Adjusting trial results for biases in meta-analysis: combining data-based evidence on bias with detailed trial assessment.

Flaws in the conduct of randomized trials can lead to biased estimation of the intervention effect. Methods for adjustment of within-trial biases in meta-analysis include the use of empirical evidence from an external collection of meta-analyses, and the use of expert opinion informed by the assessment of detailed trial information. Our aim is to present methods to combine these two approaches to gain the advantages of both. We make use of the risk of bias information that is routinely available in Cochrane reviews, by obtaining empirical distributions for the bias associated with particular bias profiles (combinations of risk of bias judgements). We propose three methods: a formal combination of empirical evidence and opinion in a Bayesian analysis; asking experts to give an opinion on bias informed by both summary trial information and a bias distribution from the empirical evidence, either numerically or by selecting areas of the empirical distribution. The methods are demonstrated through application to two example binary outcome meta-analyses. Bias distributions based on opinion informed by trial information alone were most dispersed on average, and those based on opinions obtained by selecting areas of the empirical distribution were narrowest. Although the three methods for combining empirical evidence with opinion vary in ease and speed of implementation, they yielded similar results in the two examples

Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: a tutorial.

BACKGROUND: Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. METHODS: In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards). RESULTS: A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. CONCLUSIONS: By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics

Has Chlamydia trachomatis prevalence in young women in England, Scotland and Wales changed? Evidence from national probability surveys

We evaluate the utility of the National Surveys of Attitudes and Sexual Lifestyles (Natsal) undertaken in 2000 and 2010, before and after the introduction of the National Chlamydia Screening Programme, as an evidence source for estimating the change in prevalence of Chlamydia trachomatis (CT) in England, Scotland and Wales. Both the 2000 and 2010 surveys tested urine samples for CT by Nucleic Acid Amplification Tests (NAATs). We examined the sources of uncertainty in estimates of CT prevalence change, including sample size and adjustments for test sensitivity and specificity, survey non-response and informative non-response. In 2000, the unadjusted CT prevalence was 4.22% in women aged 18-24 years; in 2010, CT prevalence was 3.92%, a non-significant absolute difference of 0.30 percentage points (95% credible interval -2.8 to 2.0). In addition to uncertainty due to small sample size, estimates were sensitive to specificity, survey non-response or informative non-response, such that plausible changes in any one of these would be enough to either reverse or double any likely change in prevalence. Alternative ways of monitoring changes in CT incidence and prevalence over time are discussed

Cost-effectiveness of Sertraline in primary care according to initial severity and duration of depressive symptoms: findings from the PANDA RCT

BACKGROUND: Antidepressants are commonly prescribed for depression, but it is unclear whether treatment efficacy depends on severity and duration of symptoms and how prescribing might be targeted cost-effectively. OBJECTIVES: We investigated the cost-effectiveness of the antidepressant sertraline compared with placebo in subgroups defined by severity and duration of depressive symptoms. METHODS: We undertook a cost-effectiveness analysis from the perspective of the NHS and Personal and Social Services (PSS) in the UK alongside the PANDA (What are the indications for Prescribing ANtiDepressants that will leAd to a clinical benefit?) randomised controlled trial (RCT), which compared sertraline with placebo over a 12-week period. Quality of life data were collected at baseline and at 2, 6, and 12 weeks post-randomisation using EQ-5D-5L, from which we calculated quality-adjusted life years (QALYs). Costs (in 2017/18£) were collected using patient records and from resource use questionnaires administered at each follow-up interval. Differences in mean costs and mean QALYs and net monetary benefits were estimated. Our primary analysis used net monetary benefit regressions to identify any interaction between the cost-effectiveness of sertraline and subgroups defined by baseline symptom severity (0-11; 12-19; 20+ on the Clinical Interview Schedule-Revised) and, separately, duration of symptoms (greater or less than 2 years duration). A secondary analysis estimated the cost-effectiveness of sertraline versus placebo, irrespective of duration or severity. RESULTS: There was no evidence of an association between the baseline severity of depressive symptoms and the cost-effectiveness of sertraline. Compared to patients with low symptom severity, the expected net benefits in patients with moderate symptoms were £24 (95% CI - £280 to £328; p value 0.876) and the expected net benefits in patients with high symptom severity were £37 (95% CI - £221 to £296; p value 0.776). Patients who had a longer history of depressive symptoms at baseline had lower expected net benefits from sertraline than those with a shorter history; however, the difference was uncertain (- £27 [95% CI - £258 to £204]; p value 0.817). In the secondary analysis, patients treated with sertraline had higher expected net benefits (£122 [95% CI £18 to £226]; p value 0.101) than those in the placebo group. Sertraline had a high probability (> 95%) of being cost-effective if the health system was willing to pay at least £20,000 per QALY gained. CONCLUSIONS: We found insufficient evidence of a prespecified threshold based on severity or symptom duration that GPs could use to target prescribing to a subgroup of patients where sertraline is most cost-effective. Sertraline is probably a cost-effective treatment for depressive symptoms in UK primary care. TRIAL REGISTRATION: Controlled Trials ISRCTN Registry, ISRCTN84544741

Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept

<p>Abstract</p> <p>Background</p> <p>We propose a simple new method for estimating progression of a chronic disease with multi-state properties by unifying the prevalence pool concept with the Markov process model.</p> <p>Methods</p> <p>Estimation of progression rates in the multi-state model is performed using the E-M algorithm. This approach is applied to data on Type 2 diabetes screening.</p> <p>Results</p> <p>Good convergence of estimations is demonstrated. In contrast to previous Markov models, the major advantage of our proposed method is that integrating the prevalence pool equation (that the numbers entering the prevalence pool is equal to the number leaving it) into the likelihood function not only simplifies the likelihood function but makes estimation of parameters stable.</p> <p>Conclusion</p> <p>This approach may be useful in quantifying the progression of a variety of chronic diseases.</p

A cost-effectiveness analysis of shortened direct-acting antiviral treatment in genotype 1 noncirrhotic treatment-naive patients with chronic hepatitis C virus

BACKGROUND:Direct-acting antivirals are successful in curing hepatitis C virus infection in more than 95% of patients treated for 12 weeks, but they are expensive. Shortened treatment durations, which may have lower cure rates, have been proposed to reduce costs. OBJECTIVES:To evaluate the lifetime cost-effectiveness of different shortened treatment durations for genotype 1 noncirrhotic treatment-naive patients. METHODS:Assuming a UK National Health Service perspective, we used a probabilistic decision tree and Markov model to compare 3 unstratified shortened treatment durations (8, 6, and 4 weeks) against a standard 12-week treatment duration. Patients failing shortened first-line treatment were re-treated with a 12-week treatment regimen. Parameter inputs were taken from published studies. RESULTS:The 8-week treatment duration had an expected incremental net monetary benefit of £7737 (95% confidence interval £3242-£11 819) versus the standard 12-week treatment, per 1000 patients. The 6-week treatment had a positive incremental net monetary benefit, although some uncertainty was observed. The probability that the 8- and 6-week treatments were the most cost-effective was 56% and 25%, respectively, whereas that for the 4-week treatment was 17%. Results were generally robust to sensitivity analyses, including a threshold analysis that showed that the 8-week treatment was the most cost-effective at all drug prices lower than £40 000 per 12-week course. CONCLUSIONS:Shortening treatments licensed for 12 weeks to 8 weeks is cost-effective in genotype 1 noncirrhotic treatment-naive patients. There was considerable uncertainty in the estimates for 6- and 4-week treatments, with some indication that the 6-week treatment may be cost-effective

Impact of Reporting Bias in Network Meta-Analysis of Antidepressant Placebo-Controlled Trials

BACKGROUND: Indirect comparisons of competing treatments by network meta-analysis (NMA) are increasingly in use. Reporting bias has received little attention in this context. We aimed to assess the impact of such bias in NMAs. METHODS: We used data from 74 FDA-registered placebo-controlled trials of 12 antidepressants and their 51 matching publications. For each dataset, NMA was used to estimate the effect sizes for 66 possible pair-wise comparisons of these drugs, the probabilities of being the best drug and ranking the drugs. To assess the impact of reporting bias, we compared the NMA results for the 51 published trials and those for the 74 FDA-registered trials. To assess how reporting bias affecting only one drug may affect the ranking of all drugs, we performed 12 different NMAs for hypothetical analysis. For each of these NMAs, we used published data for one drug and FDA data for the 11 other drugs. FINDINGS: Pair-wise effect sizes for drugs derived from the NMA of published data and those from the NMA of FDA data differed in absolute value by at least 100% in 30 of 66 pair-wise comparisons (45%). Depending on the dataset used, the top 3 agents differed, in composition and order. When reporting bias hypothetically affected only one drug, the affected drug ranked first in 5 of the 12 NMAs but second (n = 2), fourth (n = 1) or eighth (n = 2) in the NMA of the complete FDA network. CONCLUSIONS: In this particular network, reporting bias biased NMA-based estimates of treatments efficacy and modified ranking. The reporting bias effect in NMAs may differ from that in classical meta-analyses in that reporting bias affecting only one drug may affect the ranking of all drugs