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Effects of cost sharing on physician utilization under favourable conditions for supplier-induced demand.

The effects of cost sharing on the demand for ambulatory care in experimental circumstances are well understood since the Rand Health Insurance Experiment (HIE). However, in a non-experimental real-world context, supplier-induced demand of doctors might erode some of the significant negative out-of-pocket price elasticity identified in the HIE. Belgium is an interesting test case for this hypothesis because it has relatively high rates of patient cost sharing in its public health insurance system and a very high density of physicians, all remunerated fee-for-service. We have exploited the price variation generated by a substantial increase in patient co-payment rates in 1994 to estimate out-of-pocket price elasticities for three groups of users, and for three types of services using a fixed-effects model in levels and in differences. We obtain significant out-of-pocket price elasticities for the general population in the range from -0.39 to -0.28 for GP home visits, -0.16 to -0.12 for GP office visits and -0.10 for specialist visits. The estimates were generally lower and less significant for the groups of elderly and disabled. The differences we find in price responsiveness appear to be fairly robust and consistent with the HIE predictions. These results suggest that--at least in the short run--non-experimental utilization effects of cost sharing are very similar to the experimental evidence, even in a situation of favourable conditions for supplier-induced demand.

Ambulatory Care↗

Nursing intervention studies: a descriptive analysis of issues important to clinicians.

When reading a report of an intervention study, clinicians are interested in knowing: whether the intervention is effective, with whom it is effective, how much benefit it produces, and whether associated, adverse outcomes occur. Recommendations have been made in the research literature regarding how to conduct and report intervention studies so as to produce knowledge regarding these questions. This descriptive study was conducted to estimate the frequency with which these recommendations are being used in nursing intervention studies. Data pertinent to five research questions were extracted from 84 experimental and quasi-experimental study reports published between 1998 and 2000. Seventeen percent of the studies used a design that could statistically test for variation in intervention effect depending on the level of an individual characteristic. However, a test of interaction was actually conducted in only 8% of the studies. The magnitude of the intervention's effect was addressed in 38% of the study reports. Providing the proportion of persons in the intervention group who attained a discrete outcome was the most frequently used way of showing intervention magnitude. Associated, adverse outcomes were examined in 23% of the studies, and were most often measured as continuous variables. The low level of use of recommended methods leads the author to suggest dialogue between clinicians and researchers to determine if intervention studies are being conducted and reported in ways that produce knowledge that is useful to clinicians.

Data Collection↗

Meta-analysis of diabetes patient education research: variations in intervention effects across studies.

Data from a previously reported meta-analysis of diabetes patient education literature were reanalyzed to determine the influence of study/subject characteristics, such as study quality and age of subjects, on patient outcomes. Patient knowledge and self-management skills, weight loss, glycosylated hemoglobin levels, and psychological outcomes were analyzed as outcome variables. Seventy-three relevant published and unpublished studies were located. Patient education appeared to be more effective in younger patients, particularly for the knowledge outcome. For all patients, glycosylated hemoglobin levels improved between 1 and 6 months postintervention, but decreased to 1-month levels after 6 months. Length of the educational intervention did not appear to influence outcomes. More rigorous, experimental research designs tended to produce more conservative effect size estimates. Implications for diabetes patient education are discussed.

Adult↗

The qualitative research audit trail: a complex collection of documentation.

A qualitative study typically involves a large volume of researcher-generated data, including notes about the context of the study, methodological decisions, data analysis procedures, and self-awareness of the researcher. Such data are important in many aspects of the study, particularly in the development of an audit trail to substantiate trustworthiness. Unfortunately, there is little information available to assist researchers in generating the needed documentation. In this article, we discuss the types of data that contribute to credible investigations. Strategies for maintaining effective records in qualitative studies are included, along with examples from our own research.

Data Collection↗

All-cause mortality associated with atypical and typical antipsychotics in demented outpatients.

PURPOSE: To estimate the association between use of typical and atypical antipsychotics and all-cause mortality in a population of demented outpatients. METHODS: The study cohort comprised all demented patients older than 65 years and registered in the Integrated Primary Care Information (IPCI) database, during 1996-2004. First, mortality rates were calculated during use of atypical and typical antipsychotics. Second, we assessed the association between use of atypical and typical antipsychotics and all-cause mortality through a nested case-control study in the cohort of demented patients. Each case was matched to all eligible controls at the date of death by age and duration of dementia. Odds ratios were estimated through conditional logistic regression analyses. RESULTS: The crude mortality rate was 30.1 (95%CI: 18.2-47.1) and 25.2 (21.0-29.8) per 100 person-years (PY) during use of atypical and typical antipsychotics, respectively. No significant difference in risk of death was observed between current users of atypical and typical antipsychotics (OR = 1.3; 95%CI: 0.7-2.4). Both types of antipsychotics were associated with a significantly increased risk of death as compared to non-users (OR = 2.2, 1.2-3.9 for atypical antipsychotics; OR=1.7, 1.3-2.2 for typical antipsychotics). CONCLUSIONS: Conventional antipsychotic drug should be included in the FDA's Public Health advisory, which currently warns only of the increased risk of death with the use of atypical antipsychotics in elderly demented persons.

Age Factors↗

Comparison of stratification and adaptive methods for treatment allocation in an acute stroke clinical trial.

Achieving balance on prognostic factors between treatment groups in a clinical trial is important to ensure that any observed treatment effect may be attributed to the treatment itself. Improving the balance on prognostic factors also potentially increases the statistical power attained in a trial. Substantial imbalances may occur by chance if simple randomization is used. Allocation of the treatment according to stratified random blocks based on clinical features is the conventional approach to obtain treatment groups that are as similar as possible. An alternative approach, known as minimization (or more generally as adaptive stratification), has also been proposed. We assessed the feasibility of adaptive stratification in the context of a clinical trial of insulin to control plasma glucose level following acute stroke. We determined suitable settings for the parameters in the adaptive stratification procedure by simulation studies. Specifically, we assessed: the optimal probability for allocating a patient to the preferred (leading to least imbalance on prognostic factors) treatment group; the number of variables that could be incorporated in the adaptive stratification algorithm; the weighting that should be given to each variable; and whether interactions between variables should be included. We then compared the statistical power, across a range of simulated treatment effects, between trials where treatments were allocated by stratified random blocks and by adaptive stratification. Finally, we considered the importance of the method of analysis in realizing the gain in power which may potentially be achieved by allocating treatments using stratified random blocks or adaptive stratification.

Aged↗

Adjustments to the Mantel-Haenszel test for data from stratified multistage surveys.

The usual form of the Mantel-Haenszel test statistic assumes independent observations. This is inappropriate for data from a stratified multistage survey. Two alternative adjustments to the test statistic are developed to deal with this: (a) a modification of the effective sample size for each row of each table, using the design effects, extending a method proposed by Donald and Donner for familial aggregation studies; (b) a Taylor series approximation to the variance of the square root of the numerator of the Mantel-Haenszel statistic. Both methods are evaluated by application to both simulated and real data. The two methods perform equally well, and offer a considerable improvement over the unadjusted test statistic when observations from the same cluster are highly correlated. A simplified adjustment is also considered, as is the need for correction to the variance of the odds ratio estimator.

Chi-Square Distribution↗

Analysis of aberrations in public health surveillance data: estimating variances on correlated samples.

The detection of unusual patterns in health data presents an important challenge to health workers interested in early identification of epidemics or important risk factors. A useful procedure for detection of aberrations is the ratio of a current report to some historic baseline. This work addresses the problem of finding the variance of such a ratio when the surveillance reports are correlated. Results show that, when estimating this variance or the variance of the sample mean from a series of observations with an estimated correlation structure, bootstrap and jackknife estimates may be overly optimistic. The delta method or a classical method may be more useful when such model dependence is inappropriate.

Analysis of Variance↗

Effects of mid-point imputation on the analysis of doubly censored data.

Doubly censored data arise in some cohort studies of the AIDS incubation period because the time of infection may be known only up to an interval defined by two successive screening tests for HIV antibody. A simple analytic approach is to impute the infection time by the mid-point of the interval and then apply standard survival techniques for right censored data. The objective of this paper is to investigate the statistical properties of such a mid-point imputation approach. We investigated the asymptotic bias of the Kaplan-Meier estimate, coverage probabilities of associated confidence intervals, bias in hazard ratio, and the size of the logrank test. We show that the statistical properties of mid-point imputation depend strongly on the underlying distributions of infection times and the incubation periods, and the width of the interval between screening tests. In the absence of treatment, the median incubation period of HIV infection is approximately 10 years, and we conclude that, for this situation, mid-point imputation is a reasonable procedure for interval widths of 2 years or less.

Bias↗

The effect of matching on the power of randomized community intervention studies.

Currently, there is considerable interest in studies that use the community as the experimental unit. Health promotion programmes are one example. Because such activities are expensive, the number of experimental units (communities) is usually very small. Investigators often match communities on demographic variables in order to improve the power of their studies. Matching is known to improve power in certain circumstances. However, we show here that if the number of communities is small, the matched design will probably have less power than the unmatched design. This is due primarily to the loss of degrees of freedom in the matched design, which outweighs the benefits of matching on any but the strongest correlates of changes in behaviour. In the community intervention situation, even small differences in sample size between the matched and unmatched analyses can have expensive consequences.

Bias↗

A modelling approach to the analysis of menstrual diary data.

In clinical trials to compare contraceptives, women are usually asked to record whether or not each day is a bleeding day over the duration of the trial. In this paper we describe how parametric models, which include terms corresponding to covariates recorded for each woman, can be used to analyse data on the occurrence of certain adverse events identified from the diary record. Linear logistic models are used to analyse the probability of prolonged bleeding or amenorrhoea, and log-linear models are used to analyse the lengths of bleeding episodes. In both cases variation between women is allowed for by including a random effect in the model. The application of our methods is illustrated using a data base made available by the World Health Organization.

Bias↗

A case for Bayesianism in clinical trials.

This paper describes a Bayesian approach to the design and analysis of clinical trials, and compares it with the frequentist approach. Both approaches address learning under uncertainty. But they are different in a variety of ways. The Bayesian approach is more flexible. For example, accumulating data from a clinical trial can be used to update Bayesian measures, independent of the design of the trial. Frequentist measures are tied to the design, and interim analyses must be planned for frequentist measures to have meaning. Its flexibility makes the Bayesian approach ideal for analysing data from clinical trials. In carrying out a Bayesian analysis for inferring treatment effect, information from the clinical trial and other sources can be combined and used explicitly in drawing conclusions. Bayesians and frequentists address making decisions very differently. For example, when choosing or modifying the design of a clinical trial, Bayesians use all available information, including that which comes from the trial itself. The ability to calculate predictive probabilities for future observations is a distinct advantage of the Bayesian approach to designing clinical trials and other decisions. An important difference between Bayesian and frequentist thinking is the role of randomization.

Bayes Theorem↗

The role of p-values in analysing trial results.

The current widespread practice of using p-values as the main means of assessing and reporting the results of clinical trials cannot be defended. Reasons for grave concern over the present situation range from the unsatisfactory nature of p-values themselves, their very common misunderstanding by statisticians as well as by clinicians and their serious distorting influence on our perception of the very nature of clinical trials. It is argued, however, that only by fully understanding the reasons why they have become so universally popular can we hope to change opinion and introduce more sensible ways of summarizing and reporting results. Some of the ways in which this might happen are discussed.

Bayes Theorem↗

Comparison of two tests useful in situations where treatment is expected to increase variability relative to controls.

The type I error and power characteristics of the modified t test were compared with those of the generalized t test. Results suggested that, in contrast to the generalized t test, the modified t test can be seriously non-robust to departures from population normality, with such departures often producing anti-conservative results. Neither test held an absolute power advantage over the other when responses were from normal distributions, but the modified t test was generally more powerful for these conditions. In comparison with the pooled samples t test, both tests were usually much more efficient when treatment caused increases both in mean response and between-subject variance, and suffered only small disadvantages when between-subject variance was unchanged by treatment. Given these results and other considerations, recommendations for use of these recently devised tests are given.

Bias↗

A comparative study of several antibiotic formulations using a design based on a combination of balanced incomplete blocks and Latin squares.

A practical application of an experimental design, suitable for the comparison of several treatments, and based on combining balanced incomplete blocks and Latin squares balanced for carryover effects, is presented in the context of comparing a number of paediatric antibiotic formulations for taste, smell and colour. The recommended designs originally suggested by Patterson, have the advantage of balanced incomplete blocks, in that a single trial may be used to compare a larger number of treatments than may reasonably be given to any individual subject. In addition, the incorporation of suitably chosen Latin squares allows for assessment of any effect of order of presentation of the treatments and for any simple first-order carryover effect of one treatment into the following treatment period. Inclusion of such effects in the overall analysis could result in the reduction of bias in the comparisons of the treatments.

Analysis of Variance↗