Chapter 11: Sequential Clinical Trials Descriptive Exploratory Experimental Describe Find Cause Populations Relationships and Effect Sequential Clinical Trials

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

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

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

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

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

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:

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 ”

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

Measuring Preference Subjective Criteria Subjective evaluation of function Patient ’ s general reaction to treatment Measuring Preference- Continuous Data Can be reduced to Nominal Data Treatment A preferred if it: Increases ROM at least 20 degrees more than Treatment B

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

Measuring Preference OutcomeTreatment A Treatment B Preference B None A B

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 H 1 : A>B

Sequential Plans for Evaluating Trials 2. Lower boundary crossed (L), recommend B Terminal decision: Accept H1 B>A 3. Middle boundary crossed (M), either above or bellow the origin, no preference Terminal Decision: Accept H0: A=B Figure 11-2

Effect Size Preference is described according to the proportion in favor of Treatment A (the experimental treatment) Under H 0 - this proportion is 50% for each treatment Under H r - 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

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

Type I Error Alpha can designate: a one-tailed test (ά 1 ) directional research hypothesis or a two-tailed test (ά 2 ) non directional research hypothesis

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

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

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)

Sequential Clinical Trials Now you know all about sequential clinical trials!!!!