BMC Medical Research Methodology - Latest articles

Balance algorithm for cluster randomized trials

Ben R Carter and Kerenza Hood — 2008-10-09

Background: Within cluster randomized trials no algorithms exist to generate a full enumeration of a block randomization, balancing for covariates across treatment arms. Furthermore, often for practical reasons multiple blocks are required to fully randomize a study, which may not have been well balanced within blocks. Results: We present a convenient and easy to use randomization tool to undertake allocation concealed block randomization. Our algorithm highlights allocations that minimize imbalance between treatment groups across multiple baseline covariates. We demonstrate the algorithm using a cluster randomized trial in primary care (the PRE-EMPT Study) and show that the software incorporates a trade off between independent random allocations that were likely to be imbalanced, and predictable deterministic approaches that would minimise imbalance. We extend the methodology of single block randomization to allocate to multiple blocks conditioning on previous allocations. Conclusions: The algorithm is included in the supplementary data and we advocate its use for robust randomization within cluster randomized trials.

Intervention, recruitment and evaluation challenges in the Bangladeshi community: Experience from a peer lead educational course.

S M Choudhury, S Brophy, M A Fareedi, B Zamen, P Ahmed and Drr Williams — 2008-10-09

Background: The incidence of Type 2 diabetes is increasing worldwide and diabetes is four times more common among ethnic minority groups than among the general Caucasian population. This study reflects on the specific issues of engaging people and evaluating interventions through written questionnaires within older ethnic minority groups. Methods: The original protocol set out to evaluate an adapted version of the X-PERT patient program (http://www.xpert-diabetes.org.uk/) using questionnaires and interviews. Results: Questionnaires, even verbally completed, were unsuccessful and difficult to administer as participants found the questionnaire structure and design difficult to follow and did not perceive any benefit to completing the questionnaires. The benefits of attending the course were also poorly understood by participants and in many cases people participated in coming to the course as a favour to the researcher. Engaging participants required word of mouth and the involvement of active members of the community. Conclusions: Peer led courses and their evaluation in older ethnic minority communities needs a very different approach for that in younger Caucasian patients. A structured approached to evaluation (favoured by western educational system) is inappropriate. Engaging participants is difficult and the employment of local well known people is essential.

Reliability and validity of the international physical activity questionnaire in the Nord-Trondelag health study (HUNT) population of men

Nanna Kurtze, Vegar Rangul and Bo-Egil Hustvedt — 2008-10-09

Background: There is no standardized method for the assessment of physical activity (PA). Therefore it is important to investigate the validity and comparability of different measures. The International Physical Activity Questionnaire (IPAQ) has been developed as an instrument for cross-national assessment of PA and has been validated in 12 countries. These instruments have acceptable measurement properties for monitoring population levels of PA among 18-65 year-old adults in diverse settings. However, there are some concerns that IPAQ may over-report PA. The purpose of this study is to evaluate the reliability and validity of IPAQ, short version, last 7-days in the Nord-Trondelag Health Study (HUNT) population of men. Methods: The questionnaire was administered twice to a random sample of 108 men aged 20-39 and validity by comparing results with VO2max and ActiReg, an instrument that measures PA and energy expenditure (EE). ActiReg discriminates between the body positions: stand, sit, bend forward and lie and also registers if there is motion or not in each of them or both. Results: Our results for reliability of the IPAQ short version were good for vigorous and fair for moderate activities. Intraclass correlations ranged from a low of 0.30 for moderate activity hours, to a high of 0.80 for sitting hours. Concerning validity, our results suggest that total IPAQ vigorous PA was a moderately good measure of vigorous activity, having moderately strong, significant correlations with VO2max, r = 0.41 (p[less than or equal to]0.01), but correlated not with metabolic equivalent (METs) values of 6 or more measured with ActiReg. Only total IPAQ walking was fair correlated with METs 1-3 and METs 3-6, respectively r = -0.27 and 0.26 (p[less than or equal to]0.05). The index for IPAQ sitting hours per week was moderate correlated with METs values of 1-3 and negatively correlated with METs values of 3-6. Classification of PA in three levels (low, moderate and high) correlated also most strongly with VO2max (0.31 p0.01) and METs 3-6 and METs 1-3 from ActiReg (r = 0.32 and -0.31, p0.01). Classification of BMI in three levels (normal, overweight and obese) correlated most strongly negative with VO2max (-0.42 p0.01) and MJ from ActiReg (r = 0.31 p0.01). Conclusions: Our results indicate that IPAQ short version for men has acceptable reliability and criterion validity for vigorous activity and sitting. Walking has moderate reliability. Only the IPAQ for walking had a fair correlation with METs 6+. The questions about moderate activity had fair reproducibility and correlated poorly with most comparison measures.

The HCV Synthesis Project: Scope, methodology, and preliminary results

2008-09-14

Background: The hepatitis C virus (HCV) is hyper-endemic in injecting drug users. There is also excess HCV among non-injection drug users who smoke, snort, or sniff heroin, cocaine, crack, or methamphetamine. Methods: To summarize the research literature on HCV in drug users and identify gaps in knowledge, we conducted a synthesis of the relevant research carried out between 1989 and 2006. Using rigorous search methods, we identified and extracted data from published and unpublished reports of HCV among drug users. We designed a quality assurance system to ensure accuracy and consistency in all phases of the project. We also created a set of items to assess study design quality in each of the reports we included. Results: We identified 629 reports containing HCV prevalence rates, incidence rates and/or genotype distribution among injecting or non-injecting drug user populations published between January 1989 and December 2006. The majority of reports were from Western Europe (41%), North America (26%), Asia (11%) and Australia/New Zealand (10%). We also identified reports from Eastern Europe, South America, the Middle East, and the Caribbean. The number of publications reporting HCV rates in drug users increased dramatically between 1989 and 2006 to 27–52 reports per year after 1998. Conclusion: The data collection and quality assurance phases of the HCV Synthesis Project have been completed. Recommendations for future research on HCV in drug users have come out of our data collection phase. Future research reports can enhance their contributions to our understanding of HCV etiology by clearly defining their drug user participants with respect to type of drug and route of administration. Further, the use of standard reporting methods for risk factors would enable data to be combined across a larger set of studies; this is especially important for HCV seroconversion studies which suffer from small sample sizes and low power to examine risk factors.

Data management for prospective research studies using SAS® software

Robin L Kruse and David R Mehr — 2008-09-11

Background: Maintaining data quality and integrity is important for research studies involving prospective data collection. Data must be entered, erroneous or missing data must be identified and corrected if possible, and an audit trail created. Methods: Using as an example a large prospective study, the Missouri Lower Respiratory Infection (LRI) Project, we present an approach to data management predominantly using SAS software. The Missouri LRI Project was a prospective cohort study of nursing home residents who developed an LRI. Subjects were enrolled, data collected, and follow-ups occurred for over three years. Data were collected on twenty different forms. Forms were inspected visually and sent off-site for data entry. SAS software was used to read the entered data files, check for potential errors, apply corrections to data sets, and combine batches into analytic data sets. The data management procedures are described. Results: Study data collection resulted in over 20,000 completed forms. Data management was successful, resulting in clean, internally consistent data sets for analysis. The amount of time required for data management was substantially underestimated. Conclusion: Data management for prospective studies should be planned well in advance of data collection. An ongoing process with data entered and checked as they become available allows timely recovery of errors and missing data.

Industry-supported meta-analyses compared with meta-analyses with non-profit or no support: Differences in methodological quality and conclusions

2008-09-09

Background: Studies have shown that industry-sponsored meta-analyses of drugs lack scientific rigour and have biased conclusions. However, these studies have been restricted to certain medical specialities. We compared all industry-supported meta-analyses of drug-drug comparisons with those without industry support. Methods: We searched PubMed for all meta-analyses that compared different drugs or classes of drugs published in 2004. Two authors assessed the meta-analyses and independently extracted data. We used a validated scale for judging the methodological quality and a binary scale for judging conclusions. We divided the meta-analyses according to the type of support in 3 categories: industry-supported, non-profit support or no support, and undeclared support. Results: We included 39 meta-analyses. Ten had industry support, 18 non-profit or no support, and 11 undeclared support. On a 0–7 scale, the median quality score was 6 for meta-analyses with non-profit or no support and 2.5 for the industry-supported meta-analyses (P < 0.01). Compared with industry-supported meta-analyses, more meta-analyses with non-profit or no support avoided bias in the selection of studies (P = 0.01), more often stated the search methods used to find studies (P = 0.02), searched comprehensively (P < 0.01), reported criteria for assessing the validity of the studies (P = 0.02), used appropriate criteria (P = 0.04), described methods of allocation concealment (P = 0.05), described methods of blinding (P = 0.05), and described excluded patients (P = 0.08) and studies (P = 0.15). Forty percent of the industry-supported meta-analyses recommended the experimental drug without reservations, compared with 22% of the meta-analyses with non-profit or no support (P = 0.57).In a sensitivity analysis, we contacted the authors of the meta-analyses with undeclared support. Eight who replied that they had not received industry funding were added to those with non-profit or no support, and 3 who did not reply were added to those with industry support. This analysis did not change the results much. Conclusion: Transparency is essential for readers to make their own judgment about medical interventions guided by the results of meta-analyses. We found that industry-supported meta-analyses are less transparent than meta-analyses with non-profit support or no support.

Alternative regression models to assess increase in childhood BMI

Andreas Beyerlein, Ludwig Fahrmeir, Ulrich Mansmann and André M Toschke — 2008-09-08

Background: Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods: Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results: GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion: GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

Pooling overdispersed binomial data to estimate event rate

Yinong Young-Xu and K Arnold Chan — 2008-08-19

Background: The beta-binomial model is one of the methods that can be used to validly combine event rates from overdispersed binomial data. Our objective is to provide a full description of this method and to update and broaden its applications in clinical and public health research. Methods: We describe the statistical theories behind the beta-binomial model and the associated estimation methods. We supply information about statistical software that can provide beta-binomial estimations. Using a published example, we illustrate the application of the beta-binomial model when pooling overdispersed binomial data. Results: In an example regarding the safety of oral antifungal treatments, we had 41 treatment arms with event rates varying from 0% to 13.89%. Using the beta-binomial model, we obtained a summary event rate of 3.44% with a standard error of 0.59%. The parameters of the beta-binomial model took the values of 1.24 for alpha and 34.73 for beta. Conclusion: The beta-binomial model can provide a robust estimate for the summary event rate by pooling overdispersed binomial data from different studies. The explanation of the method and the demonstration of its applications should help researchers incorporate the beta-binomial method as they aggregate probabilities of events from heterogeneous studies.

Sampling 'hard-to-reach' populations in health research: yield from a study targeting Americans living in Canada

2008-08-18

Background: Some populations targeted in survey research can be hard to reach, either because of lack of contact information, or non-existent databases to inform sampling. Here, we present a methodological "case-report" of the yield of a multi-step survey study assessing views on health care among American emigres to Canada, a hard-to-reach population. Methods: To sample this hard-to-reach population, we held a live media conference, supplemented by a nation-wide media release announcing the study. We prepared an 'op-ed' piece describing the study and how to participate. We paid for advertisements in 6 newspapers. We sent the survey information to targeted organizations. And lastly, we asked those who completed the web survey to send the information to others. We use descriptive statistics to document the method's yield. Results: The combined media strategies led to 4 television news interviews, 10 newspaper stories, 1 editorial and 2 radio interviews. 458 unique individuals accessed the on-line survey, among whom 310 eligible subjects provided responses to the key study questions. Fifty-six percent reported that they became aware of the survey via media outlets, 26% by word of mouth, and 9% through both the media and word of mouth. Conclusion: Our multi-step communication method yielded a sufficient sample of Americans living in Canada. This combination of paid and unpaid media exposure can be considered by others as a unique methodological approach to identifying and sampling hard-to-reach populations.

Performing meta-analysis with incomplete statistical information in clinical trials

Jianbing Ma, Weiru Liu, Anthony Hunter and Weiya Zhang — 2008-08-18

Background: Results from clinical trials are usually summarized in the form of sampling distributions. When full information (mean, SEM) about these distributions is given, performing meta-analysis is straightforward. However, when some of the sampling distributions only have mean values, a challenging issue is to decide how to use such distributions in meta-analysis. Currently, the most common approaches are either ignoring such trials or for each trial with a missing SEM, finding a similar trial and taking its SEM value as the missing SEM. Both approaches have drawbacks. As an alternative, this paper develops and tests two new methods, the first being the prognostic method and the second being the interval method, to estimate any missing SEMs from a set of sampling distributions with full information. A merging method is also proposed to handle clinical trials with partial information to simulate meta-analysis. Methods: Both of our methods use the assumption that the samples for which the sampling distributions will be merged are randomly selected from the same population. In the prognostic method, we predict the missing SEMs from the given SEMs. In the interval method, we define intervals that we believe will contain the missing SEMs and then we use these intervals in the merging process. Results: Two sets of clinical trials are used to verify our methods. One family of trials is on comparing different drugs for reduction of low density lipprotein cholesterol (LDL) for Type-2 diabetes, and the other is about the effectiveness of drugs for lowering intraocular pressure (IOP). Both methods are shown to be useful for approximating the conventional meta-analysis including trials with incomplete information. For example, the meta-analysis result of Latanoprost versus Timolol on IOP reduction for six months provided in [1] was 5:05+/-1.15 (Mean+/-SEM) with full information. If the last trial in this study is assumed to be with partial information, the traditional analysis method for dealing with incomplete information that ignores this trial would give 6:49 +/- 1:36 while our prognostic method gives 5:02+/-1.15, and our interval method provides two intervals as Mean [4:25; 5:63] and SEM [1:01; 1:24]. Conclusions: Both the prognostic and the interval methods are useful alternatives for dealing with missing data in meta-analysis. We recommend clinicians to use the prognostic method to predict the missing SEMs in order to perform meta-analysis and the interval method for obtaining a more cautious result.

Engaging participants in a complex intervention trial in Australian General Practice

2008-08-13

Background: The paper examines the key issues experienced in recruiting and retaining practice involvement in a large complex intervention trial in Australian General Practice. Methods: Reflective notes made by research staff and telephone interviews with staff from general practices which expressed interest, took part or withdrew from a trial of a complex general practice intervention. Results: Recruitment and retention difficulties were due to factors inherent in the demands and context of general practice, the degree of engagement of primary care organisations (Divisions of General Practice), perceived benefits by practices, the design of the trial and the timing and complexity of data collection. Conclusion: There needs to be clearer articulation to practices of the benefits of the research to participants and streamlining of the design and processes of data collection and intervention to fit in with their work practices. Ultimately deeper engagement may require additional funding and ongoing participation through practice research networks.Trial RegistrationCurrent Controlled Trials ACTRN12605000788673