Statistical power considerations in genotype-based recall randomized controlled trials
(2015) BINP32 20142Degree Projects in Bioinformatics
- Abstract
- Validation of gene × environment interaction results from epidemiological studies in randomized clinical trials (RCTs) is usually hampered by insufficient statistical power. Genotype-based recall (GBR) refers to the design of studies in which subgroups of the population are selected based on their genotypes. In this thesis we test if a GBR approach of recruiting two groups of participants with distinct genetic profiles can yield sufficiently higher statistical power with reduced sample sizes, leading to a reduction of experimental costs, compared with conventional RCTs of comparable sample size. To this end, we modelled GBR sampling, with participants chosen from the two extremes of a genetic risk score distribution, and compare this with... (More)
- Validation of gene × environment interaction results from epidemiological studies in randomized clinical trials (RCTs) is usually hampered by insufficient statistical power. Genotype-based recall (GBR) refers to the design of studies in which subgroups of the population are selected based on their genotypes. In this thesis we test if a GBR approach of recruiting two groups of participants with distinct genetic profiles can yield sufficiently higher statistical power with reduced sample sizes, leading to a reduction of experimental costs, compared with conventional RCTs of comparable sample size. To this end, we modelled GBR sampling, with participants chosen from the two extremes of a genetic risk score distribution, and compare this with conventional ”random” sampling. We performed power calculations using simulations in the R program using assumptions from the Diabetes Prevention Program. We have calculated the required sample sizes to reach sufficient statistical power when analyzing the interaction between a genetic factor and Intensive Lifestyle Intervention in a linear regression model, with 1-year small low-density lipoprotein (LDL) particles level as the outcome. Similarly, the statistical power for the interaction effect between a genetic factor and metformin treatment was simulated in a Cox proportional hazards regression model, with time to developing type 2 diabetes as the outcome. Statistical power for interactions in various scenarios including different effect sizes, allele frequencies, initial sampling frames and error rates were also examined for both types of regression model. Results from almost all simulations confirm that GBR is more powerful than conventional when the object is to detect gene × environment interactions in both the linear regression and Cox regression models. Last but not least, an online application for statistical power calculations has also been developed by this author and made available to the community. (Less)
- Popular Abstract
- Genotype-based recall randomized controlled trials
Validation of gene × environment interaction results from epidemiological studies in randomized clinical trials (RCTs) is usually hampered by insufficient statistical power. Genotype-based recall (GBR) refers to the design of studies in which subgroups of the population are selected based on their genotypes. In this thesis we test if a GBR approach of recruiting two groups of participants with distinct genetic profiles can yield sufficiently higher statistical power with reduced sample sizes.
Many of the complex disease are assumed to be as a result of gene-environment interaction. In clinical trials they try to test this hypotheses by examining gene-treatment interaction effect to... (More) - Genotype-based recall randomized controlled trials
Validation of gene × environment interaction results from epidemiological studies in randomized clinical trials (RCTs) is usually hampered by insufficient statistical power. Genotype-based recall (GBR) refers to the design of studies in which subgroups of the population are selected based on their genotypes. In this thesis we test if a GBR approach of recruiting two groups of participants with distinct genetic profiles can yield sufficiently higher statistical power with reduced sample sizes.
Many of the complex disease are assumed to be as a result of gene-environment interaction. In clinical trials they try to test this hypotheses by examining gene-treatment interaction effect to determine whether a genetic profile modifies the response to a clinical intervention or not. Recently a great amount of research has been dedicated to determining the interaction between genetic and environmental factors underling complex diseases.
In RCTs participants are randomly assigned to different active and control groups and they are mainly used to test the efficacy of medical interventions in clinical trials. Designing a clinical trial can cost a lot and researchers have tried to find strategies to reduce the costs. For Instance, enrolling participants with high risk of disease based on clinical as well as genetic risk factors can make a reduction in study size, duration and cost. We hypothesised that recruiting participants with genotypes of interest, an approach named genotype-based recall (GBR) can improve the statistical power.
Simulation-based power calculation
We modelled GBR sampling, with participants chosen from the two extremes of a genetic risk score distribution, which can be developed by summing the number of risk alleles linked to the disease and compare this with conventional ”random” sampling. We performed power calculations using simulations in the R program focusing on two different regression models; multivariable linear regression model with quantitative outcome variables, Cox proportional hazards regression models for time-to-event outcome variables.
We considered different parameters affecting power for gene-environment interaction effect and results from almost all simulations confirm that GBR is more powerful than conventional random sampling in both the linear regression and Cox regression models. An online application for statistical power calculations has also been developed by this author and made available to the community for designing clinical trials in validation of gene × environment interaction results from epidemiological studies.
Advisors: Paul Franks, Ashfaq Ali, Mattias Ohlsson
Degree project 60 credits in Bioinformatics 2015
Department of Biology, Lund University (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/7758171
- author
- Atabaki-Pasdar, Naeimeh
- supervisor
-
- Ashfaq Ali LU
- organization
- course
- BINP32 20142
- year
- 2015
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 7758171
- date added to LUP
- 2015-08-10 10:46:20
- date last changed
- 2015-08-17 08:13:32
@misc{7758171,
abstract = {{Validation of gene × environment interaction results from epidemiological studies in randomized clinical trials (RCTs) is usually hampered by insufficient statistical power. Genotype-based recall (GBR) refers to the design of studies in which subgroups of the population are selected based on their genotypes. In this thesis we test if a GBR approach of recruiting two groups of participants with distinct genetic profiles can yield sufficiently higher statistical power with reduced sample sizes, leading to a reduction of experimental costs, compared with conventional RCTs of comparable sample size. To this end, we modelled GBR sampling, with participants chosen from the two extremes of a genetic risk score distribution, and compare this with conventional ”random” sampling. We performed power calculations using simulations in the R program using assumptions from the Diabetes Prevention Program. We have calculated the required sample sizes to reach sufficient statistical power when analyzing the interaction between a genetic factor and Intensive Lifestyle Intervention in a linear regression model, with 1-year small low-density lipoprotein (LDL) particles level as the outcome. Similarly, the statistical power for the interaction effect between a genetic factor and metformin treatment was simulated in a Cox proportional hazards regression model, with time to developing type 2 diabetes as the outcome. Statistical power for interactions in various scenarios including different effect sizes, allele frequencies, initial sampling frames and error rates were also examined for both types of regression model. Results from almost all simulations confirm that GBR is more powerful than conventional when the object is to detect gene × environment interactions in both the linear regression and Cox regression models. Last but not least, an online application for statistical power calculations has also been developed by this author and made available to the community.}},
author = {{Atabaki-Pasdar, Naeimeh}},
language = {{eng}},
note = {{Student Paper}},
title = {{Statistical power considerations in genotype-based recall randomized controlled trials}},
year = {{2015}},
}