Dataset Information

Optimal design strategies for sibling studies with binary exposures.

ABSTRACT: Sibling studies have become increasingly popular because they provide better control over confounding by unmeasured family-level risk factors than can be obtained in standard cohort studies. However, little attention has been devoted to the development of efficient design strategies for sibling studies in terms of optimizing power. We here address this issue in commonly encountered types of sibling studies, allowing for continuous and binary outcomes and varying numbers of exposed and unexposed siblings. For continuous outcomes, we show that in families with sibling pairs, optimal study power is obtained by recruiting discordant (exposed-control) pairs of siblings. More generally, balancing the exposure status within each family as evenly as possible is shown to be optimal. For binary outcomes, we elucidate how the optimal strategy depends on the variation of the binary response; as the within-family correlation increases, the optimal strategy tends toward only recruiting discordant sibling pairs (as in the case of continuous outcomes). R code for obtaining the optimal strategies is included.

SUBMITTER: Li Z

PROVIDER: S-EPMC4412035 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Optimal design strategies for sibling studies with binary exposures.

Li Zhigang Z McKeague Ian W IW Lumey Lambert H LH

The international journal of biostatistics 20140101 2

Sibling studies have become increasingly popular because they provide better control over confounding by unmeasured family-level risk factors than can be obtained in standard cohort studies. However, little attention has been devoted to the development of efficient design strategies for sibling studies in terms of optimizing power. We here address this issue in commonly encountered types of sibling studies, allowing for continuous and binary outcomes and varying numbers of exposed and unexposed ...[more]

PMID: 25153242

Similar Datasets

Project description:BackgroundCharacterization of anti-malarial drug concentration profiles is necessary to optimize dosing, and thereby optimize cure rates and reduce both toxicity and the emergence of resistance. Population pharmacokinetic studies determine the drug concentration time profiles in the target patient populations, including children who have limited sampling options. Currently, population pharmacokinetic studies of anti-malarial drugs are designed based on logistical, financial and ethical constraints, and prior knowledge of the drug concentration time profile. Although these factors are important, the proposed design may be unable to determine the desired pharmacokinetic profile because there was no formal consideration of the complex statistical models used to analyse the drug concentration data.MethodsOptimal design methods incorporate prior knowledge of the pharmacokinetic profile of the drug, the statistical methods used to analyse data from population pharmacokinetic studies, and also the practical constraints of sampling the patient population. The methods determine the statistical efficiency of the design by evaluating the information of the candidate study design prior to the pharmacokinetic study being conducted.ResultsIn a hypothetical population pharmacokinetic study of intravenous artesunate, where the number of patients and blood samples to be assayed was constrained to be 50 and 200 respectively, an evaluation of varying elementary designs using optimal design methods found that the designs with more patients and less samples per patient improved the precision of the pharmacokinetic parameters and inter-patient variability, and the overall statistical efficiency by at least 50%.ConclusionOptimal design methods ensure that the proposed study designs for population pharmacokinetic studies are robust and efficient. It is unethical to continue conducting population pharmacokinetic studies when the sampling schedule may be insufficient to estimate precisely the pharmacokinetic profile.

Dataset Information

Optimal design strategies for sibling studies with binary exposures.

Publications

Optimal design strategies for sibling studies with binary exposures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets