Joint modeling tumor burden and time to event data in oncology trials.

The tumor burden (TB) process is postulated to be the primary mechanism through which most anticancer treatments provide benefit. In phase II oncology trials, the biologic effects of a therapeutic agent are often analyzed using conventional endpoints for best response, such as objective response rate and progression-free survival, both of which causes loss of information. On the other hand, graphical methods including spider plot and waterfall plot lack any statistical inference when there is more than one treatment arm. Therefore, longitudinal analysis of TB data is well recognized as a better approach for treatment evaluation. However, longitudinal TB process suffers from informative missingness because of progression or death. We propose to analyze the treatment effect on tumor growth kinetics using a joint modeling framework accounting for the informative missing mechanism. Our approach is illustrated by multisetting simulation studies and an application to a nonsmall-cell lung cancer data set. The proposed analyses can be performed in early-phase clinical trials to better characterize treatment effect and thereby inform decision-making.

Bayesian intervals for linkage locations.

Intermediate fine mapping has received considerable attention recently, with the goal of providing statistically precise and valid chromosomal regions for fine mapping following initial identification of broad regions that are linked to a disease. The following classes of methods have been proposed and compared in the literature: (1) LOD-support intervals, (2) generalized estimating equations, (3) bootstrap, and (4) confidence set inference framework. These methods provide confidence intervals either with coverage levels deviating from the nominal confidence levels or that are not fully efficient. Here, we propose a novel Bayesian method for constructing such intervals using affected sibling pair data. The susceptibility gene location is treated as a parameter in this method, with a uniform prior. A Metropolis-Hastings algorithm is implemented to sample from the posterior distribution and highest posterior density intervals of the disease gene locations are constructed. Correct coverage levels are maintained by our method. Both simulation studies and an application to a rheumatoid arthritis dataset demonstrate the improved efficiency of the Bayesian intervals compared with existing methods.

Artificial Intelligence: How is It Changing Medical Sciences and Its Future?

Artificially intelligent computer systems are used extensively in medical sciences. Common applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients. While computer systems often execute tasks more efficiently than humans, more recently, state-of-the-art computer algorithms have achieved accuracies which are at par with human experts in the field of medical sciences. Some speculate that it is only a matter of time before humans are completely replaced in certain roles within the medical sciences. The motivation of this article is to discuss the ways in which artificial intelligence is changing the landscape of medical science and to separate hype from reality.

Haseman-Elston regression in ascertained samples: importance of dependent variable and mean correction factor selection.

OBJECTIVE: One of the first tools for performing linkage analysis, Haseman-Elston regression (HE), has been successfully used to identify linkages to several disease traits. A recent explosion in extensions of HE leaves one faced with the task of choosing a flavor of HE best suited for a given situation. This paper puts this dilemma into perspective and proposes a modification to HE for highly ascertained samples (BLUP-PM). METHODS: Using data simulated for a range of models, we evaluated type I error and power of several dependent variables in HE, including the novel BLUP-PM. RESULTS: When analyzing a continuous trait, even in highly ascertained samples, type I error is stable and approximately nominal across dependent variables. When analyzing binary traits in highly ascertained samples, type I error is elevated and unstable for all except BLUP-PM. Regardless of trait type, the optimally weighted HE regression and BLUP-PM have the greatest power. CONCLUSIONS: Ascertained samples do not always reflect the population from which they are drawn and therefore choice of dependent variable in HE becomes increasingly important. Our results do not reveal a single, universal choice, but offer criteria by which to choose and demonstrate BLUP-PM performs well in most situations.

Local false discovery rate and minimum total error rate approaches to identifying interesting chromosomal regions.

The simultaneous testing of a large number of hypotheses in a genome scan, using individual thresholds for significance, inherently leads to inflated genome-wide false positive rates. There exist various approaches to approximating the correct genomewide p-values under various assumptions, either by way of asymptotics or simulations. We explore a philosophically different criterion, recently proposed in the literature, which controls the false discovery rate. The test statistics are assumed to arise from a mixture of distributions under the null and non-null hypotheses. We fit the mixture distribution using both a nonparametric approach and commingling analysis, and then apply the local false discovery rate to select cut-off points for regions to be declared interesting. Another criterion, the minimum total error, is also explored. Both criteria seem to be sensible alternatives to controlling the classical type I and type II error rates.

Dasatinib 100 mg once daily minimizes the occurrence of pleural effusion in patients with chronic myeloid leukemia in chronic phase and efficacy is unaffected in patients who develop pleural effusion.

BACKGROUND: Dasatinib, a highly potent BCR-ABL inhibitor, is an effective treatment for patients with chronic myeloid leukemia in chronic phase (CML CP) after resistance, suboptimal response, or intolerance to prior imatinib. In a phase 3 dose optimization trial in patients with CML CP (CA180-034), the occurrence of pleural effusion was significantly minimized with dasatinib 100 mg once daily (QD) compared with other treatment arms (70 mg twice daily [twice daily], 140 mg QD, or 50 mg twice daily). METHODS: To investigate the occurrence and management of pleural effusion during dasatinib treatment, and efficacy in patients with or without pleural effusion, data from CA180-034 were analyzed. RESULTS: With 24-month minimum follow-up, 14% of patients treated with dasatinib 100 mg QD incurred pleural effusion (grade 3: 2%; grade 4: 0%) compared with 23% to 26% in other study arms. The pleural effusion rate showed only a minimal increment from 12 to 24 months. In the 100 mg QD study arm, median time to pleural effusion (any grade) was 315 days, and after pleural effusion, 52% of patients had a transient dose interruption, 35% had a dose reduction, 57% received a diuretic, and 26% received a corticosteroid. Three patients in the 100 mg QD study arm discontinued treatment after pleural effusion. Across all study arms, patients with or without pleural effusion demonstrated similar progression-free and overall survival, and cytogenetic response rates were higher in patients with a pleural effusion. CONCLUSIONS: Pleural effusion is minimized with dasatinib 100 mg QD dosing and its occurrence does not affect short- or long-term efficacy.

Efficient intermediate fine mapping: confidence set inference with likelihood ratio test statistic.

In positional cloning of disease causing genes, identification of a linked chromosomal region via linkage studies is often followed by fine mapping via association studies. Efficiency can be gained with an intermediate step where confidence regions for the locations of disease genes are constructed. The confidence set inference [CSI; Papachristou and Lin, 2006b] achieves this goal by replacing the traditional null hypothesis of no linkage with a new set of null hypotheses where the chromosomal position under consideration is in tight linkage with a trait locus. This approach was shown to perform favorably compared with several competing methods. Using the duality of confidence sets and hypothesis testing, CSI was proposed for the Mean test statistics with affected sibling pair data (CSI-Mean). We postulate that more efficient confidence sets will result if more efficient test statistics are used in the CSI framework. One promising candidate, the maximum LOD score (MLS) statistic, makes maximum use of available identity by descent information, in addition to handling markers with incomplete polymorphism naturally. We propose a procedure that tests the CSI null hypotheses using the MLS statistic (CSI-MLS). Compared with CSI-Mean, CSI-MLS provides tighter confidence regions over a range of single and two-locus disease models. The MLS test is also shown to be more powerful than the Mean test in testing the CSI null over a wide range of disease models, the advantage being most pronounced for recessive models. In addition, CSI-MLS is computationally much more efficient than CSI-Mean.

Two-step intermediate fine mapping with likelihood ratio test statistics: applications to Problems 2 and 3 data of GAW15.

Construction of precise confidence sets of disease gene locations after initial identification of linked regions can improve the efficiency of the ensuing fine mapping effort. We took the confidence set inference, a framework proposed and implemented using the Mean test statistic (CSI-Mean) and improved the efficiency substantially by using a likelihood ratio test statistic (CSI-MLS). The CSI framework requires knowledge of some disease-model-related parameters. In the absence of prior knowledge of these parameters, a two-step procedure may be employed: 1) the parameters are estimated using a coarse map of markers; 2) CSI-Mean or CSI-MLS are applied to construct the confidence sets of the disease gene locations using a finer map of markers, assuming the estimates from Step 1 for the required parameters. In this article we show that the advantages of CSI-MLS over CSI-Mean, previously demonstrated when the required parameters are known, are preserved in this two-step procedure, using both the simulated and real data contributed to Problems 2 and 3 of Genetic Analysis Workshop 15. In addition, our result suggests that microsatellite data, when available, should be used in Step 1. Also explored in detail is the effect of the absence of parental genotypes on the performance of CSI-MLS.

Fine-scale linkage disequilibrium mapping: a comparison of coalescent-based and haplotype-clustering-based methods.

Among the various linkage-disequilibrium (LD) fine-mapping methods, two broad classes have received considerable development recently: those based on coalescent theory and those based on haplotype clustering. Using Genetic Analysis Workshop 15 Problem 3 simulated data, the ability of these two classes to localize the causal variation were compared. Our results suggest that a haplotype-clustering-based approach performs favorably, while at the same time requires much less computing than coalescent-based approaches. Further, we observe that 1) when marker density is low, a set of equally spaced single-nucleotide polymorphisms (SNPs) provides better localization than a set of tagging SNPs of equal number; 2) denser sets of SNPs generally lead to better localization, but the benefit diminishes beyond a certain density; 3) larger sample size may do more harm than good when poor selection of markers results in biased LD patterns around the disease locus. These results are explained by how well the set of selected markers jointly approximates the expected LD pattern around a disease locus.

Multiple testing in the genomics era: findings from Genetic Analysis Workshop 15, Group 15.

Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type II error rates remains. Genetic Analysis Workshop 15 datasets provided a unique opportunity for participants to evaluate multiple testing strategies applicable to microarray and single nucleotide polymorphism data. The Genetic Analysis Workshop 15 multiple testing and false discovery rate group (Group 15) investigated three general categories for multiple testing corrections, which are summarized in this review: statistical independence, error rate adjustment, and data reduction. We show that while each approach may have certain advantages, adequate error control is largely dependent upon the question under consideration and often requires the use of multiple analytic strategies.

Multistage designs in the genomic era: providing balance in complex disease studies.

In this summary paper, we describe the contributions included in the Multistage Design group (Group 14) at the Genetic Analysis Workshop 15, which was held during November 12-14, 2006. Our group contrasted and compared different approaches to reducing complexity in a genetic study through implementation of staged designs. Most groups used the simulated dataset (problem 3), which provided ample opportunities for evaluating various staged designs. A wide range of multistage designs that targeted different aspects of complexity were explored. We categorized these approaches as reducing phenotypic complexity, model complexity, analytic complexity or genetic complexity. In general we learned that: (1) when staged designs are carefully planned and implemented, the power loss compared to a single-stage analysis can be minimized and study cost is greatly reduced; (2) a joint analysis of the results from each stage is generally more powerful than treating the second stage as a replication analysis.

The affected-/discordant-sib-pair design can guarantee validity of multipoint model-free linkage analysis of incomplete pedigrees when there is marker-marker disequilibrium.

Genomewide linkage studies are tending toward the use of single-nucleotide polymorphisms (SNPs) as the markers of choice. However, linkage disequilibrium (LD) between tightly linked SNPs violates the fundamental assumption of linkage equilibrium (LE) between markers that underlies most multipoint calculation algorithms currently available, and this leads to inflated affected-relative-pair allele-sharing statistics when founders' multilocus genotypes are unknown. In this study, we investigate the impact that the degree of LD, marker allele frequency, and association type have on estimating the probabilities of sharing alleles identical by descent in multipoint calculations and hence on type I error rates of different sib-pair linkage approaches that assume LE. We show that marker-marker LD does not inflate type I error rates of affected sib pair (ASP) statistics in the whole parameter space, and that, in any case, discordant sib pairs (DSPs) can be used to control for marker-marker LD in ASPs. We advocate the ASP/DSP design with appropriate sib-pair statistics that test the difference in allele sharing between ASPs and DSPs.

Genetic characterization and fine mapping of susceptibility loci for sarcoidosis in African Americans on chromosome 5.

Sarcoidosis, a systemic granulomatous disease, likely results from both environmental agents and genetic susceptibility. Sarcoidosis is more prevalent in women and, in the United States, African Americans are both more commonly and more severely affected than Caucasians. We report a follow up of the first genome scan for sarcoidosis susceptibility genes in African Americans. Both the genome scan and the present study comprise 229 African American nuclear families ascertained through two or more sibs with sarcoidosis. Regions studied included those which reached a significance in the genome scan of 0.01 (2p25, 5q11, 5q35, 9q34, 11p15 and 20q13), 0.05 (3p25 and 5p15-13) or which replicated previous findings (3p14-11). We performed genotyping with additional markers in the same families used in the genome scan. We examined multi-locus models for epistasis and performed model-based linkage analysis on subsets of the most linked families to characterize the underlying genetic model. The strongest signal was at marker D5S407 (P=0.005) on 5q11.2, using both full and half sibling pairs. Our results support, in an African American population, a sarcoidosis susceptibility gene on chromosome 5q11.2, and a gene protective for sarcoidosis on 5p15.2. These fine mapping results further prioritize the importance of candidate regions on chromosomes 2p25, 3p25, 5q35, 9q34, 11p15 and 20q13 for African Americans. Additionally, our results suggest joint action of the effects of putative genes on chromosome 3p14-11 and 5p15.2. We conclude that multiple susceptibility loci for sarcoidosis exist in African Americans and that some may have interdependent effects on disease pathogenesis.