49,978 research outputs found
Defining and Estimating Intervention Effects for Groups that will Develop an Auxiliary Outcome
It has recently become popular to define treatment effects for subsets of the
target population characterized by variables not observable at the time a
treatment decision is made. Characterizing and estimating such treatment
effects is tricky; the most popular but naive approach inappropriately adjusts
for variables affected by treatment and so is biased. We consider several
appropriate ways to formalize the effects: principal stratification,
stratification on a single potential auxiliary variable, stratification on an
observed auxiliary variable and stratification on expected levels of auxiliary
variables. We then outline identifying assumptions for each type of estimand.
We evaluate the utility of these estimands and estimation procedures for
decision making and understanding causal processes, contrasting them with the
concepts of direct and indirect effects. We motivate our development with
examples from nephrology and cancer screening, and use simulated data and real
data on cancer screening to illustrate the estimation methods.Comment: Published at http://dx.doi.org/10.1214/088342306000000655 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
User's Guide to the ROI Forecasting Calculator: Estimating ROI for Medicaid Quality Improvement Programs
Explains the online tool for state Medicaid agencies, health plans, and stakeholders to assess the cost-savings potential of quality improvement measures. Outlines analytical issues and best practices for each component and how to interpret the results
Thinking outside the box: recent advances in the analysis and presentation of uncertainty in cost-effectiveness studies
As many more clinical trials collect economic information within their study design, so health economics analysts are increasingly working with patient-level data on both costs and effects. In this paper, we review recent advances in the use of statistical methods for economic analysis of information collected alongside clinical trials. In particular, we focus on the handling and presentation of uncertainty, including the importance of estimation rather than hypothesis testing, the use of the net-benefit statistic, and the presentation of cost-effectiveness acceptability curves. We also discuss the appropriate sample size calculations for cost-effectiveness analysis at the design stage of a study. Finally, we outline some of the challenges for future research in this areaāparticularly in relation to the appropriate use of Bayesian methods and methods for analyzing costs that are typically skewed and often incomplete
Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera
This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations
The projected increase of genotyping in the clinic and the rise of large
genomic databases has led to the possibility of using patient medical data to
perform genomewide association studies (GWAS) on a larger scale and at a lower
cost than ever before. Due to privacy concerns, however, access to this data is
limited to a few trusted individuals, greatly reducing its impact on biomedical
research. Privacy preserving methods have been suggested as a way of allowing
more people access to this precious data while protecting patients. In
particular, there has been growing interest in applying the concept of
differential privacy to GWAS results. Unfortunately, previous approaches for
performing differentially private GWAS are based on rather simple statistics
that have some major limitations. In particular, they do not correct for
population stratification, a major issue when dealing with the genetically
diverse populations present in modern GWAS. To address this concern we
introduce a novel computational framework for performing GWAS that tailors
ideas from differential privacy to protect private phenotype information, while
at the same time correcting for population stratification. This framework
allows us to produce privacy preserving GWAS results based on two of the most
commonly used GWAS statistics: EIGENSTRAT and linear mixed model (LMM) based
statistics. We test our differentially private statistics, PrivSTRAT and
PrivLMM, on both simulated and real GWAS datasets and find that they are able
to protect privacy while returning meaningful GWAS results.Comment: To be presented at RECOMB 201
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