bluuebunny/crossref_metadata_2025 huggingface dataset:
Adjustment of risk ratios in case‐base studies (hybrid epidemiologic designs)
Authors: Greenland Sander
Published: April 2007

The recent growth of occupational epidemiology has led to a resurgence of interest in the hybrid epidemiologic study design of Kupper et al.,1 which has come to be termed a ‘case‐base’ or ‘case‐cohort’ design. This paper shows how the adjustment method for the risk ratio estimate given by Kupper et al. may have substantial bias, and also shows how one must modify conventional adjustment techniques (such as the Miettinen, Woolf and Mantel ‐Haenszel methods) when used with hybrid designs. This paper also provides the necessary modifications for tests and confidence intervals.
Applications of time‐series analysis: A case study on the impact of computer tomography
Authors: Tsouros Agis D. , Young Robert J.
Published: April 2007

Time‐series techniques are applied to an analysis of the use of neurodiagnostic procedures in Athens, to evaluate the effect of the introduction of computer tomography. Results confirm the usefulness of such techniques, but illustrate the dependence of their successful application on the nature and quality of data.
Evaluation of the proportionate mortality index in the presence of multiple comparisons
Authors: Rockette Howard E. , Arena Vincent C.
Published: April 2007

We investigate the frequency in occupational studies of falsely identifying significantly elevated cause‐specific mortality in the presence of multiple comparisons. When one investigates a standard battery of causes of death, use of a standardized mortality ratio as a summary index results in an excessive experimental error rate, but one can control the error rate with use of standard procedures that limit the type I error. When a major disease category for an occupational cohort has a lower rate than expected, then with use of a proportionate mortality ratio as a summary index, the standard procedures for control of the type I error are inadequate. In this situation, use of a proportionate mortality ratio as a summary index is likely to result in the false identification of an excess cause‐specific mortality more often than previously acknowledged.
Robustness of the two independent samplest‐test when applied to ordinal scaled data
Authors: Heeren Timothy , D'Agostino Ralph
Published: April 2007

One may encounter the application of the two independent samplest‐test to ordinal scaled data (for example, data that assume only the values 0, 1, 2, 3) from small samples. This situation clearly violates the underlying normality assumption for thet‐test and one cannot appeal to large sample theory for validity. In this paper we report the results of an investigation of thet‐test's robustness when applied to data of this form for samples of sizes 5 to 20. Our approach consists of complete enumeration of the sampling distributions and comparison of actual levels of significance with the significance level expected if the data followed a normal distribution. We demonstrate under general conditions the robustness of thet‐test in that the maximum actual level of significance is close to the declared level.
Use of unequal allocation in survival trials
Authors: Sposto Richard , Krailo Mark D.
Published: April 2007

In some two‐treatment clinical survival trials, a large imbalance in the allocation of patients to treatments will result in approximately the same power for the logrank test as equal allocation. This fact can be used in some trials to reduce significantly the number of patients allocated to a potentially inferior treatment.
Modified replacement randomization
Authors: Abel U.
Published: April 2007

We propose a slight modification of replacement randomization by imposing an imbalance criterion within a restricted range of patient entry numbers. The bound on imbalance is stated most simply in terms of the maximum allowable difference in treatment assignments at the end of the trial. For clinical trials in which two therapies are compared, we study, by means of computer simulations, the dependence of both selection bias and the mean number of randomization list replacements on the parameters defining the procedure.
Fitting nonlinear models with ARMA errors to biological rhythm data
Authors: Greenhouse Joel B. , Kass Robert E. , Tsay Ruey S.
Published: April 2007

Many behavioural and physiological processes are periodic with a period of approximately 24 hours. For descriptive purposes, linear regression using a simple sinusoid with a fixed 24 hour period is sometimes an adequate tool for analysing data from such processes. Inference based on regression models under the assumption of independent and identically distributed errors, however, can often mislead seriously. In this paper we present a general class of models for fitting biological rhythms, with use of higher‐order harmonic terms of one or more unknown fundamentals and ARMA processes for the errors. We describe the procedures for model specification and estimation and give the theoretical justification for these procedures. Analysis of a series of human core body temperature illustrates the methodology.
Accumulating evidence from independent studies: What we can win and what we can lose
Authors: Light Richard J.
Published: April 2007

When asking ‘what is known’ about a drug or therapy or program at any time, both researchers and practitioners often confront more than a single study. Facing a variety of findings, where conflicts may outweigh agreement, how can a reviewer constructively approach the task? In this discussion, I will outline some questions that can only be answered by examining a group of independent studies. I will also discuss some pitfalls that sometimes swamp the benefits we can gain from synthesis. Most of these pitfalls are avoidable if anticipated early in a review. The benefits of a quantitative review include information about how to match a treatment with the most promising recipients; increasing the statistical power to detect a significant new treatment; telling us when ‘contextual effects’ are important; helping us to assess the stability and robustness of treatment effectivenes; and informing us when research finds are especially sensitive to investigators' research design. The pitfalls include aggregating data from studies on different populations; aggregating when there is more than one underlying measure of central tendency; and emphasizing an average outcome when partitioning variance gives far more useful information.
Meta‐analysis of clinical trials as a scientific discipline. I: Control of bias and comparison with large co‐operative trials
Authors: Chalmers Thomas C. , Levin Howard , Sacks Henry S. , Reitman Dinah , Berrier Jayne , Nagalingam Raguraman
Published: April 2007

Meta‐analysis is an important method of bridging the gap between undersized randomized control trials and the treatment of patients. However, as in any retrospective study, the opportunities for bias to distort the results are widespread. Attempts must be made to introduce the controls found in prospective studies by blinding the selection of papers and extraction of data and making blinded duplicate determinations. Informal and personalized methods of obtaining data are probably more liable to error and bias than employing only published data. Publication bias is a serious problem requiring further research. There also need to be more comparisons of meta‐analysed small studies with large co‐operative trials.
Methods for combining randomized clinical trials: Strengths and limitations
Authors: Demets David L.
Published: April 2007

Methods for combining data from several studies exist and appear to be quite useful. None satisfactorily addresses the question of what studies should be combined. This issue is the most serious methodological limitation. Even studies with statistically significant interaction might still be combined if the effect were in the same direction. Thus, substantial scientific input is required as to what criteria must be met by each potential study. Much can be learned from combining or pooling data but it must be done cautiously. Pooling exercises do not replace well designed prospective clinical trials. Efforts for establishing basic design criteria to allow for multicentre and multicountry trials to be more easily combined might be useful.
Effects on overviews of early stopping rules for clinical trials
Authors: Green Stephanie J. , Fleming Thomas R. , Emerson Scott
Published: April 2007

Use of inappropriate stopping rules for clinical trials results in an excess of false positive conclusions when no true survival differences exist. Overviews of such trials, however, consist mainly of trials which were not stopped early, plus a few of reduced sample size which were. Simulations confirm that the level of such an overview is minimally elevated. Additional follow‐up for survival further corrects the level. In fact, for individual trials conducted according to inappropriate rules, late tests have nearly correct level. On the other hand, publication bias (differential reporting of positive results) can substantially increase the level of an overview if only published studies are included.
Implications of overviews of randomized trials
Authors: Hennekens Charles H. , Buring Julie E. , Hebert Patricia R.
Published: April 2007

Many randomized trials are of insufficient sample size to detect with adequate power the small to moderate effects that are most likely to occur. As a result, a single such trial can produce a null rinding that is, in fact, uninformative, but none the less is misinterpreted as demonstrating no effect. An overview considers all available trials and can increase the statistical power to detect an effect if present. Thus overviews can provide perhaps the most precise estimate of the magnitude of a treatment effect based on existing data. This may have implications for the formulation of public policy but certainly should influence the conduct and planning of randomized trials. Public policy may be influenced in circumstances where further trials are unlikely to be conducted. Overviews can also provide guidance as to whether changes in protocols of ongoing studies are recommended as a result of new evidence. Perhaps most importantly, overviews can provide information about whether additional trials are warranted, and, if so, the sample size that would be required to answer the research question definitively.
The relative efficiency of the sets and the cusum techniques in monitoring the occurrence of a rare event
Authors: Chen Rina
Published: April 2007

The efficiency of each of two methods for monitoring the occurrence of a rare event, the cusum, carried out monthly, and the sets techniques, was assessed by use of simulated datasets. The monthly cusum parameters used in this study were those recommended for monitoring of birth defects in a monthly analysis. The sets technique parameters, recommended for monitoring of low‐level epidemics, were adjusted to match the average interval between false alarms to the corresponding values observed for the monthly cusum technique. The results indicate that the relative efficiency of the two techniques depends on the baseline frequency of diagnoses. The sets technique is shown to be more efficient than monthly cusum when the number of cases expected in a year is no greater than five, but less efficient otherwise. In the former case, lengthening the interval between cusum analyses can make the cusum technique more efficient than the sets technique. This possibility deserves further exploration and could affect future recommendations.
A simple method of sample size calculation for unequal‐sample‐size designs that use the logrank or t‐test
Authors: Hsieh F. Y.
Published: April 2007

This paper presents a simple method of calculating sample sizes for unequal‐sample‐size designs with use of published tables applicable to equal‐sample‐size designs. The method applies to both the logrank test and the t‐test. For the power of logrank test, this paper compares the proposed method with existing methods and with the Monte Carlo simulation.
The analysis of a titration study
Authors: Chuang Christy
Published: April 2007

For analysis of data from a titration study, this paper adopts the idea of a life table analysis and utilizes a logistic linear dose–response model. The procedure uses iterated reweighted least‐squares to provide parameter estimates. Computation of the asymptotic covariance of the life‐table estimates of the response rates involves Greenwood's formula and a conditioning argument. A special case consists of examination of the covariance structure when no dropouts took place. Data from a titration study that investigated the efficacy of an anti‐hypertensive compound illustrate the procedure.
Monitoring maternity services by postal questionnaire: Congruity between mothers' reports and their obstetric records
Authors: Martin Claudia J.
Published: April 2007

The validity of information obtained from women about the medical aspects of their childbearing experiences has been assessed by comparing the responses of 223 mothers on a postal questionnaire with information extracted from their medical records. When discrepancies between the two sources were found, it was not assumed that the hospital records were correct. Instead, attempts were made to ascertain why the data might be inconsistent by re‐checking the records and by contacting the mothers again. There appeared to be five main reasons for discrepancies: mothers' limited knowledge or understanding of certain procedures; problems of interpretation and definition; occasionally inaccurate or missing information in the medical records; underreporting of sensitive information by mothers and, finally, questions which were misunderstood or misinterpreted by the mothers. For most items, however, good agreement between the mothers and hospitals was obtained, and the results indicate that valid information over a range of events can be obtained using postal surveys of mothers.
A cost‐function approach to the design of reliability studies
Authors: Eliasziw Michael , Donner Allan
Published: April 2007

We present a method to determine the number of subjects, k, and number of repeated measurements, n, that minimize the overall cost of conducting a reliability study, while providing acceptable power for tests of hypotheses concerning the reliability coefficient p. Tables showing optimal choices of k and n under various cost constraints also appear. We discuss design considerations for reliability studies in light of the results.
Hypothesis testing of time‐dependent recurrent events
Authors: Niland Joyce C. , Thomas Duncan , Azen Stanley P.
Published: April 2007

This paper examines the problem of hypothesis testing for comparison of time‐dependent recurrent events between categorical exposure groups. We compare three methods (a summary chi‐square test based on a generalization of the simple Poisson distribution, a chi‐square test based on a generalization of the compound Poisson distribution, and a test on risk scores based on individual observed to expected ratios), when the dependent variable may be autocorrelated within an individual, and disease risk may be heterogeneous among subjects. We present a simulation study and an application to a cohort of sickle cell anaemia patients. With autocorrelation or heterogeneous risk present, the simple chi‐square test is inappropriate, while the other two methods perform well. An attraction of the risk score method is its relative ease of application.
Reporting clinical trials from the viewpoint of a patient's choice of treatment
Authors: Hilden Jørgen
Published: April 2007

Those who report a clinical trial should acknowledge the right of the ‘consumer’ to make decisions based on his own valuation of the beneficial and adverse effects which rival treatments may have. Suppose a new patient is inclined to trade one unit of benefit for c units of complication. Then he should (should not) be given the treatment if his estimated utility gain, χ1 ‐cχ2, is positive (negative) and statistically significant according to the data of the trial: here χ1 (χ2) denotes the observed average benefit (complication level). If the estimated gain is not statistically significant, the data do not allow any firm recommendation. This c‐dependent recommendation in general cannot be determined from inspection of a joint confidence region for the two means concerned. Therefore investigators should present the outcome of the significance test as a function of c (inverted inference). Typically there are several types of adverse effect or benefit, in which case the quantity c must be generalized into a vector of personal relative utility weights.
Models of the healthy worker effect in industrial cohorts
Authors: Bell C. M. J. , Coleman D. A.
Published: April 2007

The patterns of standardized mortality ratios (SMRs) with duration of follow‐up and duration of service which are characteristic of the healthy worker effect in occupational cohort studies have been simulated using models of selection and survivor effects. The models support current theories about the population dynamics which underlie the selection effect but highlight inadequacies in our understanding of the survivor effect. Several possible models were tested and methods of generating appropriate data are discussed. It is important that the healthy worker effect should be better understood as it can present so large a problem in the interpretation of occupational risk estimates.
The use of lung function tests in identifying factors that affect lung growth and aging
Authors: Buist A. Sonia , Vollmer William M.
Published: April 2007

Lung function tests are used both clinically, in assessing disease, and epidemiologically, in identifying those factors which influence the growth and aging process of the lungs. The user must beware of several common pitfalls in the use of these tests. however. First, the commonly used tests of lung function can only identify patterns of dysfunction, not specific pathologic processes. Second, these tests are subject to many sources of intra‐ and inter‐subject variability, making it difficult to dissect out the signal (for example, the rate of lung aging in adults) from the noise which may greatly exceed the signal. Finally, the analysis of longitudinal pulmonary function data is complicated by the alinearity of the growth and aging process, missing data, variable follow‐up times, information censoring mechanisms and covariate processes, and problems in defining abnormality.
Effects of mismodelling and mismeasuring explanatory variables on tests of their association with a response variable
Authors: Lagakos S. W.
Published: April 2007

We consider three commonly‐used statistical tests for assessing the association between an explanatory variable and a measured, binary, or survival‐time, response variable, and investigate the loss in efficiency from mismodelling or mismeasuring the explanatory variable. With respect to mismodelling, we examine the consequences of using an incorrect dose metameter in a test for trend, of mismodelling a continuous explanatory variable, and of discretizing a continuous explanatory variable. We also examine the consequences of classification errors for a discrete explanatory variable and of measurement errors for a continuous explanatory variable. For all three statistical tests, the asymptotic relative efficiency (ARE) corresponding to each type of mis‐specification equals the square of the correlation between the correct and fitted form of the explanatory variable. This result is evaluated numerically for the different types of misspecification to provide insight into the selection of tests, the interpretation of results, and the design of studies where the ‘correct’ explanatory variable cannot be measured exactly.
Missing data in longitudinal studies
Authors: Laird Nan M.
Published: April 2007

When observations are made repeatedly over time on the same experimental units, unbalanced patterns of observations are a common occurrence. This complication makes standard analyses more difficult or inappropriate to implement, means loss of efficiency, and may introduce bias into the results as well. Some possible approaches to dealing with missing data include complete case analyses, univariate analyses with adjustments for variance estimates, two‐step analyses, and likelihood based approaches. Likelihood approaches can be further categorized as to whether or not an explicit model is introduced for the nonresponse mechanism. This paper will review the use of likelihood based analyses for longitudinal data with missing responses, both from the point of view of ease of implementation and appropriateness in view of the non‐response mechanism. Models for both measured and dichotomous outcome data will be discussed. The appropriateness of some non‐likelihood based analyses is briefly considered.
Estimating hidden morbidity via its effect on mortality and disability
Authors: Woodbury Max A. , Manton Kenneth G. , Yashin Anatoli I.
Published: April 2007

The applicability of the theory of partially observed finite‐state Markov processes to the study of disease. morbidity, and disability is explored. A method is developed for the continuous updating of parameter estimates over time in longitudinal studies analogous to Kalman filtering in continuous valued continuous time stochastic processes. It builds on a model of filtering of incompletely observed finite‐state Markov processes subject to mortality due to Yashin et al. The method of estimation is based on maximum likelihood theory and the incompleteness in the observation of the process is dealt with by applying missing information principles in maximum likelihood estimation.
Sample size and power for case‐control studies when exposures are continuous
Authors: Lubin Jay H. , Gail Mitchell H. , Ershow Abby G.
Published: April 2007

In estimating the sample size for a case‐control study, epidemiologic texts present formulae that require a binary exposure of interest. Frequently, however, important exposures are continuous and dichotomization may result in a ‘not exposed’ category that has little practical meaning. In addition, if risks vary monotonically with exposure, then dichotomization will obscure risk effects and require a greater number of subjects to detect differences in the exposure distributions among cases and controls. Starting from the usual score statistic to detect differences in exposure, this paper develops sample size formulae for case‐control studies with arbitrary exposure distributions; this includes both continuous and dichotomous exposure measurements as special cases. The score statistic is appropriate for general differentiable models for the relative odds, and, in particular, for the two forms commonly used in prospective disease occurrence models: (1) the odds of disease increase linearly with exposure; or (2) the odds increase exponentially with exposure. Under these two models we illustrate calculation of sample sizes for a hypothetical case‐control study of lung cancer among non‐smokers who are exposed to radon decay products at home.
Assessing diagnostic reliability and estimating incidence rates associated with a strictly progressive disease: Dental caries
Authors: Espeland Mark A. , Murphy William C. , Leverett Dennis H.
Published: April 2007

This paper presents a general approach for simultaneously assessing, from serial data, diagnostic consistency, interrater reliability and incidence of a strictly progressive disease. Observed data are viewed as incomplete: diagnostic errors are not distinguished from true diagnoses. We introduce a broad class of models to separate rater errors from underlying patterns of disease incidence. The analysis can include covariates and risk factors. We provide variance expressions for parameter estimates. Categorical data for estimating the incidence of dental caries serve as an example.
The probability that long‐term mean diastolic blood pressure is extreme: Tables for black individuals
Authors: Wilson P. David , Hebel J. Richard
Published: April 2007

With use of a large data base and a statistical model for diagnosis developed earlier, we have constructed the among‐individual conditional distribution of the long‐term mean diastolic blood pressure, md, of an individual, given the means of several measurements of systolic and diastolic blood pressure on the individual. Using this conditional distribution we provide tables and a simple algorithm for computation of the conditional probability that md for an individual exceeds a specified value (for example, 90 mmHg), given the measured values of systolic and diastolic pressure.
A note on sample size computation for testing interactions
Authors: Lachenbruch Peter A.
Published: April 2007

This paper discusses a simple method to compute sample sizes for testing interactions in analysis of variance. It uses orthogonal contrasts to determine the power. For the 2 × 2 table this gives a single degree of freedom contrast for consideration. For larger designs, one must consider a set of contrasts. The use of Bonferroni or Scheffé critical values is discussed. This method allows the user to compute easily a sample size without recourse to the non‐central F distribution.
The analysis of survival data from a central cancer registry with passive follow‐up
Authors: Tallis G. M. , Leppard P. , O'Neill T. J.
Published: April 2007

We present analyses for survival data obtained from a central cancer registry with passive follow‐up. This method of data collection has the potential to produce unknown random losses which would affect estimates of survival. We show that non‐parametrically estimated conditional distributions remove any effect of these unknown losses and that a compound mixture model estimates their magnitude. Lung cancer data are used to illustrate the procedures.
Planning the size of survival time clinical trials with allowance for stratification
Authors: Moussa Mohamed A. A.
Published: April 2007

Approximate allocation designs for comparing two survival distributions are considered and compared with the exact solution. The influence of incorporating covariates by stratification, accrual and follow‐up duration on trial size and power is investigated. The Bernstein and Lagakos design is suited for exponential survival due to its generality and allowance for stratification while the Freedman design is convenient for distribution‐free survival in view of its simplicity. With other specifications fixed, the saving in sample size becomes minimal as the accrual period or post‐accrual follow‐up time units expand above three. Required sample size also increases dramatically when the number of strata exceeds three.
The hidden effect of time
Authors: Altman Douglas G. , Royston J. Patrick
Published: April 2007

It is customary to regard datasets as homogeneous with respect to the order of collection of the measurements. Examples are given in which this assumption is breached. Hidden time trends have implications for the design of studies, their analysis and interpretation. It is suggested that, if the order of observations is known, a plot by time should be performed, perhaps using a cusum.
A new method of estimation of interobserver variation and its application to the radiological assessment of osteoarthrosis in hip joints
Authors: Kjærsgaard‐Andersen Per , Christensen Finn , Schmidt Steen Asmus , Pedersen Niels Wisbech , Jøensen Bent
Published: April 2007

Four frequently referenced radiological methods for assessment of osteoarthrosis in hip joints are compared with respect to their interobserver variation. One hundred hip joints were rated by four observers. Each observer applied all four methods to all the radiographs. The results were analysed by a new statistical method, which allowed detailed assessment of the nature and extent of interobserver variation. Significant differences in terms of intraobserver disagreement were found, and Heripret's method was the best in this respect. All four methods showed a significant degree of systematic disagreement among the observers. This suggests that the interobserver variation might be reduced by confronting the observers with the results of the statistical analysis of their judgements.
Modelling the liver function data in diabetes
Authors: Chen T. Timothy
Published: April 2007

We have enrolled diabetic patients into a long‐term follow‐up study that entails visits to the eye clinic every six months at which time use of a classification scheme determines whether or not the patient is normal regarding liver function. Since the study is still in progress, not all patients have complete follow‐up data. We fit models of complete symmetry to the multi‐visit binary data with supplemental margins and also investigate the data as stationary Markov chains with testing for stationarity and determination of the order of chain. The final model, a synthesis of both approaches, has potential use in future clinical trials of new drugs for diabetic patients.
Longitudinal versus cross‐sectional estimation of lung function decline–further insights
Authors: Vollmer William M. , Johnson Larry R. , McCamant Lynn E. , Buist A. Sonia
Published: April 2007

This paper explores the extent to which differences in longitudinal versus cross‐sectional inference may be influenced by the choice of statistical models. Using lung function data on 524 working men, we first compare the goodness‐of‐fit and implication for longitudinal decline of a variety of cross‐sectional models. We then compare the predicted longitudinal patterns from these models with those observed over a period of four years. In general, both approaches provide qualitatively, if not quantitatively, similar messages concerning the relative effects of smoking and age on lung function decline. Nonetheless, we acknowledge the existence of real selection and cohort effects. Although we recognize the utility of cross‐sectional designs, we discourage quantitative comparisons between studies, especially longitudinal versus cross‐sectional.
‘Optimal’ designs for drug, neurotransmitter and hormone receptor assays
Authors: Dunn G.
Published: April 2007

The present paper reports the use of computer simulations of ligand–receptor interactions to evaluate the robustness of D‐optimal designs for receptor assays when these designs are based on unrealistic models for ligand binding. Particular attention is paid to the assumed distribution of the measurement errors together with complications caused by ‘non‐specific’ binding of a radioligand to a tissue or cell preparation. It is the latter factor which suggests that an optimal design for estimation of the parameters of a simple hyperbolic dose–response curve is nowhere near to being optimal in practice.
Identifying prognostic factors in binary outcome data: An application using liver function tests and age to predict liver metastases
Authors: Makuch Robert W. , Rosenberg Philip S. , Mulshine James
Published: April 2007

A frequent issue confronting the medical scientist is the detection of important prognostic variables, or covariates, that affect the outcome variable. In this paper we propose specific guidelines for the analysis of binary outcome data based on recent developments in statistical methodology and on less formal graphical techniques. By incorporating these methods into the process of fitting the standard logistic regression model, one can assess covariate selection and model specification more thoroughly and obtain a more balanced view of a model's predictive capabilities. We apply the methods to the evaluation of non‐invasive liver function tests and age to predict the presence of liver metastases in patients with small cell lung cancer.
Prediction of outcome of anaesthesia in patients over 40 years: A multifactorial risk index
Authors: Tiret L. , Hatton F. , Desmonts J. M. , Vourc'h G.
Published: April 2007

The prediction of outcome of anaesthesia in patients over 40 years of age was assessed using a multifactorial index based on current preoperative factors recorded prospectively. The study was conducted using a representative sample of anaesthetizations (except for cardiac surgery) including 517 cases with major complication (occurring during or within 24 hours of anaesthesia) and a one in fifty random sample comprising 1538 cases without complication. A split sample approach was adopted and a logistic regression model was applied to two subsets of similar size. Four preoperative factors were significantly associated with the occurrence of complications: ASA physical status, age, surgical procedure (major/minor) and type (elective/emergency). Goodness‐of‐fit of the model was assessed using another sample of 332 cases with complication and a different subset of 987 cases without complication. The model fitted the data well (p = 0.15).
Latent variable models for the analysis of medical data with repeated measures of binary variables
Authors: Blackwood Larry
Published: April 2007

Consideration of within‐subject dependencies is a key issue in modelling binary repeated measures medical data. Borrowing from recent developments in sociology and psychology, we demonstrate the applicability of a latent variable approach to the analysis of such data. In particular we present the Rasch model as a basic model for representing the relationship of subject and treatment parameters. The latent variable approach is useful in providing a theoretical framework for specifying dependencies exactly and also as a base for considering more complicated relationships between repeated measures variables.
Identifying important results from multiple statistical tests
Authors: Parker R. A. , Rothenberg R. B.
Published: April 2007

When many statistical tests are performed simultaneously, the overall chance of a type I error (incorrect rejection of a true null hypothesis) can substantially exceed the nominal error rate used in each individual test. Numerous techniques exist to adjust results of individual tests to control this problem. In general, these techniques apply a more stringent criterion of statistical significance (a smaller P‐value) to each individual test than normally needed to maintain the experimentwise type I error. With an analysis that seeks to identify results for further research, however, such a conservative technique may not be appropriate. We present a new approach that uses a mixture of several distributions to model the set of P‐values or of test statistics. One component models the results consistent with a failure to reject the null hypothesis, while the other distribution(s) in the mixture represent results inconsistent with the null hypothesis. These latter results may not achieve statistical significance based on a conventional P‐value. We illustrate the use of the method on national mortality data and on several data sets analysed previously.
Application of multiple time series analysis to the estimation of pneumonia and influenza mortality by age 1962–1983
Authors: Stroup Donna F. , Thacker Stephen B. , Herndon Joy L.
Published: April 2007

We employed multiple time series analysis to estimate the impact of influenza on mortality in different age groups, using a procedure for updating estimates as current data become available from national mortality data collected from 1962 to 1983. We compared mortality estimates that resulted from a multivariate model for epidemic forecasting with those obtained from univariate models. We found more accurate prediction of deaths from all age groups using the multivariate model. While the univariate models show an adequate fit to the data, the multivariate model often enables earlier detection of epidemics. Additionally, the multivariate approach provides insight into relationships among different age groups at different points in time. For both models, the largest excess mortality due to pneumonia and influenza during influenza epidemics occurred among those 65 years of age and older. Although multiple time series models appear useful in epidemiologic analysis, the complexity of the modelling process may limit routine application.
Examining neighbourhood confounding in a survey: An example using the National Health and Nutrition Examination Survey II
Authors: Korn Edward L. , Graubard Barry I.
Published: April 2007

This paper demonstrates a methodology for examining whether neighbourhood stratification lessens the confounding bias of some specific risk‐factor/disease associations in analysis of the National Health and Nutrition Examination Survey II (NHANES II). The fortuitous clustering of the sample design of NHANES allows us to estimate risk‐factor/disease associations with and without controlling for neighbourhood effects on the same population. We briefly discuss the implications of this methodology for neighbourhood matching in case‐control studies.
Drug treatment of mild hypertension to reduce the risk of CHD: Is it worth‐while?
Authors: Holme Ingar
Published: April 2007

Although hypertension is regarded as a causal factor for coronary heart disease (CHD) a reduction in the risk of CHD as a result of lowering blood pressure in mild hypertension could not be demonstrated. This conclusion is based on an overview analysis of all published randomized trials in mild hypertension, including more than 25,000 subjects contributing over 125,000 person years of observation.

There is evidence that treatment reduces the risk of CHD in moderate to severe hypertension. In addition the pooling of four clinical trials with treated control groups indicates a reduction in the risk of CHD of borderline significance with β‐blockers or aggressive treatment when compared with standard treatment (mostly diuretics).

The failure of drug treatment to reduce the risk of CHD in mild hypertension is consistent with the results from epidemiological studies because of derangement of the lipid profile induced by diuretics and β‐blockers.
Remission duration: An example of interval‐censored observations
Authors: Rücker Gerta , Messerer Dorle
Published: April 2007

Remission duration data from two observational studies, the German ALL/AUL study on acute lymphoblastic and acute undifferentiated leukaemia and the Kiel Lymphoma Study, are used to demonstrate the adequacy of an interval‐censoring approach. The Turnbull estimator is contrasted with the conventionally used Kaplan—Meier estimator. In addition, a parametric model is used for estimation or simulation of the delay times of complete remission diagnosis and relapse diagnosis. Two possible consequences of the conventional approach are discussed: biased estimation (for example, overestimation of remission duration), and underestimation of the true error variance, which may lead to false positive results. On the other hand, the applicability of the interval‐censoring approach is doubtful when the censoring mechanism (examination pattern, patient's behaviour) is confounded with the endpoints.
A test to detect clustering applied to muscle fibres
Authors: Howel Denise
Published: April 2007

The random arrangement of a chosen uncommon element type within a mosaic has been assessed by the new statistics, ‘Number of Clusters’ and ‘Mean Cluster Size’. Their distributions were obtained by simulation assuming the mosaic has a hexagonal arrangement. When this assumption was relaxed to allow mosaics nearer to those observed in nature, the results were little changed. This method was then applied to a set of muscle fibre data to test hypotheses about the likely targets in inflammatory muscle disorders.
Prescription sequence analysis: A new and fast method for assessing certain adverse reactions of prescription drugs in large populations
Authors: Petri H. , De Vet H. C. W. , Naus J. , Urquhart J.
Published: April 2007

Prescription sequence analysis (PSA) uses pharmacy‐based prescription drug histories to detect a subset of drug effects: those that are themselves indications for changes in the prescribing of another drug. Dutch pharmacy practice ensures virtually complete drug histories. With a database of 25,000 patients, we used PSA to test an alleged link between the use of the anti‐vertigo drug flunarizine and mental depression. The temporal sequence of anti‐depressant use among flunarizine users shows no clustering that would suggest a causal link. PSA can be run within a few days, which may make it helpful in resolving certain of the periodic controversies about adverse drug reactions.
Explanatory and pragmatic estimates of the treatment effect when deviations from allocated treatment occur
Authors: Newcombe R. G.
Published: April 2007

In a randomized clinical trial comparing two treatments it can happen that certain patients receive the treatment other than that determined by random allocation. Significance testing is usually performed by ‘intention to treat’, that is, comparison of groups as determined by random allocation. The difference between the mean values of the outcome variable for these two groups estimates the difference in practical use between two treatment policies, corresponding to a pragmatic approach. An attenuation factor can then be used to obtain an estimate of the underlying difference in effectiveness between the two treatments, corresponding to an explanatory paradigm. Thus two distinct estimates are obtained; in many instances both are valid, but have distinct interpretations. Correspondingly, in planning sample size requirements when deviations from allocated treatment can be anticipated, the target treatment difference may be understood in either an explanatory or a pragmatic sense; for the sample size assessment method it is necessary to take this distinction into account.
Optimal placebo response rates for comparing two binomial proportions
Authors: Day Simon J.
Published: April 2007

This paper considers the problem of comparing proportions in clinical trials where two parallel groups are studied. The question of what will happen to the response rate of an active therapy if some external factor affects the placebo rate is asked and two different, but similar, answers are proposed. Assuming either a logistic or probit model has substantial influence on the sample size required to detect differences and optimal comparisons are suggested. The idea is illustrated by a clinical trial of treatment for peptic ulcer and suggestions for other possible applications are presented.
Inborn/outborn status and neonatal survival: A meta‐analysis of non‐randomized studies
Authors: Ozminkowski Ronald J. , Wortman Paul M. , Roloff Dietrich W.
Published: April 2007

We have applied meta‐analysis to investigate the relationship between birth place and the likelihood of neonatal survival, for infants of low birth weight (< 2501 grams) in a series of 19 non‐randomized studies. This paper illustrates the utility meta‐analysis in evaluating medical technologies described in non‐randomized studies, if proper attention is given to biases in those studies. The results of this meta‐analysis show strong preferences for inborn status, especially for infants who weigh 1001—2000 grams. For infants of lower or higher birth weight (that is, < 1001 or > 2000 grams), the studies are inconsistent: some favour inborn status while others favour outborn status. This heterogeneity is not surprising, because selection bias is more problematic in studies of infants at these birth weights. We discuss potential causes of and solutions to selection bias and illustrate its potential magnitude by introducing thebias factor, which should be considered in the design of future studies. When selection bias cannot be ruled out, the results shown for those who weigh 1001—2000 grams are more appropriate for generating valid conclusions and subsequent policies regarding birth place preference for low birth weight infants.
A statistical assessment of clinical equivalence
Authors: Mau Jochen
Published: April 2007

An observed confidence distribution is proposed as a measure of strength of evidence for practically equivalent efficacies of two treatments. The concept is independent of prior opinions about relevant sizes of a difference in efficacy. It also avoids retrospective power calculations for trials with missed recruitment goals and allows combination of the confidence from independent experiments.
Using association models to analyse agreement data: Two examples
Authors: Becker Mark P.
Published: April 2007

Two examples demonstrate how one can use association models to analyse agreement data. The first example concerns intra‐rater variability in the classification of sputum cytology slides, and the second deals with variability associated with the reporting of passive smoking histories. The paper emphasizes models in which one estimates category scores from the data, that is models that are not in the log‐linear family of models. Such models have use in assessment of category distinguishability and provide insights not easily obtained with log‐linear models.
Analysis of epidemiologic case—base studies for binary data
Authors: Nurminen Markku
Published: April 2007

Recent developments in statistical methods for epidemiology have revived the application of sampling techniques in the design and analysis of cohort studies. The ‘case — base’ design involves sampling of both the cases and the cohort base of the study. This paper reviews some data‐analytic imperfections of the approaches to risk ratio estimation, and modifies and advances a consistent likelihood‐based procedure — analogous to Miettinen and Nurminen's proposal for a full cohort design — for interval estimation (and also point estimation and significance testing) in the context of binary case—base data. First, the procedure avoids the use of Taylor‐series approximations to derive variance estimators for non‐linear functions of parameters. Second, the asymptotic condition effects a simple computational expression for the chi‐square function of risk ratios that is universally applicable to small samples. The statistical modelling underlying the method allows inferences about risk ratios without the assumption of rare disease either for the general population or for a particular base. The paper also extends the analysis to encompass stratified data. Finally, a numerical evaluation evinced the accurate small‐sample properties of the proposed method.
A note on confidence limits for quartiles with right censored data
Authors: Elashoff R. M. , Lee J. J. , Afifi A. A.
Published: April 2007

Two new methods are proposed for constructing the confidence limits for quartiles. The small sample behaviour of these two methods is compared with the Jennison and Turnbull modified Brookmeyer—Crowley method at three quartiles. The simulation study indicates that one of the new methods, the smoothed modified reflected method, is preferred over the other methods when the censoring rate is less than 40 per cent while the Jennison and Turnbull method is preferred for higher censoring. The results for the upper quartile are similar to the median. For the lower quartile with small sample and high censoring, semi‐infinite intervals may often occur. The correct practice is to permit the semi‐infinite intervals without modifying them since it is more informative and gives closer to nominal level coverage.