Robust and Powerful Tests for Rare Variants Using Fisher’s Method ...

This is the peer reviewed version of the following article: ``Derkach, A., Lawless, J.F. and Sun, L. (2013). Robust and powerful tests for rare variants using Fisher's method to combine evidence of association from two or more complementary tests. Genetic Epidemiology, 37 (1), 110--121", which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/gepi.21689/full DOI: 10.1002/gepi.21689. This article may be used for non-commercial purposes in accordance with http://olabout.wiley.com/WileyCDA/Section/id-828039.html. Wiley Terms and Conditions for Self-Archiving.Many association tests have been proposed for rare variants, but the choice of a powerful test is uncertain when there is limited information on the underlying genetic model. Proposed methods use either linear statistics, which are powerful when most variants are causal and have the same direction of effect, or quadratic statistics, which are more powerful in other scenarios. To achieve robustness, it is natural to combine the evidence of association from two or more complementary tests. To this end, we consider the minimum-p and Fisher’s methods of combining P-values from linear and quadratic statistics. Extensive simulation studies show that both methods are robust across models with varying proportions of causal, deleterious, and protective rare variants, allele frequencies, and effect sizes. When the majority (>75%) of the causal effects are in the same direction (deleterious or protective), Fisher’s method consistently outperforms the minimum-p and the individual linear and quadratic tests, as well as the optimal sequence kernel association test, SKAT-O. When the individual test has moderate power, Fisher’s test has improved power for 90% of the 5000 models considered, with >20% relative efficiency gain for 40% of the models. The maximum absolute power loss is 8% for the remaining 10% of the models. An application to the GAW17 quantitative trait Q2 data based on sequence data of the 1000 Genomes Project shows that, compared with linear and quadratic tests, Fisher’s test has comparable power for all 13 functional genes and provides the best power for more than half of them.Natural Sciences and Engineering Research Council of Canada || (JFL RGPIN 8597

Armitage Lecture 2011: The Design and Analysis of Life History Studies

This is the peer reviewed version of the following article: Lawless, J.F. (2013). Armitage Lecture 2011: the design and analysis of life history studies. Statistics in Medicine, 32 (13), 2155--2172, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.5754/full. DOI: 10.1002/sim.5754 This article may be used for non-commercial purposes in accordance with http://olabout.wiley.com/WileyCDA/Section/id-828039.html Wiley Terms and Conditions for Self-ArchivingLife history studies collect information on events and other outcomes during people’s lifetimes. For example, these may be related to childhood development, education, fertility, health, or employment. Such longitudinal studies have constraints on the selection of study members, the duration and frequency of follow-up, and the accuracy and completeness of information obtained. These constraints, along with factors associated with the definition and measurement of certain outcomes, affect our ability to understand, model, and analyze life history processes. My objective here is to discuss and illustrate some issues associated with the design and analysis of life history studies.Natural Sciences and Engineering Research Council of Canada || JFL RGPIN 859

Multistate Models with Intermittent Observation

The final publication: Lawless, J.F. and Nazeri Rad, N. (2015). Estimation and assessment of markov multistate models with intermittent observations on individuals. Lifetime Data Analysis, 21 (2), 160--179 is available at Springer via http://link.springer.com/article/10.1007/s10985-014-9310-z. DOI: 10.1007/s10985-014-9310-zMultistate models provide important methods of analysis for many life history processes, and this is an area where John Klein made numerous contributions. When individuals in a study group are observed continuously so that all transitions between states, and their times, are known, estimation and model checking is fairly straightforward. However, individuals in many studies are observed intermittently, and only the states occupied at the observation times are known. We review methods of estimation and assessment for Markov models in this situation. Numerical studies that show the effects of inter-observation times are provided, and new methods for assessing fit are given. An illustration involving viral load dynamics for HIV-positive persons is presented.Natural Sciences and Engineering Research Council of Canada || (JFL RGPIN 8597

Score Tests for Association Under Response-dependent Sampling Designs for Expensive Covariates

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Biometrika following peer review. The version of record ``Derkach, A., Lawless, J.F. and Sun, L. (2015). Score tests for association under response-dependent sampling designs for expensive covariates. Biometrika, 102 (4), 988--994"is available online at: http://biomet.oxfordjournals.org/content/102/4/988.full.pdf+html}{DOI: 10.1093/biomet/asv038.Response-dependent sampling is widely used in settings where certain variables are expensive to obtain. Estimation has been thoroughly investigated but recent applications have emphasized tests of association for expensive covariates and a response variable. We consider testing and provide easily implemented likelihood score tests for generalized linear models under a broad range of sampling plans. We show that when there are no additional covariates, the score statistics are identical for conditional and full likelihood approaches, and are of the same form as for ordinary random sampling. Applications in genetics are discussed briefly.Natural Sciences and Engineering Research Council of Canada || (JFL RGPIN 8597) Canadian Institutes of Health Research Strategic Training for Advanced Genetic Epidemiology fellowship (Lei Sun

Estimation of Finite Population Duration Distributions

The final publication: ``Hajducek, D.M. and Lawless, J.F. (2013). Estimation of finite population duration distributions from longitudinal survey panels with intermittent followup. Lifetime Data Analysis, 19 (3), 371--392" is available at Springer via http://link.springer.com/article/10.1007/s10985-012-9241-5 DOI: 10.1007/s10985-012-9241-5We consider survival or duration times associated with spells (sojourns in some state) or events experienced by individuals in a population over a specified time period. Duration distributions can be estimated from data recorded during followup of panel members in longitudinal surveys, but adjustments for the sample design, population structure and losses to followup are typically required. We provided weighted Kaplan-Meier estimates that allow for these features and, in particular, adjust for dependent loss to followup through the use of inverse probability of censoring weights.Natural Sciences and Engineering Research Council of Canada || (JFL RGPIN 8597

Statistical Issues in Modeling Chronic Disease in Cohort Studies

The final publication (Cook, R. J., & Lawless, J. F. (2014). Statistical issues in modeling chronic disease in cohort studies. Statistics in Biosciences, 6(1), 127-161. DOI: 10.1007/s12561-013-9087-8) is available at Springer via http://link.springer.com/article/10.1007/s12561-013-9087-8Observational cohort studies of individuals with chronic disease provide information on rates of disease progression, the effect of fixed and time-varying risk factors, and the extent of heterogeneity in the course of disease. Analysis of this information is often facilitated by the use of multistate models with intensity functions governing transition between disease states. We discuss modeling and analysis issues for such models when individuals are observed intermittently. Frameworks for dealing with heterogeneity and measurement error are discussed including random effect models, finite mixture models, and hidden Markov models. Cohorts are often defined by convenience and ways of addressing outcome-dependent sampling or observation of individuals are also discussed. Data on progression of joint damage in psoriatic arthritis and retinopathy in diabetes are analysed to illustrate these issues and related methodology.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887

Discussions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111979/1/j.1751-5823.2011.00145.x.pd

Cumulative processes related to event histories

Costs or benefits which accumulate for individuals over time are of interest in many life history processes. Familiar examples include costs of health care for persons with chronic medical conditions, the payments to insured persons during periods of disability, and quality of life which is sometimes used in the evaluation of treatments in terminally ill patients. For convenience, here we use the term costs to refer to cost or other cumulative measures. Two important scenarios are (i) where costs are associated with the occurrence of certain events, so that total cost accumulates as a step function, and (ii) where individuals may move between various states over time, with cost accumulating at a constant rate determined by the state occupied. In both cases, there is frequently a random variable T that represents the duration of the process generating the costs. Here we consider estimation of the mean cumulative cost over a period of interest using methods based upon marginal features of the cost process and intensity based models. Robustness to adaptive censoring is discussed in the context of the multi-state methods. Data from a quality of life study of breast cancer patients are used to illustrate the methods

Cumulative processes related to event histories

Costs or benefits which accumulate for individuals over time are of interest in many life history processes. Familiar examples include costs of health care for persons with chronic medical conditions, the payments to insured persons during periods of disability, and quality of life which is sometimes used in the evaluation of treatments in terminally ill patients. For convenience, here we use the term costs to refer to cost or other cumulative measures. Two important scenarios are (i) where costs are associated with the occurrence of certain events, so that total cost accumulates as a step function, and (ii) where individuals may move between various states over time, with cost accumulating at a constant rate determined by the state occupied. In both cases, there is frequently a random variable T that represents the duration of the process generating the costs. Here we consider estimation of the mean cumulative cost over a period of interest using methods based upon marginal features of the cost process and intensity based models. Robustness to adaptive censoring is discussed in the context of the multi-state methods. Data from a quality of life study of breast cancer patients are used to illustrate the methods.Peer Reviewe