Biomedical subjects
Publications and source records attributed to A E Ades.
Comparison of age-specific prevalence of juvenile-onset diabetes mellitus between 1946 and 1958 British cohort birth studies (up to the ages of 26 and 23, respectively) suggests that the overall prevalence of diabetes in young life has not increased, but the disease is manifest at an earlier age in susceptible individuals.
Using the Rutter Behavior Questionnaire, schoolteachers assessed the frequency of common abnormal behavior in 544 8-year-old children with phenylketonuria who were born in the United Kingdom and in whom the diagnosis was made by routine testing in infancy, either during the early years of screening (cohort 1, births 1964 to 1971) or after a national reorganization of the program (cohort 2, births 1972 to 1977). All children received treatment before 4 months of age. Two matched control subjects were assessed for each patient. Compared with the controls, patients in cohorts 1 and 2 receiving a strict low-phenylalanine (phe) diet (average phe concentration less than 600 mumol/L) were 1.5 and 1.7 times, respectively, more likely to have deviant behavior; those receiving a less well controlled diet were 2.5 and 1.9 times, respectively, more likely to show such behavior. Patients more often had mannerisms, hyperactivity, and signs of anxiety and were less responsive and more solitary than were controls. On the other hand, they were not more aggressive, untruthful, or disobedient, nor absent from school more frequently. The increased frequency of deviant behavior may be the result of both psychologic stress and neurologic impairment.
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A computer program was written to analyse oligonucleotide patterns displayed by gel electrophoresis following restriction endonuclease digestion of human cytomegaloviral DNA, and was applied to an epidemiological study of the transmission of infection in a hospital special care baby unit, with regard to infant-to-infant and mother-to-infant transmission. The program calculates the molecular weight of oligonucleotides from their mobilities, using a cubic spline curve based on the mobilities of oligonucleotides from the AD169 strain. A matching algorithm then calculates the number of unmatched fragments for each pair of viral isolates. This was used as a similarity measure which successfully distinguished mother and infant isolate pairs from epidemiologically unrelated pairs. The program is not intended to provide fully automatic matching, but could be recommended as a screening device to pick out pairs of strains which are sufficiently similar to suggest a common source of infection, and which may warrant closer comparison. Other applications are discussed, and the possible use of densitometers to automate data entry is considered.
Lung cancer mortality was examined in a cohort of 4393 men employed at a zinc-lead-cadmium smelter. There was an excess of lung cancer (overall SMR = 124.5, 95% confidence interval 107-144) which was particularly evident for those employed for more than 20 years. A statistically significant trend in SMRs with increasing duration of employment was apparent. Quantitative estimates of exposure to cadmium and ordinal rankings for lead, arsenic, zinc, sulphur dioxide, and dust were used to calculate cumulative exposures from job histories. Matched logistic regression was used to compare the cumulative exposures of cases of lung cancer to those of controls matched for date of birth and date of starting work and surviving at the time of death of the matched cases. The increasing risk of lung cancer associated with increasing duration of employment could not be accounted for by cadmium and did not appear to be restricted to any particular process or department. Although lung cancer mortality was associated with estimates of cumulative exposure to arsenic and to lead, it was not possible to determine whether the increased risk might be due to arsenic, lead, or to other contaminants in the smelter. These results are compared with findings from other non-ferrous smelters.
As part of a project within the European Community research activities on acquired immunodeficiency syndrome (AIDS), infants born to human-immunodeficiency-virus-seropositive mothers are being followed up from birth. By October, 1986, 71 infants from Padua, Berlin, and Edinburgh had been followed up to a median age of 6 months (range 1-15 months). Symptoms of AIDS or AIDS-related complex (ARC) had developed in 5, 3 of whom had died. The median age at antibody loss was during the 10th month. An estimated 75% will have lost maternal antibody by 12 months, but loss of antibody did not exclude infection confirmed by virus culture. Numbers were too small to draw conclusions about the risk of AIDS/ARC and mode of delivery or breast-feeding. The study suggested that the risk of AIDS/ARC is higher in infants born to mothers who have AIDS symptoms during pregnancy.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Decision models are usually populated 1 parameter at a time, with 1 item of information informing each parameter. Often, however, data may not be available on the parameters themselves but on several functions of parameters, and there may be more items of information than there are parameters to be estimated. The authors show how in these circumstances all the model parameters can be estimated simultaneously using Bayesian Markov chain Monte Carlo methods. Consistency of the information and/or the adequacy of the model can also be assessed within this framework. Statistical evidence synthesis using all available data should result in more precise estimates of parameters and functions of parameters, and is compatible with the emphasis currently placed on systematic use of evidence. To illustrate this, WinBUGS software is used to estimate a simple 9-parameter model of the epidemiology of HIV in women attending prenatal clinics, using information on 12 functions of parameters, and to thereby compute the expected net benefit of 2 alternative prenatal testing strategies, universal testing and targeted testing of high-risk groups. The authors demonstrate improved precision of estimates, and lower estimates of the expected value of perfect information, resulting from the use of all available data.
In structuring decision models of medical interventions, it is commonly recommended that only 2 branches be used for each chance node to avoid logical inconsistencies that can arise during sensitivity analyses if the branching probabilities do not sum to 1. However, information may be naturally available in an unconditional form, and structuring a tree in conditional form may complicate rather than simplify the sensitivity analysis of the unconditional probabilities. Current guidance emphasizes using probabilistic sensitivity analysis, and a method is required to provide probabilistic probabilities over multiple branches that appropriately represents uncertainty while satisfying the requirement that mutually exclusive event probabilities should sum to 1. The authors argue that the Dirichlet distribution, the multivariate equivalent of the beta distribution, is appropriate for this purpose and illustrate its use for generating a fully probabilistic transition matrix for a Markov model. Furthermore, they demonstrate that by adopting a Bayesian approach, the problem of observing zero counts for transitions of interest can be overcome.
There has been an increasing interest in using expected value of information (EVI) theory in medical decision making, to identify the need for further research to reduce uncertainty in decision and as a tool for sensitivity analysis. Expected value of sample information (EVSI) has been proposed for determination of optimum sample size and allocation rates in randomized clinical trials. This article derives simple Monte Carlo, or nested Monte Carlo, methods that extend the use of EVSI calculations to medical decision applications with multiple sources of uncertainty, with particular attention to the form in which epidemiological data and research findings are structured. In particular, information on key decision parameters such as treatment efficacy are invariably available on measures of relative efficacy such as risk differences or odds ratios, but not on model parameters themselves. In addition, estimates of model parameters and of relative effect measures in the literature may be heterogeneous, reflecting additional sources of variation besides statistical sampling error. The authors describe Monte Carlo procedures for calculating EVSI for probability, rate, or continuous variable parameters in multi parameter decision models and approximate methods for relative measures such as risk differences, odds ratios, risk ratios, and hazard ratios. Where prior evidence is based on a random effects meta-analysis, the authors describe different ESVI calculations, one relevant for decisions concerning a specific patient group and the other for decisions concerning the entire population of patient groups. They also consider EVSI methods for new studies intended to update information on both baseline treatment efficacy and the relative efficacy of 2 treatments. Although there are restrictions regarding models with prior correlation between parameters, these methods can be applied to the majority of probabilistic decision models. Illustrative worked examples of EVSI calculations are given in an appendix.
Markov transition models are frequently used to model disease progression. The authors show how the solution to Kolmogorov's forward equations can be exploited to map between transition rates and probabilities from probability data in multistate models. They provide a uniform, Bayesian treatment of estimation and propagation of uncertainty of transition rates and probabilities when 1) observations are available on all transitions and exact time at risk in each state (fully observed data) and 2) observations are on initial state and final state after a fixed interval of time but not on the sequence of transitions (partially observed data). The authors show how underlying transition rates can be recovered from partially observed data using Markov chain Monte Carlo methods in WinBUGS, and they suggest diagnostics to investigate inconsistencies between evidence from different starting states. An illustrative example for a 3-state model is given, which shows how the methods extend to more complex Markov models using the software WBDiff to compute solutions. Finally, the authors illustrate how to statistically combine data from multiple sources, including partially observed data at several follow-up times and also how to calibrate a Markov model to be consistent with data from one specific study.
This article shows that the interpretation of the random-effects models used in meta-analysis to summarize heterogeneous treatment effects can have a marked effect on the results from decision models. Sources of variation in meta-analysis include the following: random variation in outcome definition (amounting to a form of measurement error), variation between the patient groups in different trials, variation between protocols, and variation in the way a given protocol is implemented. Each of these alternatives leads to a different model for how the heterogeneity in the effect sizes previously observed might relate to the effect size(s) in a future implementation. Furthermore, these alternative models require different computations and, when the net benefits are nonlinear in the efficacy parameters, result in different expected net benefits. The authors' analysis suggests that the mean treatment effect from a random-effects meta-analysis will only seldom be an appropriate representation of the efficacy expected in a future implementation. Instead, modelers should consider either the predictive distribution of a future treatment effect, or they should assume that the future implementation will result in a distribution of treatment effects. A worked example, in a probabilistic, Bayesian posterior framework, is used to illustrate the alternative computations and to show how parameter uncertainty can be combined with variation between individuals and heterogeneity in meta-analysis.