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Douglas G Altman

Publications and source records attributed to Douglas G Altman.

90 records · Page 5Linked to original sources
PubMed2002

Meta-analysis combining parallel and cross-over clinical trials. I: Continuous outcomes.

Among clinical trials assessing a given treatment, often parallel and cross-over designs are used together. In the first paper of a series of three, we explore two methods to pool continuous outcomes in a meta-analysis combining parallel and cross-over trial designs: the weighted mean difference (WMD) and the standardized weighted mean difference (SWMD). The combined design meta-analytic formulae are based on a weighted average of the two design treatment estimates. A random effects model can be implemented. Both WMD and SWMD can be used, the choice of the method is determined by the type of outcomes obtained in the trials. Compared to the number of included subjects, the relative weight of the cross-over design is large in combined-design meta-analysis. Differences in the weight estimation between WMD and SWMD can also accentuate the relative weight of cross-over trials, which must be considered a case of design-specific bias.

Blood Pressure↗
PubMed2002

Meta-analysis combining parallel and cross-over clinical trials. II: Binary outcomes.

We examine different methods to pool binary outcomes used both in parallel and cross-over trials. Odds ratio (OR) estimators obtained from joint conditional probabilities in cross-over trials, such as the Mantel-Haenszel and Peto methods, are compared to an OR estimator using marginal results of cross-over trials. When there is correlation between the outcomes in the two cross-over periods, joint conditional ORs differ from marginal ORs and cannot be combined with OR estimates from parallel trials. The marginal OR estimate is independent of the between-period correlation and it includes a correction for cross-over correlation in the variance estimate. As its computation is similar in cross-over and parallel trials, it is the method of choice to pool results from parallel and cross-over trials in a combined design meta-analysis.

Albuminuria↗
PubMed2002

Meta-analysis combining parallel and cross-over clinical trials. III: The issue of carry-over.

In meta-analysis combining results from parallel and cross-over trials, there is a risk of bias originating from the carry-over effect in cross-over trials. When pooling treatment effects estimated from parallel trials and two-period two-treatment cross-over trials, meta-analytic estimators of treatment effect can be obtained from the combination of parallel trial results either with cross-over trial results based on data of the first period only or with cross-over trial results analysed with data from both periods. Taking data from the first cross-over period protects against carry-over but gives less efficient treatment estimators and may lead to selection bias. This study evaluates in terms of variance reduction and mean square error the cost of calculating meta-analysis estimates with data from the first period instead of data from the two cross-over periods. If the information on cross-over sequence is available, we recommend performing two combined design meta-analyses, one using the first cross-over period data and one based on data from both cross-over periods. To investigate simultaneously the statistical significance of these two estimators as well as the carry-over at meta-analysis level, a method based on a multivariate analysis of the meta-analytic treatment effect and carry-over estimates is proposed.

Blood Pressure↗
PubMed2002

Statistical methods for assessing the influence of study characteristics on treatment effects in 'meta-epidemiological' research.

Biases in systematic reviews and meta-analyses may be examined in 'meta-epidemiological' studies, in which the influence of trial characteristics such as measures of study quality on treatment effect estimates is explored. Published studies to date have analysed data from collections of meta-analyses with binary outcomes, using logistic regression models that assume that there is no between- or within-meta-analysis heterogeneity. Using data from a study of publication bias (39 meta-analyses, 394 published and 88 unpublished trials) and language bias (29 meta-analyses, 297 English language trials and 52 non-English language trials), we compare results from logistic regression models, with and without robust standard errors to allow for clustering on meta-analysis, with results using a 'meta-meta-analytic' approach that can allow for between- and within-meta-analysis heterogeneity. We also consider how to allow for the confounding effects of different trial characteristics. We show that both within- and between meta-analysis heterogeneity may be of importance in the analysis of meta-epidemiological studies, and that confounding exists between the effects of publication status and trial quality.

Bias↗
PubMed2002

Poor-quality medical research: what can journals do?

The aim of medical research is to advance scientific knowledge and hence--directly or indirectly--lead to improvements in the treatment and prevention of disease. Each research project should continue systematically from previous research and feed into future research. Each project should contribute beneficially to a slowly evolving body of research. A study should not mislead; otherwise it could adversely affect clinical practice and future research. In 1994 I observed that research papers commonly contain methodological errors, report results selectively, and draw unjustified conclusions. Here I revisit the topic and suggest how journal editors can help.

Peer Review, Research↗
PubMed2002

How statistical expertise is used in medical research.

CONTEXT: Investigation of the nature and frequency of statistician involvement in medical research and its relation to the final editorial decision. METHODS: Authors of original research articles who submitted to BMJ and Annals of Internal Medicine from May through August 2001 were sent a short questionnaire at the time of manuscript submission. Authors were asked if they received assistance from a person with statistical expertise, the nature of any such contribution, and reasons why, if no statistical input was received. RESULTS: The response rate was 75% (704/943); methodological input was reported for 514 (73%) of these papers. In 435 papers (85%), such input was provided by biostatisticians or epidemiologists and, if deemed significant, was typically associated with authorship. A total of 33 of 122 methodologists (27%) whose main contribution started at the analysis stage received neither acknowledgment nor authorship. Research without methodological assistance was more likely to be rejected without review (71% vs 57%; chi(2) = 10.6; P =.001) and possibly less likely to be accepted for publication (7% vs 11%; chi(2) = 2.37; P =.12). CONCLUSIONS: Statistical input to medical research is widely recommended but inconsistently obtained. Individuals providing such expertise are often not involved until the analysis of data and many go unrecognized by either authorship or acknowledgment.

Authorship↗
PubMed2002

Survival plots of time-to-event outcomes in clinical trials: good practice and pitfalls.

Survival plots of time-to-event data are a key component for reporting results of many clinical trials (and cohort studies). However, mistakes and distortions often arise in the display and interpretation of survival plots. This article aims to highlight such pitfalls and provide recommendations for future practice. Findings are illustrated by topical examples and also based on a survey of recent clinical trial publications in four major journals. Specific issues are: should plots go up or down (we recommend up), how far in time to extend the plot, showing the extent of follow-up, displaying statistical uncertainty by including SEs or CIS, and exercising caution when interpreting the shape of plots and the time-pattern of treatment difference.

Clinical Trials as Topic↗
PubMed2002

Quantification of the completeness of follow-up.

Completeness of follow-up is important, especially in clinical trials, since unequal follow-up in the treatment groups can bias the analysis of results. In survival studies, information on participants who do not complete the study is often omitted because their data can be included up to the time at which they were lost to follow-up. We propose a simple measure of completeness that is the ratio of the total observed person-time and the potential person-time of follow-up in a study. Our measure is easy to calculate, can be illustrated pictorially, and can be used to identify subgroups with especially poor follow-up.

Clinical Trials as Topic↗
PubMed2002

Meta-analysis, Simpson's paradox, and the number needed to treat.

BACKGROUND: There is debate concerning methods for calculating numbers needed to treat (NNT) from results of systematic reviews. METHODS: We investigate the susceptibility to bias for alternative methods for calculating NNTs through illustrative examples and mathematical theory. RESULTS: Two competing methods have been recommended: one method involves calculating the NNT from meta-analytical estimates, the other by treating the data as if it all arose from a single trial. The 'treat-as-one-trial' method was found to be susceptible to bias when there were imbalances between groups within one or more trials in the meta-analysis (Simpson's paradox). Calculation of NNTs from meta-analytical estimates is not prone to the same bias. The method of calculating the NNT from a meta-analysis depends on the treatment effect used. When relative measures of treatment effect are used the estimates of NNTs can be tailored to the level of baseline risk. CONCLUSIONS: The treat-as-one-trial method of calculating numbers needed to treat should not be used as it is prone to bias. Analysts should always report the method they use to compute estimates to enable readers to judge whether it is appropriate.

Bias↗
PubMed2002

Reporting on statistical methods to adjust for confounding: a cross-sectional survey.

BACKGROUND: The use of complex statistical models to adjust for confounding is common in medical research. OBJECTIVE: To determine the frequency and adequacy of adjustment for confounding in medical articles. DESIGN: Cross-sectional survey. SETTING: 34 scientific medical journals with a high impact factor. MEASUREMENTS: Frequency of reporting on methods used to adjust for confounding in 537 original research articles published in January 1998. RESULTS: Of the 537 articles, 169 specified that adjustment for confounding was used. In 1 paper in 10, it was unclear which statistical method was used or for which variables adjustment was made. In 45% of papers, it was not clear how multicategory or continuous variables were treated in the analysis. Inadequate reporting was less frequent if an author was affiliated with a department of statistics, epidemiology, or public health and if articles were published in journals with a high impact factor. CONCLUSIONS: Details of methods used to adjust for confounding are frequently not reported in original research articles.

Confounding Factors, Epidemiologic↗
PubMed2002

Meta-analyses involving cross-over trials: methodological issues.

BACKGROUND: Meta-analysis of randomized controlled trials (RCTs) is usually based on trials where patients are randomized individually into two different, parallel, treatment groups. This paper concentrates on RCTs of a different design-two-period, two-treatment cross-over trials. METHODS: The characteristics of these trials are outlined, with detailed examples of methods for analysis for both continuous and binary data. These case studies are then extended into the context of a meta-analysis. The Cochrane Library was surveyed to assess current practice for synthesis. RESULTS: Methods are described for continuous and binary data for use both when the necessary paired data are given and also when they need to be calculated or imputed, and some suggestions are provided to help people wishing to synthesize data from cross-over trials into meta-analyses. The survey suggested that about 8% of the trials in the Cochrane library were cross-over trials and 18% of the reviews referred to such trials, although there was no consistent approach to their inclusion into the reviews. CONCLUSIONS: Methods do exist for including valuable information from two-period, two-treatment cross-over trials into quantitative reviews. However, poor reporting of cross-over trials will often impede attempts to perform a meta-analysis using the available methods.

Cross-Over Studies↗
PubMed2002

Guidelines for the design and statistical analysis of experiments using laboratory animals.

For ethical and economic reasons, it is important to design animal experiments well, to analyze the data correctly, and to use the minimum number of animals necessary to achieve the scientific objectives---but not so few as to miss biologically important effects or require unnecessary repetition of experiments. Investigators are urged to consult a statistician at the design stage and are reminded that no experiment should ever be started without a clear idea of how the resulting data are to be analyzed. These guidelines are provided to help biomedical research workers perform their experiments efficiently and analyze their results so that they can extract all useful information from the resulting data. Among the topics discussed are the varying purposes of experiments (e.g., exploratory vs. confirmatory); the experimental unit; the necessity of recording full experimental details (e.g., species, sex, age, microbiological status, strain and source of animals, and husbandry conditions); assigning experimental units to treatments using randomization; other aspects of the experiment (e.g., timing of measurements); using formal experimental designs (e.g., completely randomized and randomized block); estimating the size of the experiment using power and sample size calculations; screening raw data for obvious errors; using the t-test or analysis of variance for parametric analysis; and effective design of graphical data.

Animal Welfare↗
PubMed2002

Charts of fetal size: limb bones.

OBJECTIVE: To construct new size charts for all fetal limb bones. DESIGN: A prospective, cross sectional study. SETTING: Ultrasound department of a large hospital. SAMPLE: 663 fetuses scanned once only for the purpose of the study at gestations between 12 and 42 weeks. METHODS: Centiles were estimated by combining separate regression models fitted to the mean and standard deviation, assuming that the measurements have a normal distribution at each gestational age. MAIN OUTCOME MEASURES: Determination of fetal limb lengths from 12 to 42 weeks of gestation. RESULTS: Size charts for fetal bones (radius, ulna, humerus, tibia, fibula, femur and foot) are presented and compared with previously published data. CONCLUSIONS: We present new size charts for fetal limb bones which take into consideration the increasing variability with gestational age. We have compared these charts with other published data; the differences seen may be largely due to methodological differences. As standards for fetal head and abdominal measurements have been published from the same population, we suggest that the use of the new charts may facilitate prenatal diagnosis of skeletal dysplasias.

Bones of Upper Extremity↗
PubMedDate not supplied

Epidemiology and reporting of randomised trials published in PubMed journals.

Although randomised trials are important for evidence-based medicine, little is known about their overall characteristics. We assessed the epidemiology and reporting of methodological details for all 519 PubMed-indexed randomised trials published in December, 2000 (383 [74%] parallel-group, 116 [22%] crossover). 482 (93%) were published in specialty journals. A median of 80 participants (10th-90th percentile 25-369) were recruited for parallel-group trials. 309 (60%) were blinded. Power calculation, primary outcomes, random sequence generation, allocation concealment, and handling of attrition were each adequately described in less than half of publications. The small sample sizes are worrying, and poor reporting of methodological characteristics will prevent reliable quality assessment of many published trials.

Publishing↗