identification and estimation of causal effects with outcomes truncated by death it is common in medical studies that the outcome of interest is truncated by death meaning that a subject has died before the outcome could be measured in this case restricted analysis among survivors may be subject to selection bias hence it is of interest to estimate the survivor average causal effect defined as the average causal effect among the subgroup consisting of subjects who would survive under either exposure in this paper we consider the identification and estimation problems of the survivor average causal effect we propose to use a substitution variable in place of the latent membership in the always survivor group the identification conditions required for a substitution variable are conceptually similar to conditions for a conditional instrumental variable and may apply to both randomized and observational studies we show that the survivor average causal effect is identifiable with use of such a substitution variable and propose novel model parameterizations for estimation of the survivor average causal effect under our identification assumptions our approaches are illustrated via simulation studies and a data analysis

the maximal determinant and subdeterminants of 1 matrices abstract in this paper we study the maximal absolute values of determinants and subdeterminants of 1 matrices especially hadamard matrices it is conjectured that the determinants of 1 matrices of order n can have only the values k p where p is specified from an appropriate procedure this conjecture is verified for small values of n the question of what principal minors can occur in a completely pivoted 1 matrix is also studied an algorithm to compute the n j n j minors j 1 2 of hadamard matrices of order n is presented and these minors are determined for j 1 4

informational masking for simultaneous nonspeech stimuli psychometric functions for fixed and randomly mixed maskers sensitivity d and response bias were measured as a function of target level for the detection of a 1000 hz tone in multitone maskers using a one interval two alternative forced choice 1i 2afc paradigm ten such maskers each with eight randomly selected components in the region 200 5000 hz with 800 1250 hz excluded to form a protected zone were presented under two conditions the fixed condition in which the same eight component masker is used throughout an experimental run and the random condition in which an eight component masker is chosen randomly trial to trial from the given set of ten such maskers differences between the results obtained with these two conditions help characterize the listener s susceptibility to informational masking im the d results show great intersubject variability but can be reasonably well fit by simple energy detector models in which internal noise and filter bandwidth are used as fitting parameters in contrast the results are not well fit by these models in addition to presentation of new data and its relation to energy detector models this paper provides comments on a variety of issues problems and research needs in the im area 2005 acoustical society of america doi 10 1121 1 2032748

an algorithm to find formulae and values of minors for hadamard matrices ii abstract we give an algorithm to obtain formulae and values for minors of hadamard matrices one step in our algorithm allows the n j n j minors of a hadamard matrix to be given in terms of the minors of a 2 j 1 2 j 1 matrix in particular we illustrate our algorithm by finding explicitly all the n 4 n 4 minors of a hadamard matrix

an eigenvalue approach evaluating minors for weighing matrices w n n 1 abstract in the present paper we concentrate our study on the evaluation of minors for weighing matrices w n n 1 theoretical proofs concerning their minors up to the order of n 4 n 4 are derived introducing an eigenvalue approach a general theorem specifying the analytical form of any n l n l minor is developed an application to the growth problem for weighing matrices is given

similarity uncertainty and masking in the identification of nonspeech auditory patterns this study examined whether increasing the similarity between informational maskers and signals would increase the amount of masking obtained in a nonspeech pattern identification task the signals were contiguous sequences of pure tone bursts arranged in six narrow band spectro temporal patterns the informational maskers were sequences of multitone bursts played synchronously with the signal tones the listener s task was to identify the patterns in a 1 interval 6 alternative forced choice procedure three types of multitone maskers were generated according to different randomization rules for the least signal like informational masker the components in each multitone burst were chosen at random within the frequency range of 200 6500 hz excluding a protected region around the signal frequencies for the intermediate masker the frequency components in the first burst were chosen quasirandomly but the components in successive bursts were constrained to fall in narrow frequency bands around the frequencies of the components in the initial burst within the narrow bands the frequencies were randomized this masker was considered to be more similar to the signal patterns because it consisted of a set of narrow band sequences any one of which might be mistaken for a signal pattern the most signal like masker was similar to the intermediate masker in that it consisted of a set of synchronously played narrow band sequences but the variation in frequency within each sequence was sinusoidal completing roughly one period in a sequence this masker consisted of discernible patterns but not patterns that were part of the set of signals in addition masking produced by gaussian noise bursts thought to produce primarily peripherally based energetic masking was measured and compared to the informational masking results for the three informational maskers more masking was produced by the maskers comprised of narrow band sequences than for the masker in which the frequencies were not constrained to narrow bands also the slopes of the performance level functions for the three informational maskers were much shallower than for the gaussian noise masker or for no masker the findings provided qualified support for the hypothesis that increasing the similarity between signals and maskers or parts of the maskers causes greater informational masking however it is also possible that the greater masking was a consequence of increasing the number of perceptual streams that had to be evaluated by the listener

semiparametric estimation of treatment effects given base line covariates on an outcome measured after a post randomization event occurs summary we consider estimation from a double blind randomized trial of treatment effect within levels of base line covariates on an outcome that is measured after a post treatment event e has occurred in the subpopulation pe e that would experience event e regardless of treatment specifically we consider estimation of the parameters y indexing models for the outcome mean conditional on treatment and base line covariates in the subpopulation p e such parameters are not identified from randomized trial data but become identified if additionally it is assumed that the subpopulation p of subjects that would experience event e under the second treatment but not under the first s empty and a parametric model for the conditional probability that a subject experiences event e if assigned to the first treatment given that the subject would experience the event if assigned to the second treatment his or her outcome under the second treatment and his or her pretreatment covariates we develop a class of estimating equations whose solutions comprise up to asymptotic equivalence all consistent and asymptotically normal estimators of 7y under these two assumptions in addition we derive a locally semiparametric efficient estimator of y we apply our methods to estimate the effect on mean viral load of vaccine versus placebo after infection with human immunodeficiency virus the event e in a placebo controlled randomized acquired immune deficiency syndrome vaccine trial

adjusting for nonignorable drop out using semiparametric nonresponse models abstract consider a study whose design calls for the study subjects to be followed from enrollment time t 0 to time t t at which point a primary endpoint of interest y is to be measured the design of the study also calls for measurements on a vector v t of covariates to be made at one or more times t during the interval 0 t we are interested in making inferences about the marginal mean 0 of y when some subjects drop out of the study at random times q prior to the common fixed end of follow up time t the purpose of this article is to show how to make inferences about 0 when the continuous drop out time q is modeled semiparametrically and no restrictions are placed on the joint distribution of the outcome and other measured variables in particular we consider two models for the conditional hazard of drop out given v t y where v t denotes the history of the process v t through time t t 0 t in the first model we assume that q t v t y exp 0 y where 0 is a scalar paramet

on the nature and discovery of structure abstract extending principles of experimentation we discuss conditions under which nonexperimental data allow consistent estimation of effects of the kind revealed by experimentation and relevant to decisions we show how implications of these conditions are often overlooked and how failure to distinguish between factors and concomitants makes almost anything said about a model ambiguous if not wrong the effects to be estimated dictate the factors to be included consistency and efficiency determine the concomitants whose effects are not to be estimated concomitants may affect but must not be affected by the factors effects of excluded variables on an included variable may cause inconsistency if the included variable is a factor can only reduce inconsistency if the included variable is a concomitant exclusion of a variable because it is highly correlated with another may sometimes be legitimate if both variables are concomitants never if either is a factor a condition for consistent estimatio

intensity discrimination in the presence of random frequency multicomponent maskers and broadband noise this study examined the effects of multicomponent random frequency maskers and broadband noise maskers on intensity discrimination at 1000 hz maskers and signals were 200 ms presented simultaneously in the first set of conditions thresholds were measured for the detection of a 1000 hz tone in the presence of 40 or 60 db spl random frequency or noise maskers with extensive training of listeners with the random frequency masker to assure stable effects of masker frequency uncertainty the random frequency maskers had two six or ten components chosen at random from a large frequency range 300 3000 hz excluding a 160 hz band around 1000 hz for these maskers performance across the four listeners was very similar showing large effects of masker frequency uncertainty for noise maskers performance matched predictions for energy based masking in the second and third sets of conditions intensity discrimination was measured at 1000 hz for pedestals ranging from 40 to 80 db spl first in isolation and then in the presence of the maskers the pattern of results for intensity discrimination in quiet showed the expected near miss to weber s law but poorer performance than typically observed the addition of broadband noise maskers had little effect on performance however random frequency maskers degraded performance in nearly all conditions with the size of the effect dependent on the level of the pedestal relative to the masker considering the pedestal as a tonal masker the data were fitted with various models of combined masking a simple power law model provided excellent fits with exponents ranging from 0 24 to 0 35 for the multicomponent maskers but 1 0 linear for the noise the results support models positing that the effects of individual maskers undergo nonlinear transformation before they are added independent of the mechanisms which produce these effects because random frequency maskers presumably produce informational uncertainty based masking the nonlinearity in this case appears central rather than peripheral

estimating exposure effects by modelling the expectation of exposure conditional on confounders in order to estimate the causal effects of one or more exposures or treatments on an outcome of interest one has to account for the effect of confounding factors which both covary with the exposures or treatments and are independent predictors of the outcome in this paper we present regression methods which in contrast to standard methods adjust for the confounding effect of multiple continuous or discrete covariates by modelling the conditional expectation of the exposures or treatments given the confounders in the special case of a univariate dichotomous exposure or treatment this conditional expectation is identical to what rosenbaum and rubin have called the propensity score they have also proposed methods to estimate causal effects by modelling the propensity score our methods generalize those of rosenbaum and rubin in several ways first our approach straightforwardly allows for multivariate exposures or treatments each of which may be continuous ordinal or discrete second even in the case of a single dichotomous exposure our approach does not require subclassification or matching on the propensity score so that the potential for residual confounding i e bias due to incomplete matching is avoided third our approach allows a rather general formalization of the idea that it is better to use the estimated propensity score than the true propensity score even when the true score is known the additional power of our approach derives from the fact that we assume the causal effects of the exposures or treatments can be described by the parametric component of a semiparametric regression model to illustrate our methods we reanalyze the effect of current cigarette smoking on the level of forced expiratory volume in one second in a cohort of 2 713 adult white males we compare the results with those obtained using standard methods

a bayesian approach to estimating causal vaccine effects on binary post infection outcomes to estimate causal effects of vaccine on post infection outcomes hudgens and halloran 2006 defined a post infection causal vaccine efficacy estimand vei based on the principal stratification framework they also derived closed forms for the maximum likelihood estimators of the causal estimand under some assumptions extending their research we propose a bayesian approach to estimating the causal vaccine effects on binary post infection outcomes the identifiability of the causal vaccine effect vei is discussed under different assumptions on selection bias the performance of the proposed bayesian method is compared with the maximum likelihood method through simulation studies and two case studies a clinical trial of a rotavirus vaccine candidate and a field study of pertussis vaccination for both case studies the bayesian approach provided similar inference as the frequentist analysis however simulation studies with small sample sizes suggest that the bayesian approach provides smaller bias and shorter confidence interval length copyright 2015 john wiley sons ltd

causal inference without counterfactuals abstract a popular approach to the framing and answering of causal questions relies on the idea of counterfactuals outcomes that would have been observed had the world developed differently for example if the patient had received a different treatment by definition one can never observe such quantities nor assess empirically the validity of any modeling assumptions made about them even though one s conclusions may be sensitive to these assumptions here i argue that for making inference about the likely effects of applied causes counterfactual arguments are unnecessary and potentially misleading an alternative approach based on bayesian decision analysis is presented properties of counterfactuals are relevant to inference about the likely causes of observed effects but close attention then must be given to the nature and context of the query as well as to what conclusions can and cannot be supported empirically in particular even in the absence of statistical uncertainty such inferences m

semiparametric efficiency in multivariate regression models with missing data abstract we consider the efficiency bound for the estimation of the parameters of semiparametric models defined solely by restrictions on the means of a vector of correlated outcomes y when the data on y are missing at random we show that the semiparametric variance bound is the asymptotic variance of the optimal estimator in a class of inverse probability of censoring weighted estimators and that this bound is unchanged if the data are missing completely at random for this case we study the asymptotic performance of the generalized estimating equations gee estimators of mean parameters and show that the optimal gee estimator is inefficient except for special cases the optimal weighted estimator depends on unknown population quantities but for monotone missing data we propose an adaptive estimator whose asymptotic variance can achieve the bound

asymptotic efficiency in estimation with conditional moment restrictions abstract in this paper bounds on asymptotic efficiency are derived for a class of non parametric models the data are independent and identically distributed according to some unknown distribution f there is a given function of the data and a parameter the restrictions are that a conditional expectation of this function is zero at some point in the parameter space this point is to be estimated if f is assumed to be a multinomial distribution with known finite support then the problem becomes parametric and the bound can be obtained from the information matrix this bound turns out to depend only upon certain conditional moments and not upon the support of the distribution since a general f can be approximated by a multinomial distribution the multinomial bound applies to the general case

level dominance in sound source identification impact sounds were synthesized according to standard textbook equations given for the motion of simply supported metal plates in a two interval forced choice procedure highly practiced listeners identified from these sounds a predefined class of target plates based on their particular material and geometric properties the effects of two factors on identification were examined the relative level of partials comprising the sounds and the relative amount of information given as the difference in d each partial provided for identification in different conditions one factor was fixed while the other either increased or decreased with frequency the effect on listener identification in each case was determined from a logistic discriminant analysis of trial by trial responses yielding a vector of listener decision weights on the frequency and decay of individual partials the weights increased proportionally with relative level but were largely uninfluenced by relative information content a result exactly opposite to that expected from a maximum likelihood observer the dominant effect of relative level was replicated for other sound sources clamped bars and stretched membranes and was not diminished by randomizing the relative level of partials across trials the results are taken to underscore the importance of relative level in the identification of rudimentary sound sources

randomization based inference within principal strata in randomized studies treatment comparisons conditional on intermediate postrandomization outcomes using standard analytic methods do not have a causal interpretation an alternative approach entails treatment comparisons within principal strata defined by the potential outcomes for the intermediate outcome that would be observed under each treatment assignment in this article we develop methods for randomization based inference within principal strata we compare our proposed methods with existing large sample methods as well as traditional intent to treat approaches this research is motivated by hiv prevention studies where few infections are expected and inference is desired within the always infected principal stratum that is all individuals who would become infected regardless of randomization assignment

molecular analysis of the effect of relative tone level on multitone pattern discrimination molecular psychophysics attempts to model the observer s response to stimuli as they vary from trial to trial the approach has gained popularity in multitone pattern discrimination studies as a means of estimating the relative reliance or decision weight listeners give to different tones in the pattern various factors affecting decision weights have been examined but one largely ignored is the relative level of tones in the pattern in the present study listeners detected a level increment in a sequence of 5 100 ms 2 0 khz tone bursts alternating in level between 40 and 80db spl the level increment was made largest on the 40 db tones yet despite this all four highly practiced listeners gave near exclusive weight to the 80 db tones the effect was the same when the tones were replaced by bursts of broadband gaussian noise alternating in level it was reduced only when the level differences were made 10db and it was entirely reversed only when the low level tones alternated with louder bursts of ga

rank based principal stratum sensitivity analyses we describe rank based approaches to assess principal stratification treatment effects in studies where the outcome of interest is only well defined in a subgroup selected after randomization our methods are sensitivity analyses in that estimands are identified by fixing a parameter and then we investigate the sensitivity of results by varying this parameter over a range of plausible values we present three rank based test statistics and compare their performance through simulations and provide recommendations we also study three different bootstrap approaches for determining levels of significance finally we apply our methods to two studies an hiv vaccine trial and a prostate cancer prevention trial copyright 2013 john wiley sons ltd

values of minors of some infinite families of matrices constructed from supplementary difference sets and their application to the growth problem abstract we obtain explicit formulae for the values of the 2 v j minors j 0 1 2 of some infinite families of matrices of order 2 v constructed from supplementary difference sets of the form 2 v k 1 k 2 this allows us to obtain information on the growth problem for families of matrices with moderate growth several examples specifying the pivot pattern of these matrices are presented and a conjecture about their pivot structure is posed

consistent estimation of the influence function of locally asymptotically linear estimators on considere des estimateurs qui se comportent asymptotiquement localement comme une moyenne d une fonction prise aux observations cette fonction etant appelee fonction d influence

the quantum algebra of partial hadamard matrices abstract a partial hadamard matrix is a matrix h m m n t whose rows are pairwise orthogonal we associate with each such h a certain quantum semigroup g of quantum partial permutations of 1 m and study the correspondence h g we discuss as well the relation between the completion problems for a given partial hadamard matrix and completion problems for the associated submagic matrix p m m m n c in both cases introducing certain criteria for the existence of the suitable completions