chapter 11: sequential clinical trials
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Chapter 11: Sequential Clinical Trials. Descriptive Exploratory Experimental Describe Find Cause Populations Relationships and Effect Sequential Clinical Trials. - PowerPoint PPT PresentationTRANSCRIPT
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Chapter 11:Sequential Clinical TrialsDescriptive Exploratory Experimental
Describe Find Cause
Populations Relationships and Effect
Sequential
Clinical Trials
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Sequential Clinical Trials
Experimental designs – Relative efficacy of different treatments
(cause and effect) Problems: 1. Fixed sample size prior 2. All data must be collected prior to analysis
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The Design of a Sequential Trial
Purpose- To compare two treatments: New Treatment (experimental)- A Old Treatment (standard)- B Null hypothesis A = B Explicit operational definitions, target
population, and measurements are established
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Between-Subject Comparison
First eligible patient admitted and is randomly assigned to either treatment A or B
Second eligible patient is admitted and is assigned to the alternate treatment
These two patients form a pair Results of the pair are considered “little
experiment” as we can determine for this pair whether A or B was better
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Within- Subject Comparison
The comparison between A and B can be made on one subject when both treatments are presented to each subject
This approach is only appropriate when there are no carry over effects expected from one treatment to another
Usually, alternate pairs are given the treatments in reverse order, resulting in a crossover design
Crossover design reduces intersubject variability
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Design- continued
The whole experiment is a sequence of these “little experiments” with each pair represents a comparison
The comparison between A and B can be measured in two ways:
Continuous variable- the magnitude of the difference
Nominal (discrete) variable- the preference measured by a subjective, yet, clearly defined criteria indicating that one treatment is more effective than the other
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Sequential Chart
Results of each comparison within a pair of subjects are plotted on a sequential chart showing the cumulative results for all comparisons
After each successive “little experiment” is plotted the researcher stops to consider the results of all pairs completed so far and make one of the following three decisions:
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Decision Making
1. Stop and reject Null hypothesis making a terminal decision to recommend A or B
2. Stop and accept Null hypothesis making a terminal decision that there is no difference between A or B
3. Continue to collect data because the cumulated data are not yet sufficient to draw a conclusion
The process of considering cumulative results after each pair is called “sequential analysis”
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Measuring Preference
Preference is defined on the basis of clinically meaningful differences between two treatments
Specific criteria for preference of one treatment over the other can vary in objectivity
Objective Criteria Death Vs. Survival Cured Vs. Not cured
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Measuring Preference
Subjective Criteria• Subjective evaluation of function• Patient’s general reaction to treatmentMeasuring Preference- Continuous DataCan be reduced to Nominal Data
Treatment A preferred if it:Increases ROM at least 20 degrees more
than Treatment B
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Measuring Preference
Drawback: Difference either 25 or 75 degrees is
considered as difference When the difference is based on
Magnitude, the amount of difference is taken into account
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Measuring Preference
Outcome Treatment
A
Treatment
B
Preference
1.
2.
3.
4.
+
-
+
-
+
-
-
B
None
None
A
B
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Sequential Plans for Evaluating Preference The decision to stop or continue a trail
is based on “little experiments”
Stopping Rules:
1. Upper boundary crossed (U), recommend A
Terminal decision: Accept H1 : A>B
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Sequential Plans for Evaluating Trials
2. Lower boundary crossed (L), recommend B
Terminal decision: Accept H1 B>A3. Middle boundary crossed (M), either
above or bellow the origin, no preferenceTerminal Decision: Accept H0: A=BFigure 11-2
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Effect Size
Preference is described according to the proportion in favor of Treatment A (the experimental treatment)
Under H0- this proportion is 50% for each treatment
Under Hr- this proportion is some value above 50%
If we set effect size at 0.80, we expect at least 80% of preferences to be for A before recommending Treatment A
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Type I Error
The acceptable risk of recommending one treatment over the other when treatment A and B are not different
Type I Error rate is the probability of incorrectly rejecting the Null hypothesis (no difference), and accepting the Research hypothesis
The risk is symbolized by ά (alpha) and is set at .05
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Type I Error
Alpha can designate: a one-tailed test
(ά1) directional research hypothesis
or
a two-tailed test
(ά2) non directional research hypothesis
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Type II Error
The probability of incorrectly accepting Null hypothesis (no difference), when rejecting research hypothesis (there is a difference between A and B), yet the analysis was not able to detect it.
This risk is symbolized by β (beta) and is set between 0.05 and 0.20
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Power
The probability that a statistical test will be able to detect a true difference between A and B.
Power is equal to 1-β If β= .05; power = .95 This means that there will be 95% chance
that an outer boundary will be correctly crossed
Figure 11-2, page 207, 208
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Limitations of Sequential Designs The analysis is limited to two treatments No opportunity to explore multiple
effects or interaction effects No opportunity to control for extraneous
variables Treatment of ties Conditional decision vs. terminal
decision (based on boundary crossing)
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Sequential Clinical Trials
Now you know all about sequential clinical trials!!!!