Background
Latent autoimmune diabetes in adults (LADA) is a slowly developing type 1 diabetes.

Objectives
To compare interventions used for LADA.

Search methods
Studies were obtained from searches of electronic databases, supplemented by handsearches, conference proceedings and consultation with experts. Date of last search was December 2010.

Selection criteria
Randomised controlled trials (RCT) and controlled clinical trials (CCT) evaluating interventions for LADA or type 2 diabetes with antibodies were included.

Data collection and analysis
Two authors independently extracted data and assessed risk of bias. Studies were summarised using meta-analysis or descriptive methods.

Main results
Searches identified 13,306 citations. Fifteen publications (ten studies) were included, involving 1019 participants who were followed between three months to 10 years (1060 randomised). All studies had a high risk of bias. Sulphonylurea (SU) with insulin did not improve metabolic control significantly more than insulin alone at three months (one study, n = 15) and at 12 months (one study, n = 14) of treatment and follow-up. SU (with or without metformin) gave poorer metabolic control compared to insulin alone (mean difference in glycosylated haemoglobin A1c (HbA1c) from baseline to end of study, for insulin compared to oral therapy: -1.3% (95% confidence interval (CI) -2.4 to -0.1; P = 0.03, 160 participants, four studies, follow-up/duration of therapy: 12, 30, 36 and 60 months; however, heterogeneity was considerable). In addition, there was evidence that SU caused earlier insulin dependence (proportion requiring insulin at two years was 30% in the SU group compared to 5% in conventional care group (P < 0.001); patients classified as insulin dependent was 64% (SU group) and 12.5% (insulin group, P = 0.007). No intervention influenced fasting C-peptide, but insulin maintained stimulated C-peptide better than SU (one study, mean difference 7.7 ng/ml (95% CI 2.9 to 12.5)). In a five year follow-up of GAD65 (glutamic acid decarboxylase formulated with aluminium hydroxide), improvements in fasting and stimulated C-peptide levels (20 μg group) were maintained after five years. Short term (three months) follow-up in one study (n = 74) using Chinese remedies did not demonstrate a significant difference in improving fasting C-peptide levels compared to insulin alone (0.07 µg/L (95% CI -0.05 to 0.19). One study using vitamin D with insulin showed steady fasting C-peptide levels in the vitamin D group but declining fasting C-peptide levels (368 to 179 pmol/L, P = 0.006) in the insulin alone group at 12 months follow-up. Comparing studies was difficult as there was a great deal of heterogeneity in the studies and in their selection criteria. There was no information regarding health-related quality of life, complications of diabetes, cost or health service utilisation, mortality and limited evidence on adverse events (studies on oral agents or insulin reported no adverse events in terms of severe hypoglycaemic episodes).

Authors' conclusions
Two studies show SU leading to earlier insulin dependence and a meta-analysis of four studies with considerable heterogeneity showed poorer metabolic control if SU is prescribed for patients with LADA compared to insulin. One study showed that vitamin D with insulin may protect pancreatic beta cells in LADA. Novel treatments such as GAD65 in certain doses (20 μg) have been suggested to maintain fasting and stimulated C-peptide levels. However, there is no significant evidence for or against other lines of treatment of LADA.

/pdf/interventions-for-latent-autoimmune-diabetes-lada-in-adults-575yg4pwr3.pdf

Interventions for latent autoimmune diabetes (LADA) in adults.

There is a growing recognition of the value of synthesising qualitative research in the evidence base in order to facilitate effective and appropriate health care. In response to this, methods for undertaking these syntheses are currently being developed. Thematic analysis is a method that is often used to analyse data in primary qualitative research. This paper reports on the use of this type of analysis in systematic reviews to bring together and integrate the findings of multiple qualitative studies. We describe thematic synthesis, outline several steps for its conduct and illustrate the process and outcome of this approach using a completed review of health promotion research. Thematic synthesis has three stages: the coding of text 'line-by-line'; the development of 'descriptive themes'; and the generation of 'analytical themes'. While the development of descriptive themes remains 'close' to the primary studies, the analytical themes represent a stage of interpretation whereby the reviewers 'go beyond' the primary studies and generate new interpretive constructs, explanations or hypotheses. The use of computer software can facilitate this method of synthesis; detailed guidance is given on how this can be achieved. We used thematic synthesis to combine the studies of children's views and identified key themes to explore in the intervention studies. Most interventions were based in school and often combined learning about health benefits with 'hands-on' experience. The studies of children's views suggested that fruit and vegetables should be treated in different ways, and that messages should not focus on health warnings. Interventions that were in line with these suggestions tended to be more effective. Thematic synthesis enabled us to stay 'close' to the results of the primary studies, synthesising them in a transparent way, and facilitating the explicit production of new concepts and hypotheses. We compare thematic synthesis to other methods for the synthesis of qualitative research, discussing issues of context and rigour. Thematic synthesis is presented as a tried and tested method that preserves an explicit and transparent link between conclusions and the text of primary studies; as such it preserves principles that have traditionally been important to systematic reviewing.

/pdf/methods-for-the-thematic-synthesis-of-qualitative-research-491f5mxj08.pdf

Methods for the thematic synthesis of qualitative research in systematic reviews

Such diverse thinkers as Lao-Tze, Confucius, and U.S. Defense Secretary Donald Rumsfeld have all pointed out that we need to be able to tell the difference between real and assumed knowledge. The systematic review is a scientific tool that can help with this difficult task. It can help, for example, with appraising, summarising, and communicating the results and implications of otherwise unmanageable quantities of data. This is important because quite often there are so many studies, and their results are often so conflicting, that no policymaker or practitioner could possibly carry out this task themselves.Systematic review methods have been widely used in health care, and are becoming increasingly common in the social sciences (fostered, for example, by the work of the Campbell Collaboration). 

This book outlines the rationale and methods of systematic reviews, giving worked examples from social science and other fields. It requires no previous knowledge, but takes the reader through the process stage by stage. It draws on examples from such diverse fields as psychology, criminology, education, transport, social welfare, public health, and housing and urban policy, among others.The book includes detailed sections on assessing the quality of both quantitative, and qualitative research; searching for evidence in the social sciences;meta-analytic and other methods of evidence synthesis; publication bias; heterogeneity; and approaches to dissemination.

Systematic Reviews in the Social Sciences: A Practical Guide

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner’s instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.

/pdf/effect-size-confidence-interval-and-statistical-significance-4svgygjn60.pdf

Effect size, confidence interval and statistical significance: a practical guide for biologists.

In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I(2) index has been proposed to quantify the degree of heterogeneity in a meta-analysis. In this article, the performances of the Q test and the confidence interval around the I(2) index are compared by means of a Monte Carlo simulation. The results show the utility of the I(2) index as a complement to the Q test, although it has the same problems of power with a small number of studies.

/pdf/assessing-heterogeneity-in-meta-analysis-q-statistic-or-i2-57zh1r444i.pdf

Assessing heterogeneity in meta-analysis: Q statistic or I2 index?

An output voltage wave having a desired amplitude and frequency is formed from selected portions of the AC signal generated by an induction generator. The selected portions of the generated signal are chosen in such a fashion as to establish the phase angle of the current drawn from the induction generator at a value that results in an appropriate excitation current being provided for the induction generator. Control arrangements are utilized to determine the required phase angle for the current drawn from the induction generator and to determine the portions of the generated signal that must be utilized to provide such a phase angle. Establishing the phase angle of the current drawn from the induction generator is accomplished without affecting the phase angle of the output current, which is determined by the load.

Systematic Reviews in Health Care

Life expectancy of individuals on combination antiretroviral therapy in high-income countries: a collaborative analysis of 14 cohort studies

BACKGROUND Subgroup analyses are common in randomised controlled trials (RCTs). There are many easily accessible guidelines on the selection and analysis of subgroups but the key messages do not seem to be universally accepted and inappropriate analyses continue to appear in the literature. This has potentially serious implications because erroneous identification of differential subgroup effects may lead to inappropriate provision or withholding of treatment. OBJECTIVES (1) To quantify the extent to which subgroup analyses may be misleading. (2) To compare the relative merits and weaknesses of the two most common approaches to subgroup analysis: separate (subgroup-specific) analyses of treatment effect and formal statistical tests of interaction. (3) To establish what factors affect the performance of the two approaches. (4) To provide estimates of the increase in sample size required to detect differential subgroup effects. (5) To provide recommendations on the analysis and interpretation of subgroup analyses. METHODS The performances of subgroup-specific and formal interaction tests were assessed by simulating data with no differential subgroup effects and determining the extent to which the two approaches (incorrectly) identified such an effect, and simulating data with a differential subgroup effect and determining the extent to which the two approaches were able to (correctly) identify it. Initially, data were simulated to represent the 'simplest case' of two equal-sized treatment groups and two equal-sized subgroups. Data were first simulated with no differential subgroup effect and then with a range of types and magnitudes of subgroup effect with the sample size determined by the nominal power (50-95%) for the overall treatment effect. Additional simulations were conducted to explore the individual impact of the sample size, the magnitude of the overall treatment effect, the size and number of treatment groups and subgroups and, in the case of continuous data, the variability of the data. The simulated data covered the types of outcomes most commonly used in RCTs, namely continuous (Gaussian) variables, binary outcomes and survival times. All analyses were carried out using appropriate regression models, and subgroup effects were identified on the basis of statistical significance at the 5% level. RESULTS While there was some variation for smaller sample sizes, the results for the three types of outcome were very similar for simulations with a total sample size of greater than or equal to 200. With simulated simplest case data with no differential subgroup effects, the formal tests of interaction were significant in 5% of cases as expected, while subgroup-specific tests were less reliable and identified effects in 7-66% of cases depending on whether there was an overall treatment effect. The most common type of subgroup effect identified in this way was where the treatment effect was seen to be significant in one subgroup only. When a simulated differential subgroup effect was included, the results were dependent on the nominal power of the simulated data and the type and magnitude of the subgroup effect. However, the performance of the formal interaction test was generally superior to that of the subgroup-specific analyses, with more differential effects correctly identified. In addition, the subgroup-specific analyses often suggested the wrong type of differential effect. The ability of formal interaction tests to (correctly) identify subgroup effects improved as the size of the interaction increased relative to the overall treatment effect. When the size of the interaction was twice the overall effect or greater, the interaction tests had at least the same power as the overall treatment effect. However, power was considerably reduced for smaller interactions, which are much more likely in practice. The inflation factor required to increase the sample size to enable detection of the interaction with the same power as the overall effect varied with the size of the interaction. For an interaction of the same magnitude as the overall effect, the inflation factor was 4, and this increased dramatically to of greater than or equal to 100 for more subtle interactions of < 20% of the overall effect. Formal interaction tests were generally robust to alterations in the number and size of the treatment and subgroups and, for continuous data, the variance in the treatment groups, with the only exception being a change in the variance in one of the subgroups. In contrast, the performance of the subgroup-specific tests was affected by almost all of these factors with only a change in the number of treatment groups having no impact at all. CONCLUSIONS While it is generally recognised that subgroup analyses can produce spurious results, the extent of the problem is almost certainly under-estimated. This is particularly true when subgroup-specific analyses are used. In addition, the increase in sample size required to identify differential subgroup effects may be substantial and the commonly used 'rule of four' may not always be sufficient, especially when interactions are relatively subtle, as is often the case. CONCLUSIONS--RECOMMENDATIONS FOR SUBGROUP ANALYSES AND THEIR INTERPRETATION: (1) Subgroup analyses should, as far as possible, be restricted to those proposed before data collection. Any subgroups chosen after this time should be clearly identified. (2) Trials should ideally be powered with subgroup analyses in mind. However, for modest interactions, this may not be feasible. (3) Subgroup-specific analyses are particularly unreliable and are affected by many factors. Subgroup analyses should always be based on formal tests of interaction although even these should be interpreted with caution. (4) The results from any subgroup analyses should not be over-interpreted. Unless there is strong supporting evidence, they are best viewed as a hypothesis-generation exercise. In particular, one should be wary of evidence suggesting that treatment is effective in one subgroup only. (5) Any apparent lack of differential effect should be regarded with caution unless the study was specifically powered with interactions in mind. CONCLUSIONS--RECOMMENDATIONS FOR RESEARCH: (1) The implications of considering confidence intervals rather than p-values could be considered. (2) The same approach as in this study could be applied to contexts other than RCTs, such as observational studies and meta-analyses. (3) The scenarios used in this study could be examined more comprehensively using other statistical methods, incorporating clustering effects, considering other types of outcome variable and using other approaches, such as Bootstrapping or Bayesian methods.

/pdf/subgroup-analyses-in-randomised-controlled-trials-4yzxbz4ezr.pdf

M Egger

Papers

Systematic Reviews in Health Care

Life expectancy of individuals on combination antiretroviral therapy in high-income countries: a collaborative analysis of 14 cohort studies

Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives

Investigating and Dealing with Publication and Other Biases

Principles of and Procedures for Systematic Reviews