The challenge of a 2-year follow-up after intervention for weight loss in primary care.

BACKGROUND: Many weight loss programmes show short-term success, but long-term data in larger studies are scarce, especially in community settings. Attrition is common and complicates the interpretation of long-term outcomes. OBJECTIVE: To investigate 2-year outcomes and explore issues of attrition and missing data. SUBJECTS: A total of 772 overweight and obese adults recruited by primary care practices in Australia, Germany and the UK and randomised to a 12-month weight loss intervention delivered in a commercial programme (CP) or in standard care (SC). MEASUREMENT: Weight change from 0-24 and 12-24 months including measured weights only and measured and self-reported weights, using last observation carried forward (LOCF), baseline observation carried forward (BOCF), completers-only and missing-at-random (MAR) analyses. RESULTS: A total of 203 participants completed the 24-month visit. Using measured weights only, there was a trend for greater 24-month weight loss in CP than in SC, but the difference was only statistically significant in the LOCF and BOCF analyses: LOCF: -4.14 vs -1.99 kg, difference adjusted for centre -2.08 kg, P<0.001; BOCF: -1.33 vs -0.74 kg, adjusted difference -0.60 kg, P=0.032; completers: -4.76 vs -2.99 kg, adjusted difference -1.53 kg, P=0.113; missing at random: -3.00 vs -1.94 kg, adjusted difference -1.04 kg, P=0.150. Both groups gained weight from 12-24 months and weight regain was significantly (P<0.001) greater for CP than for SC in all analysis approaches. Inclusion of self-reported weights from a further 138 participants did not change the interpretation of the findings. CONCLUSION: Initial weight loss was poorly maintained during the no-intervention follow-up, but both groups did have lower weight over the 24 months. Attrition was high in both groups, and assumptions about missing data had considerable impact on the magnitude and statistical significance of treatment effects. It is vital that trials on weight loss interventions consider the plausibility of these differences in an analytical approach when interpreting research findings and comparing data between studies.

Two-stage designs optimal under the alternative hypothesis for phase II cancer clinical trials.

The Simon two-stage optimal design is often used for phase II cancer clinical trials. A study proceeds to the second stage unless the null hypothesis, that the true tumour response rate is below some specified value, is already accepted at the end of stage one. The conventional optimal design, for given type 1 and type 2 error rates, is the one which minimises the expected sample size under the null hypothesis. However, at least some new agents are active, and designs that explicitly address this possibility should be considered. We therefore investigate novel designs which are optimal under the alternative hypothesis, that the tumour response rate is higher than the null hypothesis value, and also designs which allow early stopping for efficacy. We make available, software for identifying the corresponding optimal and minimax designs. Considerable savings in expected sample sizes can be achieved if the alternative hypothesis is in fact true, without sample sizes suffering too much if the null hypothesis is true. We present an example discussing the merits of different designs in a practical context. We conclude that it is relevant to consider optimal designs under a range of hypotheses about the true response rate, and that allowing early stopping for efficacy is always advantageous in terms of expected sample size.

A prospective analysis of dietary energy density at age 5 and 7 years and fatness at 9 years among UK children.

OBJECTIVE: To analyse whether high dietary energy density (DED) is associated with increased fat mass and risk of excess adiposity in free-living children. DESIGN: Longitudinal, observational cohort study. SUBJECTS: Six hundred and eighty-two healthy children from the Avon Longitudinal Study of Parents and Children. MEASUREMENTS: Diet was assessed at age 5 and 7 years using 3-day diet diaries, and DED (kJ g(-1)) was calculated excluding drinks. Fat mass was estimated at age 9 years using Dual-Energy X-ray Absorptiometry. To adjust for body size, fat mass index (FMI) was calculated by dividing fat mass (kg) by height (m(5.8)). Excess adiposity was defined as the top quintile of logFMI. RESULTS: Mean DED at age 5 years was higher among children with excess adiposity at age 9 years compared to the remaining sample (8.8+/-0.16 vs 8.5+/-0.07 kJ g(-1)), but there was no evidence of an association with excess adiposity at age 9 years (odds ratio (OR)=1.14, 95% confidence interval (CI) 0.90-1.44) after controlling for potential confounders. Mean DED at age 7 years was higher among children with excess adiposity compared to the remaining sample (9.1+/-0.12 vs 8.8+/-0.06 kJ g(-1)) and a 1 kJ g(-1) rise in DED increased the odds of excess adiposity at 9 years by 36% (OR=1.36, 95% CI 1.09-1.69) after controlling for potential confounders. CONCLUSION: Higher DED at age 7 years, but not age 5 years, is a risk factor for excess adiposity at age 9 years, perhaps reflecting deterioration in the ability to compensate for extra calories in an energy-dense diet. DED tracks strongly from age 5 to 7 years suggesting intervention to alter dietary habits need to commence at younger ages to prevent the formation of preferences for energy dense foods.

Stepwise haplotype analysis: are LD patterns repeatable?

A variety of techniques exist to describe and depict patterns of pairwise linkage disequilibrium (LD). In the current paper, a new log-linear framework is proposed for the summarisation of local interactions among single nucleotide polymorphisms (SNPs). Our approach provides a straightforward means of capturing the diversity of higher-order LD relationships for small numbers of loci by investigating inter-marker interactions. Our method was applied to a dataset of 76 SNP markers spanning a genomic interval of length 2.8 megabases. The analysis of three short sub-regions is described in detail here. Model and graphical representations of contiguous markers in medium to high LD are presented. In the regions studied, evidence for sub-structure was detected, supporting the view that the genomic reality is complex. Interestingly, a critical evaluation of the method by bootstrapping showed that while some LD relationships were captured in a highly repeatable fashion, the majority were not. Large numbers of small interactions, both direct and indirect, mean that many models can adequately summarise the data at hand. Our results suggest that repeatability should be further investigated in the application of LD-based approaches.

Association between the ancestral haplotype HLA A30B18DR3 and multiple sclerosis in central Sardinia.

Association and linkage studies have established the importance of the major histocompatibility complex (MHC) in the susceptibility for multiple sclerosis (MS). We carried out a case-control study to investigate the ancestral haplotype A30B18DR3 and MS in the Nuoro population of Sardinia, which is isolated and genetically distinct from other populations in the Mediterranean basin and characterized by genetic homogeneity, high level of inbreeding, low migration, high prevalence of MS, high frequency of the relevant haplotype, and high past malaria prevalence. Cases and controls were serologically typed for the currently recognized HLA-A, B, and DR antigens. We used a log-linear approach to fit a wide class of models. We tested our hypothesis comparing different models via a likelihood ratio test. We overcame the complication due to unknown gametic phase using expectation-maximization (EM) algorithm as the estimation method. We estimated confidence intervals for odds ratio by using a profile likelihood approach. We found that: (1) the ancestral haplotype A30B18DR3 was associated to MS after allowing for a possible stratification in cases and controls; (2) DR3 allele was conditional independent on disease status, given A30B18 haplotype; (3) there was a tendency for ORs for the high-risk haplotypes to be higher in the high malaria strata; however, this indication did not achieve statistical significance (P = 0.11).

Autoregressive models for describing non-linear changes in biological parameters fitted using BUGS.

Many biological processes give outcome data which show a curvilinear association with time which tends to an asymptote. We show how autoregressive models can be used to describe this association within individual subjects. We also present a Bayesian approach implemented using statistical software, BUGS, to fit these models in a multi-level (hierarchical) setting that describes variation in the association between subjects. Peak expiratory flow data from a clinical trial involving subjects with asthma are used to illustrate the methods.