Biomedical subjects

Michael J Sweeting

Publications and source records attributed to Michael J Sweeting.

5 recordsLinked to original sources

PubMed2006

Anti-TNF antibody therapy in rheumatoid arthritis and the risk of serious infections and malignancies: systematic review and meta-analysis of rare harmful effects in randomized controlled trials.

CONTEXT: Tumor necrosis factor (TNF) plays an important role in host defense and tumor growth control. Therefore, anti-TNF antibody therapies may increase the risk of serious infections and malignancies. OBJECTIVE: To assess the extent to which anti-TNF antibody therapies may increase the risk of serious infections and malignancies in patients with rheumatoid arthritis by performing a meta-analysis to derive estimates of sparse harmful events occurring in randomized trials of anti-TNF therapy. DATA SOURCES: A systematic literature search of EMBASE, MEDLINE, Cochrane Library, and electronic abstract databases of the annual scientific meetings of both the European League Against Rheumatism and the American College of Rheumatology was conducted through December 2005. This search was complemented with interviews of the manufacturers of the 2 licensed anti-TNF antibodies. STUDY SELECTION: We included randomized, placebo-controlled trials of the 2 licensed anti-TNF antibodies (infliximab and adalimumab) used for 12 weeks or more in patients with rheumatoid arthritis. Nine trials met our inclusion criteria, including 3493 patients who received anti-TNF antibody treatment and 1512 patients who received placebo. DATA EXTRACTION: Data on study characteristics to assess study quality and intention-to-treat data for serious infections and malignancies were abstracted. Published information from the trials was supplemented by direct contact between principal investigators and industry sponsors. DATA SYNTHESIS: We calculated a pooled odds ratio (Mantel-Haenszel methods with a continuity correction designed for sparse data) for malignancies and serious infections (infection that requires antimicrobial therapy and/or hospitalization) in anti-TNF-treated patients vs placebo patients. We estimated effects for high and low doses separately. The pooled odds ratio for malignancy was 3.3 (95% confidence interval [CI], 1.2-9.1) and for serious infection was 2.0 (95% CI, 1.3-3.1). Malignancies were significantly more common in patients treated with higher doses compared with patients who received lower doses of anti-TNF antibodies. For patients treated with anti-TNF antibodies in the included trials, the number needed to harm was 154 (95% CI, 91-500) for 1 additional malignancy within a treatment period of 6 to 12 months. For serious infections, the number needed to harm was 59 (95% CI, 39-125) within a treatment period of 3 to 12 months. CONCLUSIONS: There is evidence of an increased risk of serious infections and a dose-dependent increased risk of malignancies in patients with rheumatoid arthritis treated with anti-TNF antibody therapy. The formal meta-analysis with pooled sparse adverse events data from randomized controlled trials serves as a tool to assess harmful drug effects.

Adalimumab↗

PubMed2006

Estimated progression rates in three United Kingdom hepatitis C cohorts differed according to method of recruitment.

OBJECTIVES: To estimate hepatitis C virus (HCV) progression rates between disease stages prior to cirrhosis, using data from liver biopsies in three observational cohorts. To demonstrate how the method of cohort recruitment can influence the estimation of HCV-progression rates. STUDY DESIGN AND SETTING: Data came from three United Kingdom observational cohorts, assembled from different referral sources. In total, 987 HCV-infected patients with an estimated (or known) date of infection and at least one histologically scored liver biopsy were eligible for inclusion in the analysis. Liver biopsy scores were used to determine the stage of HCV-related liver disease. A three-state continuous time Markov model was used to estimate covariate-specific average probabilities of progression of disease. RESULTS: After adjusting for confounders, considerably different rates of disease progression were estimated in the three cohorts. For a group of patients with the same demographics, the estimated 20-year probability of progression to cirrhosis was 12% (95% confidence interval CI = 6-22) in a hospital-based cohort, 6% (95% CI = 3-13) in a posttransfusion cohort, and 23% (95% CI = 14-37) in a cohort recruited from a tertiary referral center. CONCLUSION: Researchers using estimates of disease progression should be aware that the method of cohort recruitment has considerable influence on the progression rates that are derived.

Cohort Studies↗

PubMed2005

Bayesian back-calculation using a multi-state model with application to HIV.

Back-calculation is a method of obtaining estimates of the number of infections of a disease over time. Data on an endpoint of the disease, together with knowledge of the time from infection to endpoint, allows reconstruction of the incidence of infection. The technique has had much success when applied to the HIV epidemic, using incidence of AIDS diagnoses to inform past HIV infections. In recent years, the period from infection to AIDS has changed considerably due to new regimes of anti-viral therapies. This has led to attempts to use incidence of first positive HIV test as an alternative basis for back-calculation. Developing on earlier work, this paper explores the feasibility of a multi-state formulation of the back-calculation method that models the disease and diagnosis processes and uses HIV diagnoses as an endpoint. Estimation is carried out in a Bayesian framework, which naturally allows incorporation of external information to inform the diagnosis probabilities. The idea is illustrated on data from the HIV epidemic in homosexuals in England and Wales.

Bayes Theorem↗

PubMed2005

Ambient particulate matter and health effects: publication bias in studies of short-term associations.

BACKGROUND: Time-series studies have shown short-term temporal associations between low levels of ambient particulate air pollution and adverse health effects. It is not known whether or to what extent this literature is affected by publication bias. METHODS: We obtained effect estimates from time-series studies published up to January 2002. These were summarized and examined for funnel plot asymmetry. We compared summary estimates between single-city and prospective multicity studies. Using 1 multicity study, we examined the sensitivity of summary estimates to alternative lag selection policies. RESULTS: We found evidence for publication bias among single-city studies of daily mortality, hospital admissions for chronic obstructive lung disease (COPD), and incidence of cough symptom, but not for studies of lung function. Statistical correction for this bias reduced summary relative risk estimates for a 10 microg/m increment of particulate matter less than 10 microm aerodynamic diameter (PM10) as follows: daily mortality from 1.006 to 1.005 and admissions for COPD from 1.013 to 1.011; and odds ratio for cough from 1.025 to 1.015. Analysis of results from a large multicity study suggested that selection of positive estimates from a range of lags could increase summary estimates for PM10 and daily mortality by up to 130% above those based on nondirectional approaches. CONCLUSION: We conclude that publication bias is present in single-city time-series studies of ambient particles. However, after correcting for publication bias statistically, associations between particles and adverse health effects remained positive and precisely estimated. Differential selection of positive lags may also inflate estimates.

Air Pollutants↗

PubMed2004

What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data.

OBJECTIVES: To compare the performance of different meta-analysis methods for pooling odds ratios when applied to sparse event data with emphasis on the use of continuity corrections. BACKGROUND: Meta-analysis of side effects from RCTs or risk factors for rare diseases in epidemiological studies frequently requires the synthesis of data with sparse event rates. Combining such data can be problematic when zero events exist in one or both arms of a study as continuity corrections are often needed, but, these can influence results and conclusions. METHODS: A simulation study was undertaken comparing several meta-analysis methods for combining odds ratios (using various classical and Bayesian methods of estimation) on sparse event data. Where required, the routine use of a constant and two alternative continuity corrections; one based on a function of the reciprocal of the opposite group arm size; and the other an empirical estimate of the pooled effect size from the remaining studies in the meta-analysis, were also compared. A number of meta-analysis scenarios were simulated and replicated 1000 times, varying the ratio of the study arm sizes. RESULTS: Mantel-Haenszel summary estimates using the alternative continuity correction factors gave the least biased results for all group size imbalances. Logistic regression was virtually unbiased for all scenarios and gave good coverage properties. The Peto method provided unbiased results for balanced treatment groups but bias increased with the ratio of the study arm sizes. The Bayesian fixed effect model provided good coverage for all group size imbalances. The two alternative continuity corrections outperformed the constant correction factor in nearly all situations. The inverse variance method performed consistently badly, irrespective of the continuity correction used. CONCLUSIONS: Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and continuity correction factors is advocated for routine practice.

Bayes Theorem↗