k‐FWER Control without p ‐value Adjustment,with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins

Summary We show a novel approach for k‐FWER control which does not involve any correction, but only testing the hypotheses along a (possibly data‐driven) order until a suitable number of p‐values are found above the uncorrected α level. p‐values can arise from any linear model in a parametric or nonparametric setting. The approach is not only very simple and computationally undemanding, but also the data‐driven order enhances power when the sample size is small (and also when k and/or the number of tests is large). We illustrate the method on an original study about gene discovery in multiple sclerosis, in which were involved a small number of couples of twins, discordant by disease. The methods are implemented in an R package (someKfwer ), freely available on CRAN.     

A Mixture‐of‐Genotypes Model for the Distribution of Thermostable Phenol Sulfotransferase Activity

A statistical method for parametric density estimation based upon a mixture‐of‐genotypes model is developed for the thermostable phenol sulfotransferase (SULT1A1) activity which has a putative role in modifying risk for colon and prostate cancer/polyps. The EM algorithm for the general mixture model is modified to accommodate the genetic constraints and is used to estimate genotype frequencies from the distribution of the SULT1A1 phenotype. A parametric bootstrap likelihood ratio test is considered as a testing method for the number of mixing components. The size and power of the test is then investigated and compared with the conventional chi‐squared test. The relative risk associated with genotypes defined by this model is also investigated through the generalized linear model. This analysis revealed that a genotype with the highest mean value of SULT1A1 activity has greater impact on cancer risk than others. This result suggests that the phenotype with a higher SULT1A1 activity might be important in studying the association between the cancer risk and SULT1A1 activity. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Estimation of Linkage and Association from Allele Transmission Data

The TDT provides a hypothesis test for the presence of linkage or association (linkage disequilibrium). However, since the TDT is a single test statistic, it cannot be used to separate association and linkage. The importance of this difficulty, following a significant TDT result, has been recently emphasized by Whittaker , Denham and Morris (2000), who alert the community to the possibility that a significant TDT may result from loose linkage and strong association, or from tight linkage and weak association. To attack this problem we start with the parametric model for family‐based allele transmission data of Sham and Curtis (1995) (or Sham (1998)) and find that the parameters in the model are not always identifiable. So we introduce a reparameterization that resolves the identifiability issues and leads to a valid likelihood ratio (LR) test for linkage. Since the linkage and association parameters are both of interest, we next introduce and apply an integrated likelihood (IL) approach to provide separate point estimates and confidence intervals for these parameters. The estimates are shown to have generally small bias and mean square error, while the confidence intervals have good average length and coverage probabilities. We compare the power of the IL approach for testing linkage and, separately association, with the TDT and LR.     

Sensitivity analysis for causal inference using inverse probability weighting

Evaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting. We propose a general methodology that allows both non‐parametric and parametric analyses, which are driven by two parameters that govern the magnitude of the variation of the multiplicative errors of the propensity score and their correlations with the potential outcomes. We also introduce a specific parametric model that offers a mechanistic view on how the uncontrolled confounding may bias the inference through these parameters. Our method can be readily applied to both binary and continuous outcomes and depends on the covariates only through the propensity score that can be estimated by any parametric or non‐parametric method. We illustrate our method with two medical data sets.     

Density dependence in blowfly populations: experimental evaluation of non‐parametric time‐series modelling

Two approaches for describing density dependence in demographic rates of stage‐structured populations are compared in this study. Time‐series data from laboratory blowfly populations (Lucilia sericata) have been analysed in a separate study, with a statistical modelling approach that incorporated density dependences as unspecified (non‐parametric) functions. In this study, we assessed density‐dependent structures by manipulating densities of larvae and adults in cohorts of blowflies and measuring the demographic rates. We here compare the density‐dependent structures revealed by the cohort experiments with those estimated by the non‐parametric model. This model estimates the demographic rates to have the following density‐dependent structures: (i) larval survival was non‐linearly density‐dependent (a ‘humped’ function), (ii) adult survival was density‐independent, and (iii) reproductive rate decreased with adult density. In the cohort experiments reported here, (i) juvenile survival exhibited a positive density dependence in low densities (facilitation), which became negative at higher densities (competition). Pupal and adult size decreased with initial larval density. (ii) Adult survival was reduced by high initial larval density, but it was independent of adult density. (iii) Reproductive rate was reduced by high initial larval density, and by high adult density in populations of large individuals (from low larval density). Hence, the results from these experiments support the non‐parametric model estimates regarding density‐dependent structures of demographic rates in the blowfly populations. The mean demographic rates, however, were apparently underestimated by the model. We conclude that non‐parametric modelling is a useful first approach for exploratory analysis of ecological time‐series data.     

Estimating hazard ratios in cohort data with missing disease information due to death

In clinical and epidemiological studies information on the primary outcome of interest, that is, the disease status, is usually collected at a limited number of follow‐up visits. The disease status can often only be retrieved retrospectively in individuals who are alive at follow‐up, but will be missing for those who died before. Right‐censoring the death cases at the last visit (ad‐hoc analysis) yields biased hazard ratio estimates of a potential risk factor, and the bias can be substantial and occur in either direction. In this work, we investigate three different approaches that use the same likelihood contributions derived from an illness‐death multistate model in order to more adequately estimate the hazard ratio by including the death cases into the analysis: a parametric approach, a penalized likelihood approach, and an imputation‐based approach. We investigate to which extent these approaches allow for an unbiased regression analysis by evaluating their performance in simulation studies and on a real data example. In doing so, we use the full cohort with complete illness‐death data as reference and artificially induce missing information due to death by setting discrete follow‐up visits. Compared to an ad‐hoc analysis, all considered approaches provide less biased or even unbiased results, depending on the situation studied. In the real data example, the parametric approach is seen to be too restrictive, whereas the imputation‐based approach could almost reconstruct the original event history information.     

A comment on testing for a decline in upper Cretaceous dinosaur diversity

A common problem in ecology is to determine whether the diversity of a biological community has changed over time or in response to a disturbance. This involves testing the null hypothesis that diversity is constant against the alternative hypothesis that it has changed. As the power of this test may be low, Fritsch and Hsu (1999, Biometrics 55, 1300-1305) proposed reversing the null and alternative hypothesis. Here, I return to the original formulation and point out that power can be improved by adopting a parametric model for relative abundances and by testing against an ordered alternative.     

The area between ROC curves,a non-parametric method to evaluate a biomarker for patient treatment selection

Treatment selection markers are generally sought for when the benefit of an innovative treatment in comparison with a reference treatment is considered, and this benefit is suspected to vary according to the characteristics of the patients. Classically, such quantitative markers are detected through testing a marker-by-treatment interaction in a parametric regression model. Most alternative methods rely on modeling the risk of event occurrence in each treatment arm or the benefit of the innovative treatment over the marker values, but with assumptions that may be difficult to verify. Herein, a simple non-parametric approach is proposed to detect and assess the general capacity of a quantitative marker for treatment selection when no overall difference in efficacy could be demonstrated between two treatments in a clinical trial. This graphical method relies on the area between treatment-arm-specific receiver operating characteristic curves (ABC), which reflects the treatment selection capacity of the marker. A simulation study assessed the inference properties of the ABC estimator and compared them with other parametric and non-parametric indicators. The simulations showed that the estimate of the ABC had low bias, power comparable to parametric indicators, and that its confidence interval had a good coverage probability (better than the other non-parametric indicator in some cases). Thus, the ABC is a good alternative to parametric indicators. The ABC method was applied to data of the PETACC-8 trial that investigated FOLFOX4 versus FOLFOX4 + cetuximab in stage III colon adenocarcinoma. It enabled the detection of a treatment selection marker: the DDR2 gene.     

A comparison of tests of the difference in the proportion of patients who are cured.

We compared by simulation the likelihood ratio, Wald, and score tests based on a mixture model similar to that proposed by Farewell (1982, Biometrics 38, 1041-1046), and a simple nonparametric test based on the plateau value of the product-limit estimate, for testing the difference in cured proportions between two groups. The parametric tests obtained their asymptotic properties even in small samples provided that one could assume equal failure rates in the two groups. Otherwise, good agreement with predictions required that essentially all potential failures had been observed. The comparative properties of the parametric tests depended on whether the population survival functions crossed, with the power of the Wald test as good as or better than the others in the common situation when the survival functions do not cross. However, its size was sometimes less than nominal. The score test was often not defined and is therefore of limited value. The product-limit test often performed as well as the parametric tests, and despite being biased in some circumstances, may be a useful alternative to these, especially in small samples when some potential failures have not been observed.     

Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of t‐Statistics,with Applications to False Discovery Rate Estimation in Microarray Data Analysis

Summary Given a large number of t‐statistics, we consider the problem of approximating the distribution of noncentrality parameters (NCPs) by a continuous density. This problem is closely related to the control of false discovery rates (FDR) in massive hypothesis testing applications, e.g., microarray gene expression analysis. Our methodology is similar to, but improves upon, the existing approach by Ruppert, Nettleton, and Hwang (2007, Biometrics, 63, 483–495). We provide parametric, nonparametric, and semiparametric estimators for the distribution of NCPs, as well as estimates of the FDR and local FDR. In the parametric situation, we assume that the NCPs follow a distribution that leads to an analytically available marginal distribution for the test statistics. In the nonparametric situation, we use convex combinations of basis density functions to estimate the density of the NCPs. A sequential quadratic programming procedure is developed to maximize the penalized likelihood. The smoothing parameter is selected with the approximate network information criterion. A semiparametric estimator is also developed to combine both parametric and nonparametric fits. Simulations show that, under a variety of situations, our density estimates are closer to the underlying truth and our FDR estimates are improved compared with alternative methods. Data‐based simulations and the analyses of two microarray datasets are used to evaluate the performance in realistic situations.     

On symmetric semiparametric two-sample problem

We consider a two-sample problem where data come from symmetric distributions. Usual two-sample data with only magnitudes recorded, arising from case-control studies or logistic discriminant analyses, may constitute a symmetric two-sample problem. We propose a semiparametric model such that, in addition to symmetry, the log ratio of two unknown density functions is modeled in a known parametric form. The new semiparametric model, tailor-made for symmetric two-sample data, can also be viewed as a biased sampling model subject to symmetric constraint. A maximum empirical likelihood estimation approach is adopted to estimate the unknown model parameters, and the corresponding profile empirical likelihood ratio test is utilized to perform hypothesis testing regarding the two population distributions. Symmetry, however, comes with irregularity. It is shown that, under the null hypothesis of equal symmetric distributions, the maximum empirical likelihood estimator has degenerate Fisher information, and the test statistic has a mixture of χ²-type asymptotic distribution. Extensive simulation studies have been conducted to demonstrate promising statistical powers under correct and misspecified models. We apply the proposed methods to two real examples.     

A statistical simulation model for field testing of non‐target organisms in environmental risk assessment of genetically modified plants

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non‐target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess‐zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.     

A Bayesian method to estimate the optimal threshold of a longitudinal biomarker

The objective of this study was to develop methods to estimate the optimal threshold of a longitudinal biomarker and its credible interval when the diagnostic test is based on a criterion that reflects a dynamic progression of that biomarker. Two methods are proposed: one parametric and one non‐parametric. In both the cases, the Bayesian inference was used to derive the posterior distribution of the optimal threshold from which an estimate and a credible interval could be obtained. A numerical study shows that the bias of the parametric method is low and the coverage probability of the credible interval close to the nominal value, with a small coverage asymmetry in some cases. This is also true for the non‐parametric method in case of large sample sizes. Both the methods were applied to estimate the optimal prostate‐specific antigen nadir value to diagnose prostate cancer recurrence after a high‐intensity focused ultrasound treatment. The parametric method can also be applied to non‐longitudinal biomarkers.     

Bayesian Semiparametric Frailty Selection in Multivariate Event Time Data

Biomedical studies often collect multivariate event time data from multiple clusters (either subjects or groups) within each of which event times for individuals are correlated and the correlation may vary in different classes. In such survival analyses, heterogeneity among clusters for shared and specific classes can be accommodated by incorporating parametric frailty terms into the model. In this article, we propose a Bayesian approach to relax the parametric distribution assumption for shared and specific‐class frailties by using a Dirichlet process prior while also allowing for the uncertainty of heterogeneity for different classes. Multiple cluster‐specific frailty selections rely on variable selection‐type mixture priors by applying mixtures of point masses at zero and inverse gamma distributions to the variance of log frailties. This selection allows frailties with zero variance to effectively drop out of the model. A reparameterization of log‐frailty terms is performed to reduce the potential bias of fixed effects due to variation of the random distribution and dependence among the parameters resulting in easy interpretation and faster Markov chain Monte Carlo convergence. Simulated data examples and an application to a lung cancer clinical trial are used for illustration.     

An evaluation of new parsimony‐based versus parametric inference methods in biogeography: a case study using the globally distributed plant family Sapindaceae

Aim Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony‐based approaches. Here, we compare a parametric method, dispersal–extinction–cladogenesis (DEC), against a parsimony‐based method, dispersal–vicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes‐DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study. Location World‐wide. Methods Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes‐DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years. Results Despite differences in the underlying biogeographical model, Bayes‐DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events – which in Bayes‐DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node. Main conclusions By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes‐DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide‐spanning spatial and temporal model proposed here could prove useful for testing large‐scale biogeographical patterns in plants.     

A Bayesian Chi‐Squared Goodness‐of‐Fit Test for Censored Data Models

Summary We propose a Bayesian chi‐squared model diagnostic for analysis of data subject to censoring. The test statistic has the form of Pearson's chi‐squared test statistic and is easy to calculate from standard output of Markov chain Monte Carlo algorithms. The key innovation of this diagnostic is that it is based only on observed failure times. Because it does not rely on the imputation of failure times for observations that have been censored, we show that under heavy censoring it can have higher power for detecting model departures than a comparable test based on the complete data. In a simulation study, we show that tests based on this diagnostic exhibit comparable power and better nominal Type I error rates than a commonly used alternative test proposed by Akritas (1988, Journal of the American Statistical Association 83, 222–230). An important advantage of the proposed diagnostic is that it can be applied to a broad class of censored data models, including generalized linear models and other models with nonidentically distributed and nonadditive error structures. We illustrate the proposed model diagnostic for testing the adequacy of two parametric survival models for Space Shuttle main engine failures.     

Performance of likelihood ratio tests of evolutionary hypotheses under inadequate substitution models.

In recent years, likelihood ratio tests (LRTs) based on DNA and protein sequence data have been proposed for testing various evolutionary hypotheses. Because conducting an LRT requires an evolutionary model of nucleotide or amino acid substitution, which is almost always unknown, it becomes important to investigate the robustness of LRTs to violations of assumptions of these evolutionary models. Computer simulation was used to examine performance of LRTs of the molecular clock, transition/transversion bias, and among-site rate variation under different substitution models. The results showed that when correct models are used, LRTs perform quite well even when the DNA sequences are as short as 300 nt. However, LRTs were found to be biased under incorrect models. The extent of bias varies considerably, depending on the hypotheses tested, the substitution models assumed, and the lengths of the sequences used, among other things. A preliminary simulation study also suggests that LRTs based on parametric bootstrapping may be more sensitive to substitution models than are standard LRTs. When an assumed substitution model is grossly wrong and a more realistic model is available, LRTs can often reject the wrong model; thus, the performance of LRTs may be improved by using a more appropriate model. On the other hand, many factors of molecular evolution have not been considered in any substitution models so far built, and the possibility of an influence of this negligence on LRTs is often overlooked. The dependence of LRTs on substitution models calls for caution in interpreting test results and highlights the importance of clarifying the substitution patterns of genes and proteins and building more realistic models.     

Estimating genetic benefits of polyandry from experimental studies: a meta‐analysis

The consequences of polyandry for female fitness are controversial. Sexual conflict studies and a meta‐analysis of mating rates in insects suggest that there is a longevity cost when females mate repeatedly. Even so, compensatory material benefits can elevate egg production and fertility, partly because polyandry ensures an adequate sperm supply. Polyandry can therefore confer direct benefits. The main controversy surrounds genetic benefits. The argument is analogous to that surrounding the evolution of conventional female mate choice, except that with polyandry it is post‐copulatory mechanisms that might bias paternity towards males with higher breeding values for fitness. Recent meta‐analyses of extra‐pair copulations in birds have cast doubt on whether detectable genetic benefits exist. By contrast, another meta‐analysis showed that polyandry elevates egg hatching success (possibly due to a fertilization bias towards sperm with paternal genes that elevate embryo survival) in insects. A detailed summary of whether polyandry elevates other components of offspring performance is lacking. Here we present a comprehensive meta‐analysis of 232 effect sizes from 46 experimental studies. These experiments were specifically designed to try to quantify the potential genetic benefits of polyandry by controlling fully for the number of matings by females assigned to monandry and polyandry treatments. The bias‐corrected 95% confidence intervals for egg hatching success (d = ?0.01 to 0.61), clutch production (d = 0.07 to 0.45) and fertility (d = 0.04 to 0.40) all suggest that polyandry has a beneficial effect (although P values from parametric tests were marginally non‐significant at P = 0.075, 0.052 and 0.058, respectively). Polyandry was not significantly beneficial for any single offspring performance trait (e.g. growth rate, survival, adult size), but the test power was low due to small sample sizes (suggesting that many more studies are still needed). We then calculated a composite effect size that provides an index of general offspring performance. Depending on the model assumptions, the mean effect of polyandry was either significantly positive or marginally non‐significant. A possible role for publication bias is discussed. The magnitude of the reported potential genetic benefits (d = 0.07 to 0.19) are larger than those from two recent meta‐analyses comparing offspring sired by social and extra‐pair mates in birds (d = 0.02 to 0.04). This difference raises the intriguing possibility that cryptic, post‐copulatory female choice might be more likely to generate ‘good gene’ or ‘compatible gene’ benefits than female choice of mates based on the expression of secondary sexual traits.