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A Systematic Evaluation of Effect of Adherence Patterns on the Sample Size and Power of a Clinical Study.

ABSTRACT: The objective of our study was to evaluate the effect of adherence patterns on the sample size and power of a clinical trial. Simulations from a population pharmacokinetic/pharmacodynamic (PK/PD) model linked to an adherence model were used. Four types of drug characteristics, such as long (~35 hours) and short (~12 hours) half-life in combination with earlier or delayed time to reach steady-state PD end points were studied. Adherence patterns were simulated using Markov chains. Our results clearly demonstrate the significant impact of varying levels and patterns of nonadherence on the sample size and power of a study. For drugs with short half-lives the evidence to support efficacy could be diluted by various patterns of nonadherence that would make its efficacy indistinguishable from the response to placebo. Prospectively utilizing clinical trial simulations with thorough incorporation of various adherence patterns would provide valuable information when designing a trial.

SUBMITTER: Mallayasamy S

PROVIDER: S-EPMC6310871 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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A Systematic Evaluation of Effect of Adherence Patterns on the Sample Size and Power of a Clinical Study.

Mallayasamy Surulivelrajan S Chaturvedula Ayyappa A Blaschke Terrence T Fossler Michael J MJ

CPT: pharmacometrics & systems pharmacology 20181028 12

The objective of our study was to evaluate the effect of adherence patterns on the sample size and power of a clinical trial. Simulations from a population pharmacokinetic/pharmacodynamic (PK/PD) model linked to an adherence model were used. Four types of drug characteristics, such as long (~35 hours) and short (~12 hours) half-life in combination with earlier or delayed time to reach steady-state PD end points were studied. Adherence patterns were simulated using Markov chains. Our results clea ...[more]

PMID: 30291680

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Project description:BackgroundBefore conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifications, and approaches that are commonly proposed for sample size estimation in microarray experiments. Common methods for sample size estimation are formulated as the minimum sample size necessary to achieve a specified sensitivity (proportion of detected truly differentially expressed genes) on average at a specified false discovery rate (FDR) level and specified expected proportion (pi1) of the true differentially expression genes in the array. Unfortunately, the probability of detecting the specified sensitivity in such a formulation can be low. We formulate the sample size problem as the number of arrays needed to achieve a specified sensitivity with 95% probability at the specified significance level. A permutation method using a small pilot dataset to estimate sample size is proposed. This method accounts for correlation and effect size heterogeneity among genes.ResultsA sample size estimate based on the common formulation, to achieve the desired sensitivity on average, can be calculated using a univariate method without taking the correlation among genes into consideration. This formulation of sample size problem is inadequate because the probability of detecting the specified sensitivity can be lower than 50%. On the other hand, the needed sample size calculated by the proposed permutation method will ensure detecting at least the desired sensitivity with 95% probability. The method is shown to perform well for a real example dataset using a small pilot dataset with 4-6 samples per group.ConclusionsWe recommend that the sample size problem should be formulated to detect a specified proportion of differentially expressed genes with 95% probability. This formulation ensures finding the desired proportion of true positives with high probability. The proposed permutation method takes the correlation structure and effect size heterogeneity into consideration and works well using only a small pilot dataset.

Dataset Information

A Systematic Evaluation of Effect of Adherence Patterns on the Sample Size and Power of a Clinical Study.

Publications

A Systematic Evaluation of Effect of Adherence Patterns on the Sample Size and Power of a Clinical Study.

Similar Datasets

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