Multiplicity problem in clinical trials and some statistical approaches

NOV 16, 2019

Research paper

Multiplicity Problem in Clinical Trials and Some of its Statistical Approaches Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics

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INTRODUCTION

01 The multiplicity issue should be considered at the beginning stage i.e. starting from the design, analysis and interpretation of the study.

Researchers often face multiple testing problems in clinical trial that have an impact on type I and type II error rates resulting in invalid inference.

02

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MULTIPLICITY

Inflation of type I error rate from a multiple testing problem is commonly referred as multiplicity.

02 If there exists the problem of multiplicity, then it should be adjusted in the testing problem.

01 Type I error is simply the error rate when rejecting the null hypothesis when it is true and it is referred as significance level of the trial.

03

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NEED TO CONSIDER MULTIPLE TESTING ADJUSTMENTS Suppose a set of hypotheses are tested within the same study simultaneously, then the probability of rejecting at least one null hypothesis (i.e) type I error rate is increased, thereby results in a high risk of finding false positive.

(For example) • Consider a study of five independent null hypotheses, and each are tested simultaneously at 5% level of significance, and the result from the test is that the overall type I error rate is 23%. However, probability of atleast one significant result will be calculated using 1−(1−α)k where k is the number of tests.

• In such situation, adjustments in multiple testing problem is needed otherwise it yield invalid result that 23% chance to get atleast one significant result when the null hypothesis is actually true but we assume that the error rate is maintained at 5% level.

• Also, the false positive findings depends on the sample size of the study.

• Thus, multiplicity adjustment should be necessary in designing, analysing the data and interpreting the results.

Research paper by Li et al (2017) discusses about various multiplicity

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STATISTICAL APPROACHES FOR MULTIPLICITY Non - Parametric Procedures

Non - Hierarchical Hypotheses

Parametric Procedures

Statistical Approaches for Multiplicity

Simple Procedures Hierarchical Hypotheses Gate-keeping Procedures

1. Bonferroni Procedure 2. Simes Procedure 3. Holm Step-Down Procedure 4. Hochberg Step-Up Procedure 5. Hommel Procedure

1. Dunnet Procedure

1. Fixed - Sequence Procedure 2. Fallback Procedure

1. Serial Gate-keeping Procedures 2. Parallel Gate-keeping Procedure 3. Other Extensions of Gatekeeping Procedures

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MULTIPLICITY PROCEDURES Gatekeeping Procedure Used in situation when there are multiple endpoints and are grouped into different families.

Dunnett or stepdown Dunnett procedure Used if there is a prior evidence about the test is more significant.

Fixed Sequence Procedure Stepwise multiple testing procedure that is constructed using a pre-specified sequence of hypotheses. Disadvantage of this procedure is that • Power will be maximized as long as previous hypotheses are rejected, but minimized if a previous hypothesis is not rejected.

• Ordering of multiple hypotheses based on the clinical importance is subjective in nature.

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FALLBACK PROCEDURE

It is similar to a fixed sequence test, in which hypotheses are tested in an a priority order at the full alpha level.

01 02

Full alpha of 0.05 is split for endpoints in a pre-specified order and the hypotheses in late order can still be tested (but with different alpha levels) if the previous hypothesis is not rejected.

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CONCLUSIONS

01

02

Multiplicity problem is not uncommon in the clinical trials if we have one or more treatment in the study.

More appropriate statistical method is necessary to deal with the multiplicity problem which reduces the false positive findings under the assumed null hypothesis.

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