PTP 560 Research Methods Week 4 Thomas Ruediger, PT

Single Subject Design Similar (not identical) to clinical practice Independent variable is the intervention Dependent variable is the response (outcome) Requires strict attention and control Allows for flexibility to observe change – In clinical (real world) setting

Single Subject Designs Sample – Single individual or small group: Assume 1 person for this class – A community, department or institution Advantage – Small sample: saves time/money; clinically useful – Appreciates/differentiates unique characteristics Methods – Clinically viable, controlled experimental approach – Flexible to observe change in ongoing treatments

Single Subject Structure Repeated Measures to start - Baseline – This is where it differs from clinical practice (“the single feature” – P & W) – Attempt to reflect ongoing background effects – How is this different than clinical practice? In the clinic we start right away, not wait for a baseline. While for SSS we will wait for 3 treatments to begin specific intervention to test Also subjects needs to sign consent form. Two caveats on these baseline measures – Not unethical to withhold treatment when outcome is not known – Not all treatment is withheld, just the one of interest

Single Subject Structure Baseline Measures (AT LEAST THREE!) – Stable baseline is most desirable Indicates that the behavior is stable Increases confidence that changes after the intervention begins are due to that intervention – Variable baseline is problematic Usually requires continued baseline collection Investigate possible causes (Cyclical, time of day/week etc) If cannot resolve, at risk for obscuring intervention effect – Trend or slope of baseline Accelerating or decelerating May be stable or unstable

Single Subject Structure How many baseline measure are needed? – AT LEAST 3

Single Subject Designs Baseline Characteristics Stable or variable? – Consistency of the response – Left are stable, right are unstable Trend – Rate of change or slope

Single Subject Structure Target behavior Quantifying the measure? – Frequency % correct In an interval – Duration – Quantitative Score (Magnitude)

Single Subject Structure Intervention Phase – At least 3 data points – The minimum number of data points needed in an A-B study is 6 (3 for phase A and 3 for phase B) Reliability usually assessed=assuming no change the measurement is the same. – Concurrently with data collection – Instead of in pilot study – Inter-rater by percentage agreement A(baseline)-B(intervention or independent variable) is the simplest form of Single Subject Design – Major limitation is ability to control – This limitation is a threat to internal validity

Single Subject Designs Design Phases Baseline Phase (Left) – Information during “no treatment” – Serves as a control condition Intervention Phase (Right) – Measures during treatment – Serves as comparison

Single Subject Structure A-B-A design useful to help internal validity – The Causal Nature, However, behavior must be reversible Reversibility just needs to be sig. different, but not back to baseline. A-B-A-B – Strengthens design – Again behavior must be reversible Consider Multiple Baselines (Fig 12.7) – To avoid being unethical, if withdrawal is unethical – If behavior is: Nonreversible Prone to carryover

Week 1Week 2Week 3Week 4 BaselineIntervention Function A Baseline B Intervention A Baseline ABA Design

Week 1Week 2Week 3Week 4 BaselineIntervention Function A Baseline B Intervention A Baseline B Intervention ABAB Design

Single Subject Structure Multiple Baselines – Across behaviors One subject Multiple behaviors (outcomes) – Across subjects Multiple individual subjects One target behavior – Across conditions One subject One behavior Two or more conditions/situations/environments

Single Subject Structure Non-concurrent Multiple Baselines (Fig 12.8) – Multiple individual subjects – One target behavior – Intervention begun at randomly assigned intervals Alternate Treatments (Fig 12.9) – Appropriate when response is immediate – Session by session – Day by Day Multiple Treatment A-B-C-A (Fig 12.10) – Across conditions One subject One behavior Two or more conditions/situations/environments

Single Subject Structure Data analysis – Comparisons ONLY across adjacent phases Only compare letters that are next to each other, so can’t compare A to C. – Are the data level? – Visual – Mean – Is there a trend? – Direction within a phase – Accelerating/decelerating/constant – What is the slope? – Rate of change

Single Subject Structure When making comparisons in these scenarios, what can you compare? A-B-A A-B-C-A A-B-C-D-E-F-G-A

Single Subject Structure Data analysis – The split middle Apply the binomial test (Table A.9) – Two standard deviation method – Serial dependency – C statistic – Statistical Process Control Upper and Lower Control Limits – Based on 3 standard deviations – Then apply the three rules (p 266)

Autocorrelation: if data are correlated

Single Subject Designs Celeration Line (Split Middle Line) – Measure of central tendency – Represents the median point of the data – Counts data points above or below in a given phase. – Adjust line up or down to a point where data is equally divided – Extend into intervention phase

Celeration Line

Single Subject Designs Non-Parametric Celeration Line Binomial Test 1. Extend split middle line of baseline phase into intervention phase 2. Count Total points Count points above Count points below 3. Consult Table A.9 This Figure is in Ed 3

Single Subject Design Generalization is a challenge  Strengthened by: – Direct replication – Systematic Replication: with purposeful change in some parameter – Clinical Replication: taking it out of realm of research, take it out to a clinic – Social Validation: is it okay to use this intervention.

Single Subject Designs Social Validation Importance within specific social context – Setting Treatment Goals Appropriate to functional needs of patient; social importance – Procedures Acceptable treatments/interventions; patient preference, comfort and safety – Effects Appropriate Magnitude of treatment & treatment effects

Exploratory Research Prospective: randomized-control study Retrospective: chart review study Exploratory: generating questions Descriptive For relationship investigation: SSS For correlation (how much does X vary with Y) and regression analysis (predicted ability) The Case of the “Haves” and the “Have Nots” Fig 13.2, Fig 13.3 : with an ACL without an ACL have this risk

Exploratory Research Causality can be argued for better with – 1. Established time sequence – 2. Strong association – 3. Biologic credibility – 4. Consistency with other studies – 5. Dose-response relationship