Associating somatic mutation with clinical outcomes through kernel regression and optimal transport

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analysis is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess their joint association with clinical outcomes. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene–gene similarities defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least five cancer types, where somatic mutations are associated with overall survival, progression-free interval, or cytolytic activity.     

Elastic priors to dynamically borrow information from historical data in clinical trials

Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.     

Hatching plasticity in a Southeast Asian tree frog mitigates submergence-induced mortality

Environmentally cued hatching has been well-documented in amphibians in response to a wide range of abiotic and biotic factors. The hatching of terrestrial amphibian eggs in response to flooding may be basal within the group, but amphibian lineages in tropical Asia and sub-Saharan Africa have not received as much attention as their Neotropical counterparts. We investigated submergence-induced hatching in Feihyla hansenae, a Rhacophorid tree frog with terrestrial eggs. We quantified natural rates of clutch submergence at our study site in Thailand. Using submergence experiments, we found that embryos are capable of hatching early to escape flooding, and that failure to hatch results in mortality. Among the embryos that were able to hatch early, only the earliest, youngest hatchlings experienced a trade-off in body size that persisted for 6 days, while later, older hatchlings were not significantly smaller than spontaneous hatchlings under control conditions. By incorporating our natural and experimental data into Monte Carlo methods to simulate and compare survival probabilities with and without hatching plasticity, we found an overall 3.1% increase in submergence survival due to hatching plasticity. Our findings support the idea that flooding-induced hatching is widespread across amphibians with terrestrial eggs and highlight the importance of researching understudied tropical regions. As climate change is projected to affect rainfall patterns, the ability of embryos to escape abiotic egg-stage threats may be an indicator of species' ability to flexibly navigate a changing environment.     

Canopy tree mortality depends on the proportion of crown exposed to sunlight,but this effect varies with species' wood density

Understanding what drives changes in tree mortality as well as the covariates influencing trees' response is a research priority to predict forest responses to global change. Here, we combined drone photogrammetry and ground-based data to assess the influence of crown exposure to light (relative to total crown area), growth deviations (relative to conspecifics), tree size, and species' wood density (as a surrogate for light-demanding and shade-tolerant life-history strategies) on the mortality of 984 canopy trees in an Amazon terra firme forest. Trees with lower wood density were less prone to die when their proportion of crown was more exposed to sunlight, but this relationship with relative crown exposure weakened and slightly reversed as wood density increased. Trees growing less than their species average had higher mortality, especially when the species' wood density decreased. The role of wood density in determining the survival of canopy trees under varying light conditions indicates differential responses of light-demanding versus shade-tolerant species. Our results highlight the importance of accounting for life-history strategies, via plant functional types, in vegetation dynamic models aiming to predict forest demography under a rapidly changing climate. Abstract in Spanish is available with online material.     

SURVIVAL RATES FOR THE HAWAIIAN MONK SEAL (MONACHUS SCHAUINSLANDI)

Abstract: Endangered Hawaiian monk seal ( Monachs schauinslandi ) pups at all the major breeding islands in the Northwestern Hawaiian Islands have been tagged since the early 1980s. Pups were double flipper tagged as soon as possible post-weaning. With few exceptions, an extensive tag resighting effort was conducted annually at the same islands. These resighting data were used to estimate seal survival rates from the time of tagging to age one at all locations using the ratio of seals alive in the second year to number of pups tagged. These survival rates among the islands, from weaning to age one, averaged over the years of the study, ranged from 0.80 to 0.90. For young seals over age one, capture-recapture methods were used to calculate survival pooled through several years, and these rates ranged from 0.85 to 0.98. At French Frigate Shoals and Laysan Island, the higher numbers of tagged pups allowed separate estimates of male and female survival to be calculated. These rates suggested that survival of immature females was better than males. Beginning in 1989, survival of immature seals at French Frigate Shoals declined sharply.     

Piping plover chick ecology following landscape-level disturbance

Population declines of disturbance-dependent species due to suppression of natural disturbances are realized across ecosystems. The piping plover (Charadrius melodus; plover), a disturbance-dependent and conservation-reliant shorebird that nests on sandy beaches and barrier islands on the Atlantic Coast, was listed under the United States Endangered Species Act in 1986. In 2012, Hurricane Sandy landed on Fire and Westhampton islands, barrier island nesting sites for plovers in New York, USA. Hurricane Sandy was a natural disturbance in this system, creating abundant nesting habitat. The number of chicks produced by a pair, or a population, is a direct measure of reproductive output, and gaining a better understanding of productivity and chick behavior following large-scale habitat creation may improve plover habitat management and potentially species persistence. We evaluated the effects of landscape features on habitat selection, behavior, and survival of plover broods using logistic regression, generalized linear mixed effects models, and survival models. Plover broods selected flatter sites with less dense vegetation than available at random. Chick foraging rates were highest in moist substrates and were lower in areas of higher nesting plover density. Chick survival was greater for broods that hatched earlier in the breeding season and increased as chicks aged. Generally, providing access to sites with flatter, moist substrates will likely result in higher quality brood rearing habitat on the landscape. Ultimately, vegetation removal and habitat management may be needed to reduce plover nesting density and ensure sufficient habitat, which may in turn improve plover chick survival. Moreover, sustaining natural landscape disturbances such as those resulting from storms, and not taking actions to prevent hurricane-created overwash, will allow these landscape features to persist.     

Individual risk prediction: Comparing random forests with Cox proportional-hazards model by a simulation study

With big data becoming widely available in healthcare, machine learning algorithms such as random forest (RF) that ignores time-to-event information and random survival forest (RSF) that handles right-censored data are used for individual risk prediction alternatively to the Cox proportional hazards (Cox-PH) model. We aimed to systematically compare RF and RSF with Cox-PH. RSF with three split criteria [log-rank (RSF-LR), log-rank score (RSF-LRS), maximally selected rank statistics (RSF-MSR)]; RF, Cox-PH, and Cox-PH with splines (Cox-S) were evaluated through a simulation study based on real data. One hundred eighty scenarios were investigated assuming different associations between the predictors and the outcome (linear/linear and interactions/nonlinear/nonlinear and interactions), training sample sizes (500/1000/5000), censoring rates (50%/75%/93%), hazard functions (increasing/decreasing/constant), and number of predictors (seven, 15 including noise variables). Methods' performance was evaluated with time-dependent area under curve and integrated Brier score. In all scenarios, RF had the worst performance. In scenarios with a low number of events (⩽70), Cox-PH was at least noninferior to RSF, whereas under linearity assumption it outperformed RSF. Under the presence of interactions, RSF performed better than Cox-PH as the number of events increased whereas Cox-S reached at least similar performance with RSF under nonlinear effects. RSF-LRS performed slightly worse than RSF-LR and RSF-MSR when including noise variables and interaction effects. When applied to real data, models incorporating survival time performed better. Although RSF algorithms are a promising alternative to conventional Cox-PH as data complexity increases, they require a higher number of events for training. In time-to-event analysis, algorithms that consider survival time should be used.     

Bias-reduced estimators of conditional odds ratios in matched case-control studies with unmatched confounding

We study bias-reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias-reduced conditional (or unconditional) odds ratios in matched case-control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias-reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one-step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case-control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias-reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes.     

A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching

Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then estimate propensity scores on each of the completed data sets, and use these to estimate treatment effects. However, there has been relatively little attention on developing imputation models to deal with the additional problem of missing treatment indicators, perhaps due to the consequences of generating implausible imputations. However, simply ignoring the missing treatment values, akin to a complete case analysis, could also lead to problems when estimating treatment effects. We propose a latent class model to multiply impute missing treatment indicators. We illustrate its performance through simulations and with data taken from a study on determinants of children's cognitive development. This approach is seen to obtain treatment effect estimates closer to the true treatment effect than when employing conventional imputation procedures as well as compared to a complete case analysis.     

Some new results on Cox–Czanner divergence and their applications in survival studies

In the present communication, we propose a quantile-based measure for the divergence between two survival functions. This can also be used in a dynamic way where the divergence between survival functions varies with time. Several new properties of the proposed measure are investigated with suitable examples. The behavior of the measure for various reliability models is also investigated. A real data analysis is employed to compare the relative efficacy of two treatment groups using the proposed divergence measure.