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At least 19 recordsLinked to original sources
PubMed1996

Parametric and non-parametric measures in the assessment of knee and hip osteoarthritis: interobserver reliability and correlation with radiology.

The aim of this study was to evaluate the interobserver reliability of parametric and non-parametric variables in the clinical assessment of hip and knee osteoarthritis (OA). Three rheumatologists examined 49 patients with different radiological stages of OA using different assessment tools such as a tape measure, a goniometer, a plurimeter and a hand-held pull gauge. The reliabilities of parametric variables calculated by analysis of variance (ANOVA) showed much higher values than the non-parametric ones calculated by Kendall's tau beta. The highest levels of correlation in hip OA between clinical functional tests and radiological changes were found for hip extension (r = 0.57; P < 0.01) and the Patrick sign (r = 0.54; P < 0.01) while in knee OA the highest correlations were found for knee circumference (r = 0.5; P < 0.01) and knee flexion (r = 0.035; P < 0.02). Knee muscle strength, as measured with a hand-held pull gauge, showed a high level of interobserver agreement (r = 0.79), but correlated poorly with radiological changes. In conclusion parametric variables of joint morphology as knee circumference of parametric variables of function as the Patrick sign should be preferred for assessing secondary endpoints in OA clinical trials.

Aged↗
PubMedDate not supplied

Segmented regression with errors in predictors: semi-parametric and parametric methods.

We consider the estimation of parameters in a particular segmented generalized linear model with additive measurement error in predictors, with a focus on linear and logistic regression. In epidemiologic studies segmented regression models often occur as threshold models, where it is assumed that the exposure has no influence on the response up to a possibly unknown threshold. Furthermore, in occupational and environmental studies the exposure typically cannot be measured exactly. Ignoring this measurement error leads to asymptotically biased estimators of the threshold. It is shown that this asymptotic bias is different from that observed for estimating standard generalized linear model parameters in the presence of measurement error, being both larger and in different directions than expected. In most cases considered the threshold is asymptotically underestimated. Two standard general methods for correcting for this bias are considered; regression calibration and simulation extrapolation (simex). In ordinary logistic and linear regression these procedures behave similarly, but in the threshold segmented regression model they operate quite differently. The regression calibration estimator usually has more bias but less variance than the simex estimator. Regression calibration and simex are typically thought of as functional methods, also known as semi-parametric methods, because they make no assumptions about the distribution of the unobservable covariate X. The contrasting structural, parametric maximum likelihood estimate assumes a parametric distributional form for X. In ordinary linear regression there is typically little difference between structural and functional methods. One of the major, surprising findings of our study is that in threshold regression, the functional and structural methods differ substantially in their performance. In one of our simulations, approximately consistent functional estimates can be as much as 25 times more variable than the maximum likelihood estimate for a properly specified parametric model. Structural (parametric) modelling ought not be a neglected tool in measurement error models. An example involving dust concentration and bronchitis in a mechanical engineering plant in Munich is used to illustrate the results.

Bias↗
PubMed1998

Coverage and precision of confidence intervals for area under the curve using parametric and non-parametric methods in a toxicokinetic experimental design.

PURPOSE: The coverage and precision of parametric Bailer-type confidence intervals (CIs) for area under the curve (AUC) was compared to nonparametric bootstrap confidence intervals. METHODS: Concentration-time data was simulated using Monte Carlo simulation under a toxicokinetic paradigm with sparse (SSC) and dense sampling (DSC) conditions. AUC was calculated using the trapezoidal rule and 95% CIs were computed using various parametric and nonparametric methods. RESULTS: Under SSC, the various parametric CIs contained the true population AUC with coverage probabilities ranging from 0.77 to 0.95 with low inter-subject variation (coefficient of variation (CV) = 15%) and from 0.82 to 0.95 with high inter-subject variation (CV = 50%). The nominal value should be close to 0.95. DSC tended to increase coverage by about 0.05. Bailer's method always produced the lowest coverage of all parametric CIs examined. Under SSC, bootstrap CIs had coverage probabilities ranging from 0.62 (CV = 15%) to 0.68 (CV = 50%). DSC increased coverage to 0.77. Parametric CIs were wider than their nonparametric counterparts, often giving lower CI estimates less than zero. Bailer's method and Bailer's method using the jackknife estimate of the standard error were the worst in this respect. Bootstrap CIs never had lower CI estimates less than zero. However, SSC tends to produce bootstrap distributions that are not continuous which, if used, may produce biased CI estimates. CONCLUSIONS: Bootstrap CI estimates were judged to be the "best". However, the limitations of the bootstrap should be clearly recognized and it should not be used indiscriminately. Examination of the bootstrap distribution for its degree of discreteness must be part of the statistical process.

Area Under Curve↗
PubMed1997

Comparison of parametric and non-parametric survival methods using simulated clinical data.

We derived three parametric survival models (the log-normal, log logit, and Weibull) from the clinical data of chemotherapy trials for stage II breast cancer. We then used these models to generate simulated survival data, which we analysed using both parametric (log-normal) and non-parametric (logrank, Gray-Tsiatis and Laska-Meisner) methods. With limited follow-up (5 years), the non-parametric tests had greater power than the log-normal model. This advantage diminished, however, with extended follow-up (15 years). Furthermore, only the log-normal model could distinguish reliably a survival advantage due to an increase in cured fraction from an advantage due to an increase in time to failure.

Breast Neoplasms↗
PubMedDate not supplied

Testing for differences in changes in the presence of censoring: parametric and non-parametric methods.

Some commonly used parametric and non-parametric methods for analysing repeated measures with incomplete observations are briefly reviewed. The performances of these methods in the presence of completely random, as well as informative censoring are compared in simulated experiments generated under the linear random effects model with parameter values derived from realistic examples. The effects of some moderate model deviations are also compared. The results indicate that in the presence of informative censoring, the usual parametric and nonparametric methods derived under the assumption of random censoring could either suffer severe loss of power or provide false positive results. The conditional linear model for informative censoring when used in conjunction with the bootstrap variance estimation procedure performed well under both random and informative censoring mechanisms. The non-parametric procedure obtained by ranking the individual summary statistics, although not as efficient as the conditional linear model with robust variance, also performed relatively well in most situations. Therefore, in situations in which informative censoring is likely to occur it is important to select the proper method of analysis to test for the informativeness of censoring and to account for its effects.

Bias↗
PubMedDate not supplied

Investigations into parametric analysis of data from in vivo micronucleus assays by comparison with non-parametric methods.

Data from micronucleus assays of 6 compounds were analysed by 3 non-parametric and 3 parametric methods. Two of the latter involved transformation of the data so several transformation strategies were investigated. It was concluded that the non-parametric Kolmogorov-Smirnov two-sample test was the most reliable method of analysis. None of the parametric solutions was entirely satisfactory. The Bayesian solution to the Behrens-Fisher problem of normal distributions with differing variances was an acceptable compromise after the data had been transformed by the inverse hyperbolic sine method applicable to negative binomials.

Animals↗
PubMed2000

Variance in parametric images: direct estimation from parametric projections.

Recent work has shown that it is possible to apply linear kinetic models to dynamic projection data in PET in order to calculate parameter projections. These can subsequently be back-projected to form parametric images--maps of parameters of physiological interest. Critical to the application of these maps, to test for significant changes between normal and pathophysiology, is an assessment of the statistical uncertainty. In this context, parametric images also include simple integral images from, e.g., [O-15]-water used to calculate statistical parametric maps (SPMs). This paper revisits the concept of parameter projections and presents a more general formulation of the parameter projection derivation as well as a method to estimate parameter variance in projection space, showing which analysis methods (models) can be used. Using simulated pharmacokinetic image data we show that a method based on an analysis in projection space inherently calculates the mathematically rigorous pixel variance. This results in an estimation which is as accurate as either estimating variance in image space during model fitting, or estimation by comparison across sets of parametric images--as might be done between individuals in a group pharmacokinetic PET study. The method based on projections has, however, a higher computational efficiency, and is also shown to be more precise, as reflected in smooth variance distribution images when compared to the other methods.

Animals↗
PubMed2000

Robust parametric and semi-parametric spot fitting for spot array images.

In this paper we address the problem of reliably fitting parametric and semi-parametric models to spots in high density spot array images obtained in gene expression experiments. The goal is to measure the amount of label bound to an array element. A lot of spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots we use robust M-estimators. When the parametric method fails (which can be detected automatically) we use a novel, robust semi-parametric method which can handle spots of different shapes accurately. The introduced techniques are evaluated experimentally.

Animals↗
PubMed1997

Fully parametric and semi-parametric regression models for common events with covariate measurement error in main study/validation study designs.

The derivation of the likelihood function for binary data from two types of main study/validation study designs where model covariates are measured with error is elaborated. Rather than limiting consideration to a restricted family of models with convenient mathematical properties, we suggest that empirical considerations, customized to the data at hand, should drive model choices. The joint likelihood function for the main study, in which the covariates are measured with error, and the validation study, in which they are not, is maximized, and estimation and inference proceeds using standard theory. Although the choice of the measurement error model is driven by empirical considerations, the relatively small validation study sizes typically seen may lead to misspecification, resulting in bias in estimation and inference about exposure-disease relationships. By using a nonparametric form for the measurement error model, the resulting semi-parametric methods suggested by Robins, Rotnitzky, and Zhao (1994, Journal of the American Statistical Association 89, 864-866) and Robins, Hsieh, and Newey (1995, Journal of the Royal Statistical Society, Series B 57, 409-424) are free from bias due to misspecification of the measurement error model, trading efficiency for robustness as usual. These fully and semi-parametric methods are illustrated with a detailed example from a main study/validation study of the health effects of occupational exposure to chemotherapeutics among pharmacists (Valanis et al., 1993, American Journal of Hospital Pharmacy 50, 455-462). A constant, prevalence ratio model for common binary events, with gamma covariate measurement error, is derived and empirically verified by the available data. A careful reanalysis of the data, taking measurement error fully into account, leads to a threefold increase in the log relative risk and no loss of statistical power. The semi-parametric estimates are consistent with the parametric results, providing reassurance that important bias due to misspecification of the measurement error model is unlikely.

Analysis of Variance↗
PubMed1996

Parametric and non-parametric estimation of speech formants: application to infant cry.

The present paper addresses the issue of correctly estimating the peaks in the speech envelope (formants) occurring in newborn infant cry. Clinical studies have shown that the analysis of such spectral characteristics is a helpful noninvasive diagnostic tool. In fact it can be applied to explore brain function at very early stage of child development, for a timely diagnosis of neonatal disease and malformation. The paper focuses on the performance comparison between some classical parametric and non-parametric estimation techniques particularly well suited for the present application, specifically the LP, ARX and cepstrum approaches. It is shown that, if the model order is correctly chosen, parametric methods are in general more reliable and robust against noise, but exhibit a less uniform behaviour than cepstrum. The methods are compared also in terms of tracking capability, since the signals under study are nonstationary. Both simulated and real signals are used in order to outline the relevant features of the proposed approaches.

Computer Simulation↗
PubMed1997

Parametric and non-parametric tests for the overall comparison of several treatments to a control when treatment is expected to increase variability.

We consider the problem of making an overall comparison of several treatments to a control where experimental units are randomly assigned to either the 'control' group which receives no treatment or to one of k-1 'treatment' groups. We assume that the effect of the treatments is, if anything, a location shift possibly accompanied by an increase in scale relative to that of the control group. The ANOVA F test loses considerable power in such circumstances. A modification of the ANOVA F test has been proposed which uses the variance estimate from the controls in place of the usual pooled variance estimate. However, this modification has shortcomings when k exceeds two and the variances of the treatment groups are not inflated. We develop a combination procedure to avoid the pitfalls of the modified and usual F tests. We then propose parametric and non-parametric implementations of a likelihood ratio test that more efficiently incorporates the assumptions of this problem, yielding a test with a high power profile over a large range of normal alternatives. We use simulations to compare the power of the competing tests against several alternatives for normal and non-normal data.

Analysis of Variance↗
PubMed1999

Constructing confidence intervals for cost-effectiveness ratios: an evaluation of parametric and non-parametric techniques using Monte Carlo simulation.

The statistic of interest in most health economic evaluations is the incremental cost-effectiveness ratio. Since the variance of a ratio estimator is intractable, the health economics literature has suggested a number of alternative approaches to estimating confidence intervals for the cost-effectiveness ratio. In this paper, Monte Carlo simulation techniques are employed to address the question of which of the proposed methods is most appropriate. By repeatedly sampling from a known distribution and applying the different methods of confidence interval estimation, it is possible to calculate the coverage properties of each method to see if these correspond to the chosen confidence level. As the results of a single Monte Carlo experiment would be valid only for that particular set of circumstances, a series of experiments was conducted in order to examine the performance of the different methods under a variety of conditions relating to the sample size, the coefficient of variation of the numerator and denominator of the ratio, and the covariance between costs and effects in the underlying data. Response surface analysis was used to analyse the results and substantial differences between the different methods of confidence interval estimation were identified. The methods, both parametric and non-parametric, which assume a normal sampling distribution performed poorly, as did the approach based on simply combining the separate intervals on costs and effects. The choice of method for confidence interval estimation can lead to large differences in the estimated confidence limits for cost-effectiveness ratios. The importance of such differences is an empirical question and will depend to a large extent on the role of hypothesis testing in economic appraisal. However, where it is suspected that the sampling distribution is skewed, normal approximation methods produce particularly poor results and should be avoided.

Computer Simulation↗
PubMed1991

Semi-parametric and non-parametric methods for the analysis of repeated measurements with applications to clinical trials.

Techniques applicable for the analysis of longitudinal data when the response variable is non-normal are not nearly as comprehensive as for normally-distributed outcomes. However, there have been several recent developments. Semi-parametric and non-parametric methodology for the analysis of repeated measurements is reviewed. The commonly encountered design in which, for each subject, one assesses a univariate response variable at multiple fixed time points, is considered. The types of outcomes considered include binary, ordered categorical, and continuous (but extremely non-normal) response variables. All of the methods considered allow for incomplete data due to the occurrence of missing observations. In addition, discrete and/or continuous covariates, which may be time-dependent, are accommodated by some of the approaches. The methods are demonstrated using data from three clinical trials.

Clinical Trials as Topic↗
PubMed1989

A method for comparing semi-parametric models with parametric models in competing risks analysis.

In survival analysis, Cox's regression model is often used to assess the effect of covariates on the time of failure. This semi-parametric model has been extended to the situation where more than one cause of failure is of interest. In this paper, two semi-parametric models for the analysis of competing risks with covariates in the presence of independent random censoring are considered. Particular attention is devoted to the comparison between the two models. A method using a measure derived from the generalized variance is proposed. This method is illustrated with an example in a cancer clinical trial. A FORTRAN program for the computer implementation of the method is also discussed.

Carcinoma, Squamous Cell↗
PubMed1999

Has DRG payment influenced the technical efficiency and productivity of diagnostic technologies in Portuguese public hospitals? An empirical analysis using parametric and non-parametric methods.

The use of Diagnosis Related Groups (DRG) as a mechanism for hospital financing is a currently debated topic in Portugal. The DRG system was scheduled to be initiated by the Health Ministry of Portugal on January 1, 1990 as an instrument for the allocation of public hospital budgets funded by the National Health Service (NHS), and as a method of payment for other third party payers (e.g., Public Employees (ADSE), private insurers, etc.). Based on experience from other countries such as the United States, it was expected that implementation of this system would result in more efficient hospital resource utilisation and a more equitable distribution of hospital budgets. However, in order to minimise the potentially adverse financial impact on hospitals, the Portuguese Health Ministry decided to gradually phase in the use of the DRG system for budget allocation by using blended hospital-specific and national DRG case-mix rates. Since implementation in 1990, the percentage of each hospital's budget based on hospital specific costs was to decrease, while the percentage based on DRG case-mix was to increase. This was scheduled to continue until 1995 when the plan called for allocating yearly budgets on a 50% national and 50% hospital-specific cost basis. While all other non-NHS third party payers are currently paying based on DRGs, the adoption of DRG case-mix as a National Health Service budget setting tool has been slower than anticipated. There is now some argument in both the political and academic communities as to the appropriateness of DRGs as a budget setting criterion as well as to their impact on hospital efficiency in Portugal. This paper uses a two-stage procedure to assess the impact of actual DRG payment on the productivity (through its components, i.e., technological change and technical efficiency change) of diagnostic technology in Portuguese hospitals during the years 1992-1994, using both parametric and non-parametric frontier models. We find evidence that the DRG payment system does appear to have had a positive impact on productivity and technical efficiency of some commonly employed diagnostic technologies in Portugal during this time span.

Diagnosis-Related Groups↗