Chapter 11:Sequential Clinical TrialsDescriptive 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 treatmentMeasuring Preference- Continuous DataCan 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

Outcome Treatment

A

Treatment

B

Preference

1.

2.

3.

4.

+

-

+

-

+

-

-

B

None

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 H1 : A>B

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

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

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

(ά1) directional research hypothesis

or

a two-tailed test

(ά2) 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!!!!