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For cluster randomized and multicentre trials evaluating the effect of a treatment on persons nested within clusters, equations have been published to compute the optimal sample sizes at the cluster and person level as a function of sampling costs and intraclass correlation (ICC). Here, optimal means maximum power and precision for a given sampling budget, or minimum sampling costs for a given power and precision. However, the ICC is usually unknown, and the optimal sample sizes depend strongly on this ICC. To overcome this local optimality problem, this study presents Maximin designs (MMDs) based on relative efficiency (RE) and efficiency. These designs perform well over a range of possible ICC values either in terms of RE compared with the locally optimal designs, or in terms of minimum efficiency (maximum variance) of the treatment effect estimator. The use of MMDs is illustrated using information from many cluster randomized trials in primary care. It is concluded that MMDs and the optimal design for an ICC halfway its assumed range are efficient for a range of ICC values and recommendable for practical use. This requires that trial reports mention the study cost per cluster and person. [ABSTRACT FROM AUTHOR]

There is a rich literature on the role of placebos in experimental design and evaluation of therapeutic agents or interventions. The importance of masking participants, investigators and evaluators to treatment assignment (treatment or placebo) has long been stressed as a key feature of a successful trial design. Nevertheless, there is considerable variability in the technical definition of the placebo effect and the impact of treatment assignments being unmasked. We suggest a formal concept of a ‘perception effect’ and define unmasking and placebo effects in the context of randomised trials. We employ modern tools from causal inference to derive semi-parametric estimators of such effects. The methods are illustrated on a motivating example from a recent pain trial where the occurrence of treatment-related side effects acts as a proxy for unmasking. [ABSTRACT FROM PUBLISHER]

Infection with the hepatitis C virus (HCV) is known to increase the risk of death from severe liver disease and, because HCV status is strongly associated with a history of injecting drug use, the effect of a key disease progression cofactor, infection with human immunodeficiency virus (HIV), is of interest. We examined all-cause, liver-related and drug-related mortality and excess risk of death from these causes in a large cohort of HCV-monoinfected and HIV-coinfected persons in Scotland. The study population consisted of 20,163 persons confirmed to be infected with hepatitis C through laboratory testing in Scotland between 1991 and 2005. Records with sufficient identifiers were linked to the General Register Office for Scotland death register to retrieve associated mortality data, and were further linked to a national database of HIV-positive individuals to determine coinfection status. A total of 1715 HCV monoinfected and 305 HIV coinfected persons died of any cause during the follow-up period (mean of 5.4 and 6.4 years, respectively). Significant excess mortality was observed in both HCV monoinfected and HIV coinfected populations from liver-related underlying causes (standardised mortality ratios of 25, 95% CI=23-27; and 37, 95% CI=26-52 for the two groups, respectively) and drug-related causes (25, 95% CI=23-27; 39, 95% CI=28-53. The risk of death from hepatocellular carcinoma, alcoholic or non-alcoholic liver disease, or from a drug-related cause, was greatly increased compared with the general Scottish population, with the highest standardised mortality ratio observed for hepatocellular carcinoma in the monoinfected group (70, 95% CI=57-85). This study has revealed considerable excess mortality from liver- and drug-related causes in the Scottish HCV-diagnosed population; these data are crucial to inform on the clinical management, and projected future public health burden, of HCV infection. [ABSTRACT FROM AUTHOR]

It is well established that the consumption of alcohol is implicated in both the cause and progression of chronic liver disease. The quantity of drink that is consumed, the pattern of drinking and type of alcoholic beverages consumed are all possible factors in disease aetiology. The impact of specific dietary components on the cause and progression of chronic liver disease is unclear although it is known that obesity, and hence the over-consumption of energy, is a predictor of fatty liver. Work to elucidate the role of both diet and alcohol in the aetiology of liver disease is hindered by the methods currently available to measure dietary (including alcohol) intake. The validity and reliability of retrospective methods of assessing diet are limited by their reliance on memory and, for the 24 h recall, the short-time period of intake assessed and its inability to assess variability across the week. Prospective methods which measure food and drink intake at the time of consumption, and include weighed or estimated food diaries, are useful for prospective cohort studies but are expensive and have a high respondent burden. For estimation of alcohol intake retrospectively, the Cognitive Lifetime Drinking questionnaire, which prompts responses using a lifetime calendar, is a useful tool but still depends on memory. More work is required to develop valid, reliable and easily administered tools for measurement of both diet and alcohol. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) infection in Australia is predominantly transmitted through injecting drug use. A reduction in the heroin supply in Australia in late 2000 and early 2001 may have impacted the number of injecting drug users (IDUs) and the number of new hepatitis C infections. This paper updates estimates of HCV incidence between 1960 and 2005 and models long-term sequelae from infection. Outcomes among those with HCV were also recently assessed in a linkage study assessing cancer and causes of death following HCV diagnosis in New South Wales. Linkage study outcomes have been used here to calibrate modelled outcomes. Mathematical models were used to estimate HCV incidence among IDUs, migrants to Australia from high HCV-prevalence countries, and other HCV exposure groups. Recent trends in numbers of IDUs were based on indicators of injecting drug use. A natural history of HCV model was applied to estimate the prevalence of HCV in the population. Model predicted endpoints that were calibrated against the NSW linkage data over the period 1995-2002 were: (i) incident hepatocellular carcinoma (HCC); (ii) opioid overdose deaths; (iii) liver-related deaths; and (iv) all-cause mortality. Modelled estimates and the linkage data show reasonably good calibration for HCC cases and all-cause mortality. The estimated HCC incidence was increased from 70 cases in 1995 to 100 cases in 2002. All-cause mortality estimated at 1000 in 1995 increased to 1600 in 2002. Comparison of annual opioid deaths shows some agreement. However, the models underestimate the rate of increase observed between 1995 and 1999 and do not entirely capture the rapid decrease in overdose deaths from 2000 onwards. The linkage data showed a peak of overdose deaths at 430 in 1999 compared to 320 estimated by the models. Comparison of observed liver deaths with the modelled numbers showed poor agreement. A good agreement would require an increase in liver deaths from the assumed 2 to 5% per annum following cirrhosis in the models. Mathematical models suggest that HCV incidence decreased from a peak of 14,000 infections in 1999 to 9700 infections in 2005, largely attributable to a reduction in injecting drug use. The poor agreement between projected and linked liver deaths could reflect differing coding of causes of deaths, underestimates of the numbers of people with cirrhosis following HCV, or underestimates of rates of liver death following cirrhosis. The reasonably good agreement between most of the modelled estimates with observed linkage data provides some support for the assumptions used in the models. [ABSTRACT FROM AUTHOR]

An introduction is presented in which editor discusses various articles within the issue on topics including optimal treatment allocation in cluster in multicentre trials, optimal and maximin numbers of clusters for trials and the number and timing of repeated measurements of a binary outcome.

In the last decade a growing amount of statistical research has been devoted to multiple testing, motivated by a variety of applications in medicine, bioinformatics, genomics, brain imaging, etc. Research in this area is focused on developing powerful procedures even when the number of tests is very large. This paper attempts to review research in modern multiple hypothesis testing with particular attention to the false discovery proportion, loosely defined as the number of false rejections divided by the number of rejections. We review the main ideas, stepwise and augmentation procedures; and resampling based testing. We also discuss the problem of dependence among the test statistics. Simulations make a comparison between the procedures and with Bayesian methods. We illustrate the procedures in applications in DNA microarray data analysis. Finally, few possibilities for further research are highlighted. [ABSTRACT FROM AUTHOR]

In recent years, when reporting the results of clinical trials for chronic disease, including rheumatologic conditions, use has been made of dichotomous definitions of improvement, but it is to be expected that continuous definitions would offer improved discrimination between treatment groups. Nevertheless, a well-constructed dichotomous outcome (usually a composite) has advantages of clinical sense and specificity and may, under a variety of realistic conditions, have power that closely approximates that of standard continuous outcome measures. This has been seen for established dichotomous outcome definitions for two rheumatologic conditions, rheumatoid arthritis (RA) and ankylosing spondylitis (AS). Simulation studies performed using multivariate normal generated data that approximates actual trial data for each of RA and AS patients demonstrate the relative power of several dichotomous and continuous outcomes in realistic situations for each of RA and AS. Although the continuous outcomes are typically more powerful than the dichotomous ones, there are some situations in which the power of a well-defined dichotomous outcome approaches or even exceeds that of a continuous outcome based on mean change. [ABSTRACT FROM AUTHOR]

Model-based geostatistics and Bayesian approaches are useful in the context of veterinary epidemiology when point data have been collected by appropriate study design. We take advantage of an example of Epidemiological Surveillance on urban settings where a two-stage sampling design with first stage transects is applied to study the risk of dog parasite infection in the city of Naples, 2004-2005. We specified Bayesian Gaussian spatial exponential models and Bayesian kriging were performed to predict the continuous risk surface of parasite infection on the study region. We compared the results with those obtained by the application of hierarchical Bayesian models on areal data (proportion of positive specimens by transect). The models results were consistent with each other and the Bayesian geostatistical approach proved to be more accurate in identifying areas at risk of zoonotic parasitic diseases. In general, larger risk areas were identified at the city border where wild dogs mixed with domestic dogs and human or urban barriers were less present. [ABSTRACT FROM AUTHOR]

Time-to-pregnancy(TTP), the duration that a couple waits from initiating attempts to conceive until conception occurs, is regarded as one of the direct measures of natural fecundity. Statistical tools for designing and analysing TTP studies belong to the general area of survival analysis, but several special features have been developed: it is customary to work in discrete time, and random heterogeneity between couples has always played a prominent role. This review works on this background with focus on how to perform valid analyses, under various prospective, retrospective and cross-sectional sampling frames. We illustrate using examples from our own experience. [ABSTRACT FROM AUTHOR]

Modelization of fecundability stepped recently from demography and population-based contexts to reproductive biology and treatment of infertility. This created a strong call for flexibility and robustness. Indeed, explained and unexplained heterogeneities are non-negligible sources of bias that result in false conclusions as to the determinants of fertility or to the success rates of reproductive techniques, among other examples. There are two main sources of heterogeneity: biological heterogeneity and heterogeneity of sexual behaviour. A uniform presentation of time-to-pregnancy and Barrett-Marshall models is proposed to enlighten their similarities and differences in modelling heterogeneity of fecundability. Mixed models for fecundability studies are presented as tools to allow for unexplained heterogeneity and to quantify heterogeneity of the effect of observed factors and variability of size of this unexplained heterogeneity between subpopulations. Some criteria for the modelling strategy in fecundability studies are suggested with emphasis on the unit-treatment additivity criterion. The strong and complex selection process resulting from heterogeneity is described as well as the selection and cross-selection processes of observed and unobserved fecundability factors. Consequences regarding data collection and statistical inference are discussed. In the current context, a consensus setting general rules for data collection and statistical analysis would be useful to compare the results and increase the reliability of these results in medical practice. [ABSTRACT FROM AUTHOR]

The problem of comparing two proportions in a 2 x 2 matched-pairs design with binary responses is considered. We consider one-sided null and alternative hypotheses. The problem has two nuisance parameters. Using the monotonicity of the multinomial distribution, four exact unconditional tests based on p-values are proposed by reducing the dimension of the nuisance parameter space from two to one in computation. The size and power of the four exact tests and two other tests, the exact conditional binomial test and the asymptotic McNemar's test, are considered. It is shown that the tests based on the confidence interval p-value are more powerful than the tests based on the standard p-value. In addition, it is found that the exact conditional binomial test is conservative and not powerful for testing the hypothesis. Moreover, the asymptotic McNemar's test is shown to have incorrect size; that is, its size is larger than the nominal level of the test. Overall, the test based on McNemar's statistic and the confidence interval p-value is found to be the most powerful test with the correct size among the tests in this comparison. [ABSTRACT FROM AUTHOR]

'Exact' methods for categorical data are exact in terms of using probability distributions that do not depend on unknown parameters. However, they are conservative inferentially. The actual error probabilities for tests and confidence intervals are bounded above by the nominal level. This article examines the conservatism for interval estimation and describes ways of reducing it. We illustrate for confidence intervals for several basic parameters, including the binomial parameter, the difference between two binomial parameters for independent samples, and the odds ratio and relative risk. Less conservative behavior results from devices such as (1) inverting tests using statistics that are 'less discrete', (2) inverting a single two-sided test rather than two separate one-sided tests each having size at least half the nominal level, (3) using unconditional rather than conditional methods (where appropriate) and (4) inverting tests using alternative p-values. The article concludes with recommendations for selecting an interval in three situations-when one needs to guarantee a lower bound on a coverage probability, when it is sufficient to have actual coverage probability near the nominal level, and when teaching in a classroom or consulting environment. [ABSTRACT FROM AUTHOR]

Since the early work of RA Fisher, exact methods have been recognized as important tools in data analysis because they provide valid statistical inference even with small sample sizes, or with sparse or skewed data. With the recent advance of computational power and the availability of commercial software packages, exact methods have gained substantial popularity over the past two decades. However, most of these exact methods have been devoted to testing classical null hypotheses of no differences, and until recently little was known about exact methods dealing with non-inferiority or equivalence hypotheses. The presence of nuisance parameters in testing non-inferiority/equivalence hypotheses presents a special challenge for exact methods because of the intense computational requirement. In this paper, we review exact methods available for proving non-inferiority or equivalence of two treatments with a dichotomous endpoint. First, we present the general methodology for conducting exact tests for non-inferiority or equivalence; we then discuss several unconditional and conditional methods available for constructing hypothesis tests and confidence intervals based on three commonly used measures, namely, the difference, relative risk, and odds ratio of two independent proportions or rates. Finally, we illustrate with several examples the application of these exact methods in analysing and planning non-inferiority or equivalence trials. [ABSTRACT FROM AUTHOR]

We propose a new method for computing power and sample size for linear rank tests of differences between two ordered multinomial populations. The method is flexible in that it is applicable to any general alternative hypothesis and for any choice of rank scores. We show that the method, though asymptotic, closely approximates existing exact methods. At the same time it overcomes the computational limitations of the exact methods. This advantage makes our asymptotic approach more practical for sample size computations at the planning stages of a large study. We illustrate the method with data arising from both proportional and non-proportional odds models in the two ordered multinomial setting. [ABSTRACT FROM AUTHOR]

We review the development of Bayesian statistical methods for the design and analysis of randomized controlled trials in the assessment of the cost-effectiveness of health care technologies. We place particular emphasis on the benefits of the Bayesian approach; the implications of skew cost data; the need to model the data appropriately to generate efficient and robust inferences instead of relying on distribution-free methods; the importance of making full use of quantitative and structural prior information to produce realistic inferences; and issues in the determination of sample size. Several new examples are presented to illustrate the methods. We conclude with a discussion of the key areas for future research. [ABSTRACT FROM AUTHOR]

The seeds of modern clinical trials were unwittingly sown with the first use of randomization in a 1920s agricultural field experiment. The historical development of trials is briefly reviewed here, as are multifarious pressures and problems faced by those involved with clinical trials today. These challenges include recruitment difficulties, the emerging role of patient support groups, and legal threats over informed consent, to name three. Fundamentally, they reflect an overall shift towards patient-centred, individual ethics. I suggest many problems may be overcome by increased implementation of hitherto neglected, data-dependent designs for clinical trials. Over a dozen arguments against their use are countered, primarily through ethical considerations. Benefits and costs of refining clinical trials strategy are explored hypothetically under enhanced use of such 'learn-as-you-go' designs, in contrast to traditional, equal-allocation, fixed-sample-size and frequentist-based designs. These latter methods mirror crop field trials in which one cannot make scientific progress until after gathering objective data at harvest time. Some attempts to alleviate certain problems, such as Zelen randomization to boost recruitment, or over-reliance on 'large and simple trials' to detect moderate-sized treatment effects, are discussed and found inadequate. A proposal for wider discussion is made to assist the selective introduction of 'small and complex trials,' which could simultaneously expedite medical research, satisfy the concerns of regulators, statisticians, and doctors alike, and help address the growing demands of 21st-century patients. [ABSTRACT FROM AUTHOR]

A medical statistician's routine professional activities are likely to have important ethical consequences. This is due in part to the fact that good medical practice and scientifically valid medical research both require as precursors high quality statistical design and data analysis. In this paper I discuss various ethical issues that I have encountered while working as a biostatistician at M.D. Anderson Cancer Center. I describe particular experiences and the ethical issues involved. Topics include medical decision making, benefit-harm trade-offs, safety monitoring, adaptive randomization, informed consent, and publication bias. [ABSTRACT FROM AUTHOR]

In the past two decades, it has become increasingly clear that genetic factors contribute to the aetiology of many common diseases including cancers, coronary disease, allergy and psychiatric disorders. While one goal of genetic epidemiological studies is to locate susceptibility genes for these complex diseases, it is important that strong evidence of familial aggregation be established at an early stage of research. In this paper, we discuss several study designs useful to address some issues such as (1) detecting familial aggregation, (2) testing for gene-environment interaction, (3) identifying homogeneous subgroups and (4) measuring magnitude and patterns of familial correlations. These designs include the conventional case-control design and the family case-control design. For each of these two study designs, we discuss analytical strategies as well as their strengths and weaknesses. Throughout, several examples from real studies are used for illustrative purposes. [ABSTRACT FROM AUTHOR]

Statisticians working in psychiatric epidemiology regularly confront a variety of problems that are rare in other branches of epidemiology. These problems range from the frequent absence of 'objective' biological markers and consequent reliance on informant and self-reports of considerable fallability, to the perhaps corresponding frequent use of multivariate statistical methods such as latent variable modelling. Equally, such difficulties can be regarded as challenges that offer the statistician a critical role. [ABSTRACT FROM AUTHOR]

If the broad spectrum of mechanistic research conducted on an environmental carcinogen is to be used in quantifying cancer risks, statisticians must play a key role. Statistical methods are critically needed for a scientifically valid analysis of a complicated series of linked experimental findings. This will require a greater understanding of the underlying biology than is common in statistical consulting, aiding in the development of complicated mechanistically based mathematical descriptions of mean response and in the creation of statistical methods for the estimation of model parameters (e.g. likelihoods) able to use both the underlying model and much of the available data. [ABSTRACT FROM AUTHOR]

Optimal statistical design strategies are applied to toxicokinetic experiments, for determining proper allocations of subjects and/or spacings of sampling times under a variety of nonlinear concentration-time models. The strategies include: (i) optimal allocations of subjects assuming the placement of time points is fixed, (ii) optimal spacing of design time points while assuming an equal allocation of subjects per time points and (iii) allocations/time-point spacings optimized jointly. Emphasis is placed on the first case, where a variance-minimization method is illustrated for optimizing the allocations when estimating specific toxicokinetic parameters. Appeals to forms of D-optimality are also considered, for cases when no specific toxicokinetic parameter is of specialized interest. [ABSTRACT FROM AUTHOR]

We have examined the regression effect and its magnitude under the Gaussian distributional assumption. The impact and implication of regression to the mean on the analysis of medical investigations was discussed. For simplicity, we called the approach adjusting for the regression effect a two-stage procedure and noted its relationship to the analysis of covariance model for comparing treatment groups. We also proposed to examine the correlation structure among repeated measurements in the absence of any external interventions through a model more realistic than the one assuming equal correlations. The proposed structure led us to investigate ways to reduce or eliminate regression effect via study designs when patient selection is inevitable. Two examples were given to help illustrate the discussion in this paper. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including designing of pilot study for evaluation of therapies, methodological developments aimed to improve phase oncology trials, and pilot trials from health technologies to drug trials.

Drug combination is a critically important therapeutic approach for complex diseases such as cancer and HIV due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. One of the key issues is to identify which combinations are additive, synergistic, or antagonistic. While the value of multidrug combinations has been well recognized in the cancer research community, to our best knowledge, all existing experimental studies rely on fixing the dose of one drug to reduce the dimensionality, e.g. looking at pairwise two-drug combinations, a suboptimal design. Hence, there is an urgent need to develop experimental design and analysis methods for studying multidrug combinations directly. Because the complexity of the problem increases exponentially with the number of constituent drugs, there has been little progress in the development of methods for the design and analysis of high-dimensional drug combinations. In fact, contrary to common mathematical reasoning, the case of three-drug combinations is fundamentally more difficult than two-drug combinations. Apparently, finding doses of the combination, number of combinations, and replicates needed to detect departures from additivity depends on dose-response shapes of individual constituent drugs. Thus, different classes of drugs of different dose-response shapes need to be treated as a separate case. Our application and case studies develop dose finding and sample size method for detecting departures from additivity with several common (linear and log-linear) classes of single dose-response curves. Furthermore, utilizing the geometric features of the interaction index, we propose a nonparametric model to estimate the interaction index surface by B-spine approximation and derive its asymptotic properties. Utilizing the method, we designed and analyzed a combination study of three anticancer drugs, PD184, HA14-1, and CEP3891 inhibiting myeloma H929 cell line. To our best knowledge, this is the first ever three drug combinations study performed based on the original 4D dose-response surface formed by dose ranges of three drugs.

Editorial. Comments on the statistical aspects in the design and conduct of intervention trials with a focus on treatment. Importance of the disease prevention to cost effectivity in health; Background studies using experimental trial approach in health sciences; Role of statisticians in the design and conduct of health intervention trials.