Studies on the Life History of Schistocephalus solidus: Field Observations and Laboratory Experiments

Field and laboratory investigations of the interactions between Schistocephalus solidus (Cestoda: Pseudophyllidea) and its hosts were carried out using: the copepod, Acanthocyclops viridis; the three-spined stickleback, Gasterosteus aculeatus and the chicken, Gallus gallus. The first part of the dissertation is concerned with the epidemiology and impact of the plerocercoid stage of S. solidus on a natural population of three-spined sticklebacks. Samples of about 60 sticklebacks were collected at roughly monthly intervals between August 1988 and September 1989 from an urban pond in Inverleith, Edinburgh. A study was made of uninfected fish in the 0+ cohort in order to evaluate the base level biology of the population (Chapter 2). Growth of the sticklebacks was confined to their first autumn, spring and summer of life when temperatures were high and daylight hours long. The normal life span of sticklebacks in this population is 12-18 months. However, some large 0+ fish were lost from the population over winter perhaps as a result of predation by black-headed gulls (Larus ridibundus). In previous studies lateral plate counts of 4-5 have been associated with a risk of avian predation. As these counts were observed most frequently in the stickleback population of Inverleith this may also reflect the action of avian predators. There is a single opportunity to reproduce between April and June and those that have bred succumb soon afterwards, but some 1+ sticklebacks (presumably non-breeders) manage to survive until their second winter. Analysis of stomach contents showed seasonal variation in stomach fullness. Overall, stomachs were less full during the winter than at any other time during the survey. Diet composition in uninfected sticklebacks also varied with season. In Inverleith pond, sticklebacks are largely benthic feeders, relying most heavily on chironomids and to a lesser extent on ostracods and free-living nematodes particularly during the spring and summer. Of the zooplankton consumed cladocerans were the most common and present in stomachs throughout the year. A greater reliance on plant material during winter may have been the result of food scarcity and could partly explain the poor growth at this time. The composition of the parasite population changed markedly during the life span of the 1988-1989 cohort of sticklebacks (Chapter 3). Logistic regression revealed that time of the year was the single most important determinant of the prevalence of S. solidus infection. The intensity of infection was also largely predicted by the time of year but in addition, host size was important, with the largest sticklebacks harbouring the most plerocercoids. By far the greatest proportion of infected fish and the highest infection intensities were recorded in autumn 1988. This increase in both the number of plerocercoids (which was accompanied by an increase in the abundance of small plerocercoids), strongly suggests that a wave of new infections was occurring at this time. Additional features of this wave of infection were: a tendency towards over-dispersion of parasite numbers, large relative weights of parasites and the presence of plerocercoids large enough to be infective to a definitive host. During winter, many infected fish were lost, notably those harbouring the highest parasite burdens, in terms of plerocercoid numbers, plerocercoid size and the relative weight of the plerocercoids. Such heavily infected sticklebacks may have died because of pathological consequences of their infections. Alternatively, the behaviour of the heavily infected host sticklebacks may have been altered such that they were rendered more susceptible to predation by black-headed gulls, a known definitive host of S. solidus. If avian predation is responsible for the loss of infected (and some uninfected) fish, then eggs will be released from the rapidly maturing adults into the pond at this time. The prevalence of infection was found to be fairly stable through the remainder of the survey period, but a further decrease in intensity was observed in fish sampled during summer; again this may have been the result of parasite-induced host mortality. The growth rate of plerocercoids from single infections parallelled fish growth, being high during autumn, spring and summer. In double infections, however, individual plerocercoid growth was reduced, suggesting that limited nutrients were available for parasite growth and this induced competition between two plerocercoids. Infection with S. solidus plerocercoids was accompanied by changes in the diet, growth, reproduction and longevity of sticklebacks from Inverleith pond (Chapter 4). (Abstract shortened by ProQuest.)

Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework

Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately - which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups

Implications of analysing time-to-event outcomes as binary in meta-analysis: empirical evidence from the Cochrane Database of Systematic Reviews

BACKGROUND: Systematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be analysed on the hazard ratio (HR) scale or can be dichotomized and analysed as binary outcomes using effect measures such as odds ratios (OR) or risk ratios (RR). We investigated the impact of reanalysing meta-analyses from the CDSR that used these different effect measures. METHODS: We extracted two types of meta-analysis data from the CDSR: either recorded in a binary form only ("binary"), or in binary form together with observed minus expected and variance statistics ("OEV"). We explored how results for time-to-event outcomes originally analysed as "binary" change when analysed using the complementary log-log (clog-log) link on a HR scale. For the data originally analysed as HRs ("OEV"), we compared these results to analysing them as binary on a HR scale using the clog-log link or using a logit link on an OR scale. RESULTS: The pooled HR estimates were closer to 1 than the OR estimates in the majority of meta-analyses. Important differences in between-study heterogeneity between the HR and OR analyses were also observed. These changes led to discrepant conclusions between the OR and HR scales in some meta-analyses. Situations under which the clog-log link performed better than logit link and vice versa were apparent, indicating that the correct choice of the method does matter. Differences between scales arise mainly when event probability is high and may occur via differences in between-study heterogeneity or via increased within-study standard error in the OR relative to the HR analyses. CONCLUSIONS: We identified that dichotomising time-to-event outcomes may be adequate for low event probabilities but not for high event probabilities. In meta-analyses where only binary data are available, the complementary log-log link may be a useful alternative when analysing time-to-event outcomes as binary, however the exact conditions need further exploration. These findings provide guidance on the appropriate methodology that should be used when conducting such meta-analyses

Measuring the impact of methodological research

Providing evidence of impact highlights the benefits of medical research to society. Such evidence is increasingly requested by research funders and commonly relies on citation analysis. However, other indicators may be more informative. Although frameworks to demonstrate the impact of clinical research have been reported, no complementary framework exists for methodological research. Therefore, we assessed the impact of methodological research projects conducted or completed between 2009 and 2012 at the UK Medical Research Council Clinical Trials Unit Hub for Trials Methodology Research Hub, with a view to developing an appropriate framework

Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?

Identifying which individuals beneft most from particular treatments or other interventions underpins so-called personalised or stratifed medicine. However, single trials are typically underpowered for exploring whether participant characteristics, such as age or disease severity, determine an individual's response to treatment. A meta-analysis of multiple trials, particularly one where individual participant data (IPD) are available, provides greater power to investigate interactions between participant characteristics (covariates) and treatment e?ects. We use a published IPD meta-analysis to illustrate three broad approaches used for testing such interactions. Based on another systematic review of recently published IPD meta-analyses, we also show that all three approaches can be applied to aggregate data as well as IPD. We also summarise which methods of analysing and presenting interactions are in current use, and describe their advantages and disadvantages. We recommend that testing for interactions using within-trials information alone (the def approach) becomes standard practice, alongside graphical presentation that directly visualises this

Comparison of aggregate and individual participant data approaches to meta-analysis of randomised trials : An observational study

BACKGROUND: It remains unclear when standard systematic reviews and meta-analyses that rely on published aggregate data (AD) can provide robust clinical conclusions. We aimed to compare the results from a large cohort of systematic reviews and meta-analyses based on individual participant data (IPD) with meta-analyses of published AD, to establish when the latter are most likely to be reliable and when the IPD approach might be required. METHODS AND FINDINGS: We used 18 cancer systematic reviews that included IPD meta-analyses: all of those completed and published by the Meta-analysis Group of the MRC Clinical Trials Unit from 1991 to 2010. We extracted or estimated hazard ratios (HRs) and standard errors (SEs) for survival from trial reports and compared these with IPD equivalents at both the trial and meta-analysis level. We also extracted or estimated the number of events. We used paired t tests to assess whether HRs and SEs from published AD differed on average from those from IPD. We assessed agreement, and whether this was associated with trial or meta-analysis characteristics, using the approach of Bland and Altman. The 18 systematic reviews comprised 238 unique trials or trial comparisons, including 37,082 participants. A HR and SE could be generated for 127 trials, representing 53% of the trials and approximately 79% of eligible participants. On average, trial HRs derived from published AD were slightly more in favour of the research interventions than those from IPD (HRAD to HRIPD ratio = 0.95, p = 0.007), but the limits of agreement show that for individual trials, the HRs could deviate substantially. These limits narrowed with an increasing number of participants (p < 0.001) or a greater number (p < 0.001) or proportion (p < 0.001) of events in the AD. On average, meta-analysis HRs from published AD slightly tended to favour the research interventions whether based on fixed-effect (HRAD to HRIPD ratio = 0.97, p = 0.088) or random-effects (HRAD to HRIPD ratio = 0.96, p = 0.044) models, but the limits of agreement show that for individual meta-analyses, agreement was much more variable. These limits tended to narrow with an increasing number (p = 0.077) or proportion of events (p = 0.11) in the AD. However, even when the information size of the AD was large, individual meta-analysis HRs could still differ from their IPD equivalents by a relative 10% in favour of the research intervention to 5% in favour of control. We utilised the results to construct a decision tree for assessing whether an AD meta-analysis includes sufficient information, and when estimates of effects are most likely to be reliable. A lack of power at the meta-analysis level may have prevented us identifying additional factors associated with the reliability of AD meta-analyses, and we cannot be sure that our results are generalisable to all outcomes and effect measures. CONCLUSIONS: In this study we found that HRs from published AD were most likely to agree with those from IPD when the information size was large. Based on these findings, we provide guidance for determining systematically when standard AD meta-analysis will likely generate robust clinical conclusions, and when the IPD approach will add considerable value

Use of multiple covariates in assessing treatment-effect modifiers: A methodological review of individual participant data meta-analyses

Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice

Using individual participant data to improve network meta-analysis projects.

A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment-covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics