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Principles of Experimental Design October 16, 2002 Mark Conaway Experimental Design
Use examples to illustrate principles • Reference: Maughan et
al. (1996) Effects of Ingested Fluids on Exercise Capacity and on Cardiovascular and Metabolic Responses to Prolonged Exercise in Man. Experimental Physiology, 81, 847-859. • From paper summary • “The present study examined the effects of ingestion of water and two dilute glucose-electrolyte drinks on exercise performance and ….” Experimental Design
Process of Experimental Design • What’s the research question? • effect on exercise capacity... • What treatments to study? • control group (no liquid intake) vs water vs 2 types of dilute glucose-electrolyte solutions • What are the levels of the treatments? • Paper describes exact composition of solutions • How to measure the outcome of interest • Exercise capacity: time to exhaustion on stationary cycle Experimental Design
Entire Process of Experimental Design • Process of design relies heavily on researchers’ knowledge of the field, though statistical principles can help • Do we need a “no liquid” control group? • Is “time to exhaustion” a valid measure of exercise capacity? Experimental Design
Statistical DOE: Allocate treatments to experimental material to... • Remove systematic biases in the evaluation of the effects of the treatments • “unbiased estimates of treatment effects” • Provide as much information as possible about the treatments from an experiment of this size
• “precision” Experimental Design
Statistical DOE • Remove bias, obtain maximum precision, keeping in mind • simplicity/feasibility of design • natural variation in experimental units • generalizability Experimental Design
Focus on comparative experiments • Treatments can be allocated to the experimental units by the experimenter • Other types of studies also have these as goals but: • Methods for achieving goals (unbiased estimates, precision) in comparative experiments rely on having treatments under control of experimenter Experimental Design
Back to example • 4 treatments • no water (N) • water (W) • isotonic glucose-electrolyte(I) • hypotonic glucose-electrolyte (H) • Outcome: time to exhaustion on bike • Pool of subjects available for study Experimental Design
Design 1: subjects select treatment • Does this method of allocation achieve the goals? • Possible that this method induces biases in comparisons of treatments • e.g. Would “naturally” better athletes choose electrolytes? • e.g. Would more competitive athletes choose electrolytes? Experimental Design
Design 1A: Investigators assign treatments • “Systematically” • Everyone on Monday gets assigned no water • Tuesday subjects get water only... • “Nonsystematically”: • Whatever I grab out of the cooler... • Again possible that this method induces biases in comparisons of treatments Experimental Design
What are the sources of the biases? • Key point: Bias in evaluating treatments due to allocating different treatments to different types of subjects • e.g., “better” riders get electrolyte • so differences between treatments mixed up with differences between riders • To have unbiased estimates of effects of treatment, need to have
“comparable groups” Experimental Design
Randomization is key to having comparable groups • Assign treatments at random • Note: Draw distinction between “random” and “non-systematic” • Randomization is key element for removing bias • In principle, creates comparable groups even
on factors not considered by the investigator Experimental Design
Completely randomized design • Randomly assign treatments to subjects • Generally assign treatments to equal numbers of subjects • Does this give us the most information (precision) about the treatments? • Get precise estimates by comparing treatments
on units that are as similar as possible. Experimental Design
Randomized block designs (RBD)General • Group units into subgroups (blocks) such that units within blocks are more homogeneous than in the group as a whole • Randomly assign treatments to units within subgroups (blocks) Experimental
Design
Block 1 Block 2 Block 3 Randomized block designs in exercise example • Do an initial “fitness screen” - let subjects ride bike (with water?) until exhaustion. • Arrange subjects in order of increasing times (fitness) • F1, F2, F3, F4 F5, F6,F7,F8 F9,F10,F11,F12 Experimental Design
Block 3 Block 2 Block 1 Randomized block designs in exercise example • Randomly assign treatments to units within F1, F2, F3, F4 F5, F6,F7,F8 F9,F10,F11,F12 I H N W W N I H H W I N Experimental Design
Advantages of RBD • If variable used to create blocks is highly related to outcome, generally get much more precision than a CRD without doing a larger experiment • Essentially guarantees that treatments will be compared on groups of subjects that are comparable on initial level of fitness Experimental Design
Disadvantages of RBD • Now require 2 assessments per subject if block in this way • Note: Could use some other measure of initial fitness that doesn’t require an initial assessment on the bike Experimental Design
Can
take idea further • Could group by more than one variable • Each blocking variable • Adds complexity • Might not increase precision if grouping variable is not sufficiently related to outcome Experimental Design
Repeated measures designs/Cross-over trials • Natural extension
of idea in RBD: want to compare treatments on units that are as similar as possible • 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 Experimental Design
Repeated measures designsCross-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.
Experimental Design
Cross-over designs: Example • Cross-over was done in actual experiment • Each of 12 subjects observed under each condition • Randomize order. • One week period between observations. Experimental Design
Cross-over designs: Example • Illustrates the importance of • ``wash-out period'' and • randomizing/balancing the order that treatments are applied. Experimental Design
In general, which
design? • Is the natural variability within a subject likely to be small relative to the natural variability across subjects? • More similarity within individuals or between individuals? • Are there likely to be carry-over effects? • Are there likely to be ``drop-outs''? • Is a cross-over design feasible? Experimental Design
Which
design? • No definitive statistical answer to the question. • Answer depends on knowledge of • experimental material and • the treatments to be studied Experimental Design
There are three basic principles behind any experimental design:.
Randomisation: the random allocation of treatments to the experimental units. ... .
Replication: the repetition of a treatment within an experiment allows: ... .
Reduce noise: by controlling as much as possible the conditions in the experiment..
There are 3 main principles of experimental design: Randomisation: Random assignment of treatment to experimental units. Replication: Repeated application of the basic treatment to multiple experimental units.
Experimental research design are concerned with examination of the effect of independent variable on the dependent variable, where the independent variable is manipulated through treatment or intervention(s), & the effect of those interventions is observed on the dependant variable.
