Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “hypothesis testing”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,207 records · Page 67Linked to original sources
PubMed2005

A tree-based model for homogeneous groupings of multinomials.

The motivation of this paper is to provide a tree-based method for grouping multinomial data according to their classification probability vectors. We produce an initial tree by binary recursive partitioning whereby multinomials are successively split into two subsets and the splits are determined by maximizing the likelihood function. If the number of multinomials k is too large, we propose to order the multinomials, and then build the initial tree based on a dramatically smaller number k-1 of possible splits. The tree is then pruned from the bottom up. The pruning process involves a sequence of hypothesis tests of a single homogeneous group against the alternative that there are two distinct, internally homogeneous groups. As pruning criteria, the Bayesian information criterion and the Wilcoxon rank-sum test are proposed. The tree-based model is illustrated on genetic sequence data. Homogeneous groupings of genetic sequences present new opportunities to understand and align these sequences.

Animals↗
PubMed2006

A Bayesian semi-parametric model for colorectal cancer incidences.

A Bayesian semi-parametric model is proposed to capture the interaction among demographic effects (age and gender), spatial effects (county) and temporal effects of colorectal cancer incidences simultaneously. In particular, an extension of multivariate conditionally autoregressive (CAR) processes to a partially informative Gaussian demographic spatial temporal CAR (DSTCAR) process for a spatial-temporal setting is proposed. The precision matrix of the Gaussian DSTCAR process is the Kronecker product of several components. The spatial component is modelled with a CAR prior. A pth order intrinsic autoregressive prior (IAR(p)) is implemented for the temporal component to estimate a smoothed and non-parametric temporal trend. The demographic component is modelled with a Wishart prior. Data analysis shows significant spatial correlation only exists in the age group of 50-59. Males and females in their 50s and 60s show fairly strong correlation. The hypothesis testing based on Bayes factor suggests that gender correlation cannot be ignored in this model.

Adult↗
PubMed2006

Multiple imputation methods for modelling relative survival data.

In population-based cancer survival studies, the cause-specific survival measures the net survival (excess mortality) due to cancer when the cause of death information is available and reliable. In contrast, when the cause of death is uncertain or unavailable, relative survival, the ratio of the survival rate due to all causes to the expected survival rate, is more appropriate. There is a large body of work on the modelling and hypothesis testing of cause-specific survival, but many of these methods are not directly applicable to relative survival. In this paper, we extend the multiple imputation (MI) methods (Stat. Methods Med. Res. 1999; 8:3-15) to the case of relative survival data. The MI methodology is combined with relative survival to estimate the net survival by changing relative survival data to cause-specific data. This facilitates the direct application to statistical methods developed for the cause-specific survival to the special situation of relative survival. The parameter estimates and the log-rank statistics are obtained by combining the results from multiple imputed cause-specific data. The likelihood-based methods for modelling relative survival data have been implemented by a Windows application called CANSURV (Comput. Meth. Prog. Biomed 2005). Although these methods produce accurate parameter estimates, the choice for models and diagnostic tools is limited. The MI method is presented as a simpler alternative. The relative survival data for the colorectal cancer patients from Surveillance, Epidemiology, and End Results (SEER) program (SEER Cancer Statistics Review, 1973-1999. National Cancer Institute: Bethesda, 2002) is used as an illustration. The results are compared with those obtained from the likelihood-based relative survival analysis methods. A sample SAS macro for the MI method is provided.

Black People↗
PubMed2006

A comparison of propensity score methods: a case-study estimating the effectiveness of post-AMI statin use.

There is an increasing interest in the use of propensity score methods to estimate causal effects in observational studies. However, recent systematic reviews have demonstrated that propensity score methods are inconsistently used and frequently poorly applied in the medical literature. In this study, we compared the following propensity score methods for estimating the reduction in all-cause mortality due to statin therapy for patients hospitalized with acute myocardial infarction: propensity-score matching, stratification using the propensity score, covariate adjustment using the propensity score, and weighting using the propensity score. We used propensity score methods to estimate both adjusted treated effects and the absolute and relative risk reduction in all-cause mortality. We also examined the use of statistical hypothesis testing, standardized differences, box plots, non-parametric density estimates, and quantile-quantile plots to assess residual confounding that remained after stratification or matching on the propensity score. Estimates of the absolute reduction in 3-year mortality ranged from 2.1 to 4.5 per cent, while estimates of the relative risk reduction ranged from 13.3 to 17.0 per cent. Adjusted estimates of the reduction in the odds of 3-year death varied from 15 to 24 per cent across the different propensity score methods.

Acute Disease↗
PubMed2006

Bayesian analysis of the differences of count data.

Paired count data usually arise in medicine when before and after treatment measurements are considered. In the present paper we assume that the correlated paired count data follow a bivariate Poisson distribution in order to derive the distribution of their difference. The derived distribution is shown to be the same as the one derived for the difference of the independent Poisson variables, thus recasting interest on the distribution introduced by Skellam. Using this distribution we remove correlation, which naturally exists in paired data, and we improve the quality of our inference by using exact distributions instead of normal approximations. The zero-inflated version is considered to account for an excess of zero counts. Bayesian estimation and hypothesis testing for the models considered are discussed. An example from dental epidemiology is used to illustrate the proposed methodology.

Algorithms↗
PubMed2006

A framework to monitor environment-induced major genes for developmental trajectories: implication for a prenatal cocaine exposure study.

Whether there are specific genes involved in response to different environmental agents and how such genes regulate developmental trajectories during lifetime are of fundamental importance in health, clinical and pharmaceutical research. In this article, we present a novel statistical model for monitoring environment-induced genes of major effects on longitudinal outcomes of a trait. This model is derived within the maximum likelihood framework, incorporated by mathematical aspects of growth and developmental processes. A typical structural model is implemented to approximate time-dependent covariance matrices for the longitudinal trait. This model allows for a number of biologically meaningful hypothesis tests regarding the effects of major genes on overall growth trajectories or particular stages of development. It can be used to test whether and how major genetic effects are expressed differently under altered environmental agents. In a well-designed case-control study, our model has been employed to detect cocaine-dependent genes that affect growth trajectories for head circumference during childhood. The detected gene triggers significant effects on growth curves in both cocaine-exposed (case) and unexposed groups (control), but with different extents. Significant genotype-environment interactions due to this so-called environment-sensitive gene are promising for further studies toward its genomic mapping using polymorphic molecular markers.

Adult↗
PubMed2007

Non-parametric estimation for baseline hazards function and covariate effects with time-dependent covariates.

Often in many biomedical and epidemiologic studies, estimating hazards function is of interest. The Breslow's estimator is commonly used for estimating the integrated baseline hazard, but this estimator requires the functional form of covariate effects to be correctly specified. It is generally difficult to identify the true functional form of covariate effects in the presence of time-dependent covariates. To provide a complementary method to the traditional proportional hazard model, we propose a tree-type method which enables simultaneously estimating both baseline hazards function and the effects of time-dependent covariates. Our interest will be focused on exploring the potential data structures rather than formal hypothesis testing. The proposed method approximates the baseline hazards and covariate effects with step-functions. The jump points in time and in covariate space are searched via an algorithm based on the improvement of the full log-likelihood function. In contrast to most other estimating methods, the proposed method estimates the hazards function rather than integrated hazards. The method is applied to model the risk of withdrawal in a clinical trial that evaluates the anti-depression treatment in preventing the development of clinical depression. Finally, the performance of the method is evaluated by several simulation studies.

Algorithms↗
PubMed2007

Adaptive design method based on sum of p-values.

Bauer and Kohne proposed an adaptive design using Fisher's combination of independent p-values based on subsamples from different stages (Biometrics 1994; 50(4):1029-1041). Their method provides great flexibility in the selection of statistical methods for hypothesis testing of subsamples. However, the choices for the stopping boundaries are not flexible enough to meet practical needs (Biometrics 2001; 57(3): 886-891). In this paper, an adaptive design method is proposed using linear combination of the independent p-values. The method provides great flexibility in the selection of stopping boundaries and no numerical integration is required for the two-stage designs. The stopping boundaries and p-values can be calculated manually. The operating characteristics of the adaptive designs are studied using computer simulations with and without sample size adjustment. Examples are presented for superiority and non-inferiority trials with different endpoints (normal, binary, and survival) under different adaptations. The statistical efficiency of the proposed method is compared with other methods based on conditional power.

Data Interpretation, Statistical↗
PubMedDate not supplied

Comparative analysis of two rates.

In this paper, we examine comparative analysis of rates with a view to each of the usual comparative parameters-rate difference (RD), rate ratio (RR) and odds ratio (OR)-and with particular reference to first principles. For RD and RR we show the prevailing statistical practices to be rather poor. We stress the need for restricted estimation of variance in the chi-square function underlying interval estimation (and also point estimation and hypothesis testing). For RR analysis we propose a chi-square formulation analogous to that for RD and, thus, one which obviates the present practice of log transformation and its associated use of Taylor series approximation of the variance. As for OR analysis, we emphasize that the chi-square function, introduced by Cornfield for unstratified data, and extended by Gart to the case of stratified analysis, is based on the efficient score and thus embodies its optimality properties. We provide simulation results to evince the better performance of the proposed (parameter-constrained) procedures over the traditional ones.

Biometry↗
PubMedDate not supplied

Interim analyses in clinical trials: classical vs. Bayesian approaches.

This paper concerns interim analysis in clinical trials involving two treatments from the points of view of both classical and Bayesian inference. I criticize classical hypothesis testing in this setting and describe and recommend a Bayesian approach in which sampling stops when the probability that one treatment is the better exceeds a specified value. I consider application to normal sampling analysed in stages and evaluate the gain in average sample number as a function of the number of interim analyses.

Bayes Theorem↗
PubMed1987

The three-decision problem in medical decision making.

Medical researchers and policy makers face decisions that require a choice from among two or more alternatives. Whereas traditional hypothesis tests cannot always serve the needs of the practitioner who needs to make a decision, a problem formulation that assigns losses to various incorrect decisions offers several advantages. With three possible decisions this approach offers a precise representation of the pragmatic and explanatory views of decision making. It enables the investigator to incorporate in the problem specification his attitudes about the seriousness of various errors by guiding him, before he sees the data, to a choice of asymmetric tail probabilities. It also suggests a reformulation of the P-value that can accommodate some of the difficulties practitioners face.

Biometry↗
PubMed1987

An approximation for the distribution of the scan statistic.

The scan statistic evaluates whether an apparent cluster of disease in time is due to chance. The statistic employs a 'moving window' of length w and finds the maximum number of cases revealed through the window as it scans or slides over the entire time period T. Computation of the probability of observing a certain size cluster, under the hypothesis of a uniform distribution, is infeasible when N, the total number of events, is large, and w is of moderate or small size relative to T. We give an approximation that is an asymptotic upper bound, easy to compute, and, for the purposes of hypothesis testing, more accurate than other approximations presented in the literature. The approximation applies both when N is fixed, and when N has a Poisson distribution. We illustrate the procedure on a data set of trisomic spontaneous abortions observed in a two year period in New York City.

Abortion, Spontaneous↗
PubMed1988

Sample size requirements for studies estimating odds ratios or relative risks.

This paper presents formulae for determining the number of subjects necessary, in either a case-control or a cohort study, to estimate the odds ratio or relative risk, respectively, to within a selected percentage (epsilon) of the true population value with some specified probability. This approach differs somewhat from previous comparable work that estimated the log odds ratio within a stated fixed distance rather than as a percentage of the actual odds ratio. Comparable development for relative risk has not previously appeared in the literature. These formulae provide guidelines for determination of study size that does not depend on hypothesis testing considerations.

Epidemiologic Methods↗
PubMed1988

A statistical methodology for postmarketing surveillance of adverse drug reaction reports.

This paper presents a statistically optimal exact hypothesis testing procedure for detecting changes in sales adjusted adverse drug reaction (ADR) rates between historical and current periods, with a computer program that implements this test appended. We provide discussions and illustrations on how to monitor ADR rates for product lines that consist of several pharmacologically equivalent dosage forms.

Computer Simulation↗
PubMed1989

The performance of the two-stage analysis of two-treatment, two-period crossover trials.

In the two-treatment, two-period crossover trial, patients are randomly allocated either to one group that receives treatment A followed by treatment B, or to another group that receives the treatments in the reverse order. Grizzle first proposed a two-stage procedure for analysing the data from such a trial. This paper examines the long-run sampling properties of this procedure, in terms of mean square error of point estimates, coverage probability of confidence intervals and actual significance level of hypothesis tests for the differences between the effects of the two treatments. The advantages of incorporating baseline observations into the analysis are also explored. Because the preliminary test for carryover is highly correlated with the analysis of data from the first period only, actual significance levels are higher than nominal levels even when there is no differential carryover. When carryover is present, the nominal level very seriously understates the actual level, and this becomes even worse when baseline observations are ignored. Increasing sample size only exacerbates the problem since this adverse behaviour then occurs at smaller values of the carryover effect. It is concluded that the two-stage analysis is too potentially misleading to be of practical use.

Analysis of Variance↗
PubMed1991

On the use of historical controls in the analysis of laboratory data.

Our purpose is to propose a method of data analysis incorporating historical controls. It consists of three steps of hypothesis testing procedure. The first step is the check of concurrent controls. The second is the significance testing of treatment groups in comparison with historical controls which are composed of negative and positive controls. The third is the significance testing of a linear trend of the dose-response relationship. The superiority of the proposed procedure is also verified.

Data Interpretation, Statistical↗
PubMed1991

A comparative phase II clinical trials procedure for choosing the best of three treatments.

In some clinical trials one can employ adaptive designs advantageously, although in practice such techniques are rarely used, in part due to their inherent complexity. A simple and practicable decision-theoretic approach for the case of three treatments with binary responses is considered, using equal allocation to remaining treatments and, once eliminated, a treatment cannot be re-employed. Having specified the overall number of patients treated within and beyond the comparative stages of the trial, the goal is to maximize the expected total number of those successfully treated. Investigation of the method involves a computer program that can handle arbitrarily large numbers of patients. It is shown empirically that the decision procedure behaves only marginally worse than if the truly superior treatment had been known and had been given to all patients. Implementation of the method uses a minimax approach that removes dependence on prior parameters. Primarily an identification procedure, one advantage of this approach over traditional hypothesis testing methods is the potential to detect small improvements in treatment efficacy. The intended application is to assist in treatment selection during phase II trials, especially with rapid responses and when the disease involved is serious enough that design-motivating ethical considerations become paramount.

Clinical Trials as Topic↗
PubMed1992

Sample size requirement for repeated measurements in continuous data.

In this paper we extend Bloch's discussion on the usefulness and the limitations in the application of repeated measurements per subject in study designs. We derive general sample size formulae for any finite number of comparison groups to calculate the required number of subjects with repeated measurements, that do not have to be conditionally independent. For fixed total cost, we discuss the optimal sample allocation for repeated measurements needed to maximize the power and the underestimation when using Bloch's sample size formula if in the hypothesis testing procedure the variance parameters are unknown. We have also included a quantitative investigation of the effectiveness of taking repeated measurements per subjects to reduced the required number of subjects for a given power at a given alpha-level.

Analysis of Variance↗