Download Repeated Measures Designs: Advantages, Disadvantages, and Types and more Study notes Design in PDF only on Docsity! Repeated Measures Design Mark Conaway October 11, 1999 Common design: Repeated measures designs • Take measurements on same subject over time or under different conditions. • Same basic idea as a randomized block design: – treatment effects measured on ``units'' that are similar as possible. Repeated measures designs Cross-over Designs • Subjects receive every treatment • Most common is ``two-period, two-treatment'' – Subjects are randomly assigned to receive either • A in period 1, B in period 2 or • B in period 1, A in period 2 Repeated measures designs Cross-over Designs • Important assumption: No carry-over effects – effect of treatment received in each period is not affected by treatment received in previous periods. • To minimize possibility of carry-over effects – ‘`wash-out'' time between the periods in which treatments are received. Cross-over designs: Example • Treatments: Impermeable (IP) / Semi- Permeable (SP) • Outcomes: Skin temperature, heat storage, oxygen consumption • Protocol: – 6 men studied under both types of clothing. – 3 men randomized to order (IP, SP), 3 men to (SP, IP) Rissanen and Rintamaki (1997) Ergonomics p. 141-150. Cross-over designs: Example 2 Possible designs • Completely randomized? • Randomized block? • Cross-over: – Each subject observed under each condition – Randomize order. – One week period between observations. Cross-over designs: Example 2 • Precision determined by variation in ``time to exhaustion'' by a subject over multiple occasions. – Avoids basing precision on variation in time to exhaustion between different subjects Cross-over designs: Examples • Both examples illustrate importance of – ``wash-out period'' and – randomizing/balancing the order that treatments are applied. Measurements over time (longitudinal studies) • Advantage: – May be the only design that answers questions of interest • Diasadvantages: – Analyses can be difficult – Can be biased due to dropouts, especially if dropout related to treatment being studied Measurements over time • Important to consider individual subject profiles over time. • Ignoring individual subjects can give misleading impression of – variation – direction of effects Ignoring individual patients can misrepresent variation Modified data from Crowder and Hand. Analysis of Repeated Measures Ignoring individual patients
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Analysis by summary measures • Matthews et al recommend analysis by summary measure • Common summary measures are – individual slopes – area under curve Example of Analysis by individual slopes. Data from Crowder and Hand Handling dropouts in longitudinal studies • Possible approaches. • Analyze only those who complete therapy. – May bias results, especially if reason for dropout is related to outcome Handling dropouts in longitudinal studies • Use ``Last Observation Carried Forward (LOCF)'' method. – After patient has withdrawn, use the last observation. – Could bias results; last observation may not reflect true state of subject – Does not provide reasonable assessment of uncertainty – Generally dismissed as a method for handling dropouts Handling dropouts in longitudinal studies • Modeling the dropout process – Requires assumptions and sophisticated modeling methods. • No generally accepted method for handling dropouts.
