses techniques' effectiveness in reducing bias. 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