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EMR 6550: Experimental and Quasi-Experimental Designs PowerPoint Presentation
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EMR 6550: Experimental and Quasi-Experimental Designs

EMR 6550: Experimental and Quasi-Experimental Designs

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EMR 6550: Experimental and Quasi-Experimental Designs

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  1. EMR 6550:Experimental and Quasi-Experimental Designs Dr. Chris L. S. Coryn Kristin A. Hobson Fall 2013

  2. Agenda Quasi-experimental designs that use both control groups and pretests Interrupted time-series designs Design and power problems

  3. Designs that Use Both Control Groups and Pretests

  4. Untreated Control Group Design with Dependent Pretest and Posttest Samples A selection bias is always present, but the pretest observation allows for determining the magnitude and direction of bias

  5. Outcome Pattern 1 • Both groups grow apart at different average rates in the same direction Treatment Control • This pattern is consistent with treatment effects and can sometimes be causally interpreted, but it is subject to numerous threats, especially selection-maturation

  6. Outcome Pattern 2 • Spontaneous growth only occurs in the treatment group Treatment Control • Not a lot of reliance can be placed on this pattern as the reasons why spontaneous growth only occurred in the treatment group must be explained (e.g., selection-maturation)

  7. Outcome Pattern 3 • Initial pretest differences favoring the treatment group diminish over time Treatment Control • Same internal validity threats as outcome patterns #1 and #2 except that selection-maturation threats are less plausible

  8. Outcome Pattern 4 Control • Initial pretest differences favoring the control group diminish over time Treatment • Subject to numerous validity threats (e.g., selection-instrumentation, selection-history), but generally can be causally interpreted

  9. Outcome Pattern 5 • Outcomes that crossover in the direction of relationships Treatment Control • Most amenable to causal interpretation and most threats cannot plausibly explain this pattern

  10. Modeling Selection Bias • Simple matching and stratifying • Overt biases with respect to measured variables/characteristics • Instrumental variable analysis • Statistical modeling of covariates believed to explain selection biases • Hidden bias analysis • Difference with respect to unmeasured variables/characteristics • Sensitivity analysis (how much hidden bias would need to be present to explain observed differences) • Propensity score analysis • Predicted probabilities of group membership • Propensities then used for matching or as covariate

  11. Effect-Decay Functions Immediate Effect, No Decay Delayed Effect Large Large Response Response Small Small ProgramOnset ProgramTermination ProgramOnset ProgramTermination Time Time Immediate Effect, Rapid Decay Early Effect, Slow Decay Large Large Response Response Small Small ProgramOnset ProgramTermination ProgramOnset ProgramTermination Time Time

  12. Untreated Control Group Design with Dependent Pretest and Posttest Samples Using a Double Pretest • Permits assessment of selection-maturation on the assumption that the rates between O1 and O2 will continue between O2 and O3 • Testable only on the control group

  13. Untreated Control Group Design with Dependent Pretest and Posttest Samples Using Switching Replications • A strong design and only a pattern of historical changes that mimics the time sequence of the treatment introductions can serve as an alternate explanation • The addition of treatment removal (X) can strengthen cause-effect claims

  14. Untreated Control Group Design with Dependent Pretest and Posttest Samples Using Reversed Treatment Control Group • Interpretation of this design depends on producing two effects with opposite signs • Adding a control is useful • Ethically, often difficult to use a reversed treatment

  15. Interrupted Time-Series Designs

  16. Interuppted Time-Series • A large series of observations made on the same variable consecutively over time • Observations can be made on the same units (e.g., people) or on constantly changing units (e.g., populations) • Must know the exact point at which a treatment or intervention occurred (i.e., the interruption) • Interrupted time-series designs are powerful cause-probing designs when experimental designs cannot be used and when a time series is feasible

  17. Types of Effects Form of the effect (slope or intercept) Permanence of the effect (continuous or discontinuous) Immediacy of the effect (immediate or delayed)

  18. Analytic Considerations • Independence of observations • (Most) statistical analyses assume observations are independent (one observation is independent of another) • In interrupted time-series, observations are autocorrelated (related to prior observations or lags) • Requires a large number of observations to estimate autocorrelation • Seasonality • Observations that coincide with seasonal patterns • Seasonality effects must be modeled and removed from a time-series before assessing treatment impact

  19. Simple Interrupted Time-Series Design The basic interrupted time-series design requires one treatment group with many observations before and after a treatment

  20. Change in Intercept Intervention Change in intercept

  21. Change in Slope Intervention Change in slope

  22. Weak and Delayed Effects Intervention Impact begins

  23. Validity Threats • With most interrupted time-series designs, the major validity threat is history • Events that occur at the same time as the treatment was introduced • Instrumentation is also often a threat • Over long time periods, methods of data collection may change, how variables are defined and/or measured may change • Selection is sometimes a threat • If group membership changes abruptly

  24. Additional Designs (1) nonequivalent control group, (2) nonequivalent dependent variable, and (3) removed treatment

  25. Nonequivalent Control Group Intervention Treatment group Control group

  26. Nonequivalent Dependent Variable Intervention Nonequivalent dependent variable Dependent variable

  27. Removed Treatment Introduction Removal Treatment period

  28. Design and Power Problems

  29. Problem #1 • A school administrator wants to know whether students in his district are scoring better or worse than the national norm of 500 on the SAT • He decides that a difference of 20-25 points or more from this normative value would be important to detect • He anticipates that the standard deviation of scores in his district is about 80 points • Determine the number of students necessary for power at 95% to detect a difference of 20 and 25 points • Graph both • Diagram the design of the study

  30. Problem #2 • Patients suffering from allergies are nonrandomly assigned to a treatment and placebo condition and asked to rate their comfort level on a scale of 0 to 100 • The expected standard deviation is 20 and a difference of 10-20 is expected (treatment = 50-60 and placebo = 40) • Determine the number of patients necessary for power at 95% to detect a difference of 10 and 20 points • Graph both • Diagram the design of the study

  31. Problem #3 • The cure rate for two current treatments are 10% and 60%, respectively • The alternative treatments are expected to increase the cure rate by 10% • Determine the number of patients necessary for power at 95% to detect a difference of 10% for both scenarios • Graph both • Diagram the design of the studies