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Contributors Preface I: Design 1: Development and Testing of Treatments 2: Qualitative Research 3: Single-Case Experimental Designs 4: Studies of Associations 5: Observational Studies: Retrospective Versus Prospective 6: Historical Controls 7: Subject as Own Control 8: Longitudinal Cohort Versus Cross-Sectional Cohort Studies 9: Survey Research 10: Choice of Control Groups in Treatment Studies 11: Randomization 12: Special Issues in Randomized Controlled Trials 13: Secondary Data Analysis 14: Scoping Study 15: Systematic Reviews 16: Meta-Analysis 17: Recommendations for Reporting Research Studies 18: Developing and Evaluating Systematic Reviews and Practice Guidelines
II: Statistics 19: Introduction 20: Types of Data 21: Descriptive Statistics 22: Data Distributions 23: Samples and Populations 24: Visual Display of Data 25: Data Cleaning 26: Missing Data and Imputation 27: Estimation 28: Hypothesis Testing 29: Sample Size and Power 30: Comparing Matched Samples With Continuous-Type Outcomes: Two Groups 31: Comparing Independent Samples With Continuous-Type Outcomes: Two Groups 32: Comparing Independent Samples for Continuous-Type Outcomes: Three Groups or More 33: Correlation 34: Simple Linear Regression 35: Multiple Linear Regression 36: Longitudinal and Clustered Data 37: Significance Tests: Categorical Data 38: Measures of Effect Sizes for Categorical Outcomes 39: Logistic Regression 40: Kaplan–Meier Estimator 41: Log-Rank Test 42: Proportional Hazards Model 43: Sources of Error: Selection Bias, Information Bias, and Confounding 44: Mediation Analyses 45: Epidemiology Study: Incidence and Prevalence 46: Validity and Performance of Screening: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value 47: Statistical Tools for Agreement and Reliability Studies 48: Classical Test Theory 49: Item Response Theory
III: Implementation 50: Successful Grant Applications 51: Sources of Research Funding 52: Planning Grants 53: Developing the Idea With Stakeholder Input 54: Research Questions, Hypotheses, Aims, and Abstract 55: Reviewing the Literature 56: Background and Significance 57: Preliminary Studies and Experience 58: Methods and Design 59: Types of Measures 60: Letters of Support 61: Budget and Budget Justification 62: Preaward Management 63: Post-Award Management 64: Good Clinical Practices 65: Research Misconduct 66: Study Protocol 67: Manual of Procedures 68: Treatment Manuals 69: Participant Recruitment and Enrollment 70: Participant Retention 71: Data Collection 72: Case Report Forms 73: Database Development 74: Data Dictionary 75: Data Management 76: Plan of Operation 77: Evaluation 78: Regulatory Binder and Essential Documents 79: Adverse Events 80: Protocol Deviations and Violations 81: Data and Safety Monitoring 82: Multicenter Trials 83: Site Monitoring and Oversight
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70: Participant Retention
Contributors Preface I: Design 1: Development and Testing of Treatments 2: Qualitative Research 3: Single-Case Experimental Designs 4: Studies of Associations 5: Observational Studies: Retrospective Versus Prospective 6: Historical Controls 7: Subject as Own Control 8: Longitudinal Cohort Versus Cross-Sectional Cohort Studies 9: Survey Research 10: Choice of Control Groups in Treatment Studies 11: Randomization 12: Special Issues in Randomized Controlled Trials 13: Secondary Data Analysis 14: Scoping Study 15: Systematic Reviews 16: Meta-Analysis 17: Recommendations for Reporting Research Studies 18: Developing and Evaluating Systematic Reviews and Practice Guidelines
II: Statistics 19: Introduction 20: Types of Data 21: Descriptive Statistics 22: Data Distributions 23: Samples and Populations 24: Visual Display of Data 25: Data Cleaning 26: Missing Data and Imputation 27: Estimation 28: Hypothesis Testing 29: Sample Size and Power 30: Comparing Matched Samples With Continuous-Type Outcomes: Two Groups 31: Comparing Independent Samples With Continuous-Type Outcomes: Two Groups 32: Comparing Independent Samples for Continuous-Type Outcomes: Three Groups or More 33: Correlation 34: Simple Linear Regression 35: Multiple Linear Regression 36: Longitudinal and Clustered Data 37: Significance Tests: Categorical Data 38: Measures of Effect Sizes for Categorical Outcomes 39: Logistic Regression 40: Kaplan–Meier Estimator 41: Log-Rank Test 42: Proportional Hazards Model 43: Sources of Error: Selection Bias, Information Bias, and Confounding 44: Mediation Analyses 45: Epidemiology Study: Incidence and Prevalence 46: Validity and Performance of Screening: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value 47: Statistical Tools for Agreement and Reliability Studies 48: Classical Test Theory 49: Item Response Theory
III: Implementation 50: Successful Grant Applications 51: Sources of Research Funding 52: Planning Grants 53: Developing the Idea With Stakeholder Input 54: Research Questions, Hypotheses, Aims, and Abstract 55: Reviewing the Literature 56: Background and Significance 57: Preliminary Studies and Experience 58: Methods and Design 59: Types of Measures 60: Letters of Support 61: Budget and Budget Justification 62: Preaward Management 63: Post-Award Management 64: Good Clinical Practices 65: Research Misconduct 66: Study Protocol 67: Manual of Procedures 68: Treatment Manuals 69: Participant Recruitment and Enrollment 70: Participant Retention 71: Data Collection 72: Case Report Forms 73: Database Development 74: Data Dictionary 75: Data Management 76: Plan of Operation 77: Evaluation 78: Regulatory Binder and Essential Documents 79: Adverse Events 80: Protocol Deviations and Violations 81: Data and Safety Monitoring 82: Multicenter Trials 83: Site Monitoring and Oversight
10.1891/9781617050992.0070
Authors
- Hammond, Flora M., MD
Abstract
The internal and external validities of a clinical research study are threatened when enrolled participants are lost or drop out before the study is completed. Cognitive impairment, financial stress, housing instability, substance abuse, and transportation access are common factors in attrition in studies with some clinical populations. This chapter outlines the implications of and common reasons for attrition, and provides strategies to enhance retention. Those with complete data often differ significantly from those lost to attrition. Bias occurs when lost data are not random and are related to the outcome(s). Bias may confound associations and distort prevalence estimates. Statistical techniques used to account for missing data assume that those who are missing have results like the observed cases with similar measured characteristics.