摘要:

AbstractThe paper presents a case‐control study involving a disease, exposures and several continuous confounders. The relative efficiency and validity of a fully matched design is compared with random sampling of controls. We test a viable option of a partially matched design when inability to match all study subjects on all confounders occurs. The degree of bias in the odds ratios introduced by the different designs and by the different analytic models is assessed in comparison with the estimates obtained from a total cohort, from which both cases and controls were selected. Matched designs and analytic strategies are also evaluated in terms of the variances of the odds ratios. The results indicate that matching on continuous variables may lead to a more precise estimate of odds ratio than statistical control of confounding in unmatched designs. Partial selection of controls by matching may be a useful strategy when complete matching cannot be achieved; in practice, partial matching achieves most of the benefits of full matchin

ISSN:0277-6715    

DOI:10.1002/sim.4780121101

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

摘要:

AbstractThis study proposes an econometric technique to reduce attrition bias in panel data. In the simplest case, one estimates two regressions. The first is a probit regression based on sociodemographic and clinical characteristics measured at baseline. The probit regression estimates the probability that subjects stay or leave over the duration of the study. We insert the predicted probabilities from the probit regression into an inverse Mills ratio (IMR) or hazard rate to form an instrumental variable. We use this instrumental variable subsequently as an additional covariate in a second regression model that attempts to explain fluctuations in the dependent variable. The second regression, which is linear, includes only subjects who remained in the study. In alternative models, instrumental variables are created using predicted values from least squares and logit regressions estimating the probability that subjects stay or leave. The use of the instrumental variables reduces the effects of attrition bias in the linear regression model We applied the technique to a panel of patients with rheumatoid arthritis (RA) enrolled in 1981 and followed through 1990. We attempted to predict values for a measure of functional disability recorded in 1990 with use of covariates measured in 1981. The dependent variable was an index of disability in 1990 and the independent variables (covariates) included the disability index from 1981, the years of duration of RA, gender, martial status, education, and age in 1981. The correction technique suggested that ignoring attrition bias would underestimate the strength of associations between being female and the subsequent disability index, and overestimate the strength of associations between being married spouse present, age, and the initial disability index on the one hand and the subsequent disability index on the other.

ISSN:0277-6715    

DOI:10.1002/sim.4780121102

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

摘要:

AbstractMultivariate survival data arise when an individual records multiple survival events or when individuals recording single survival events are grouped into clusters. In this paper we propose a new method for the analysis of multivariate survival data. The technique is a synthesis of the Poisson regression formulation for univariate censored survival analysis and the generalized estimating equation approach for obtaining valid variance estimates for generalized linear models in the presence of clustering. When the survival data are clustered, combining the methods provides not only valid estimates for the variances of regression parameters but also estimates of the dependence between survival times. The approach entails specifying parametric models for the marginal hazards and a dependence structure, but does not require specification of the joint multivariate survival distribution. Properties of the methodology are investigated by simulation and through an illustrative example.

ISSN:0277-6715    

DOI:10.1002/sim.4780121103

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

摘要:

AbstractIn multicentre clinical trials using a common protocol, the centres are usually regarded as being a fixed factor, thus allowing any treatment‐by‐centre interaction to be omitted from the error term for the effect of treatment. However, we feel it necessary to use the treatment‐by‐centre interaction as the error term if there issubstantialevidence that the interaction with centres is qualitative instead of quantitative. To make allowance for the estimated uncertainties of the centre means, we propose choosing a reference value (for example, the median of the ordered array of centre means) and converting the individual centre results into standardized deviations from the reference value. The deviations are then reordered, and the results ‘pushed back’ by amounts appropriate for the corresponding order statistics in a sample from the relevant distribution. The pushed‐back standardized deviations are then restored to the original scale. The appearance of opposite signs among the destandardized values for the various centres is then taken as ‘substantial evidence’ of qualitative interaction. Procedures are presented using, in any combination: (i) Gaussian, or Student'st‐distribution; (ii) order‐statistic medians or outward 90 per cent points of the corresponding order statistic distributions; (iii) pooling or grouping and pooling the internally estimated standard deviations of the centre means. The use of the least conservative combination — Student'st, outward 90 per cent points, grouping and p

ISSN:0277-6715    

DOI:10.1002/sim.4780121104

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

摘要:

AbstractTwo procedures, based on proposals discussed by Mosteller and Tukey, are described for obtaining a combined estimate of the difference between two treatment means and its confidence interval from multicentre clinical trial data. Both procedures provide estimates in the possible presence of heteroscedasticity. The first procedure is designated the primary analysis for efficacy assessment. It omits treatment‐by‐centre interaction from the error term for treatment, unless there is substantial evidence of qualitative interaction (Cimineraet al.) or other special circumstances. The second procedure is the primary analysis whenever there is substantial evidence of qualitative interaction, and can be used whenever there are other reasons to make an analysis allowing for interact

ISSN:0277-6715    

DOI:10.1002/sim.4780121105

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

摘要:

AbstractThe two‐sample Wilcoxon rank sum test is the most popular non‐parametric test for the comparison of two samples when the underlying distributions are not normal. Although the underlying distributions need not be known in detail to calculate the null distribution of the test statistic, parametric assumptions are often made to determine the power of the test or the sample size. We encountered difficulties with this approach in the planning of a recent clinical trial in stroke patients. It is shown that, for power and sample size estimation, it can be dangerous to apply the classical formulae routinely, especially with outcome scores having a U‐shaped or a J‐shaped distribution. As an example we have taken the Barthel index, a quality‐of‐life outcome measure in stroke patients. Further, we have investigated alternative methods by means of Monte Carlo simulation. The distributional characteristics of the estimated powers were compared. Our findings suggest more appropriate computer software is necessary for the calculation of power and sample size when efficacy is measured by a non‐para

ISSN:0277-6715    

DOI:10.1002/sim.4780121106

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

摘要:

AbstractSuppose the goal of a projected study is to estimate accurately the value of a ‘prediction’ proportionpthat is specific to a given set of covariates. Available pilot data show that (1) the covariates are influential in determining the value ofpand (2) their relationship topcan be modelled as a logistic regression. A sample size justification for the projected study can be based on the logistic model; the resulting sample sizes not only are more reasonable than the usual binomial sample size values from a scientific standpoint (since they are based on a model that is more realistic), but also give smaller prediction standard errors than the binomial approach with the same sample size. In appropriate situations, the logistic‐based sample sizes could make the difference between a feasible proposal and an unfeasible, binomial‐based proposal. An example using pilot study data of dental radiographs demonstrates the

ISSN:0277-6715    

DOI:10.1002/sim.4780121107

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780121108

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780121109

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780121110

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1993

数据来源: WILEY