A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests.
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ABSTRACT:
SUBMITTER: Chen Y
PROVIDER: S-EPMC4401477 | biostudies-literature | 2015 Apr
REPOSITORIES: biostudies-literature
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Project description:To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented.
| S-EPMC4245380 | biostudies-literature
Project description:The Cochrane Collaboration says that the Cochrane handbook for diagnostic test accuracy reviews (DTAR) is currently in development as per the Cochrane Collaboration. This implies that the methodology of systematic reviews (SR) of diagnostic test accuracy is still a matter of debate. At this point, comparison of methodologies for SR in case of interventions as against diagnostics would be helpful to understand DTAR.
| S-EPMC4183058 | biostudies-literature
Project description:BACKGROUND:Although guidelines for critical appraisal of diagnostic research and meta-analyses have already been published, these may be difficult to understand for clinical researchers or do not provide enough detailed information. METHODS:Development of guidelines based on a systematic review of the evidence in reports of systematic searches of the literature for diagnostic research, of methodological criteria to evaluate diagnostic research, of methods for statistical pooling of data on diagnostic accuracy, and of methods for exploring heterogeneity. RESULTS:Guidelines for conducting diagnostic systematic reviews are presented in a stepwise fashion and are followed by comments providing further information. Examples are given using the results of two systematic reviews on the accuracy of the urine dipstick in the diagnosis of urinary tract infections, and on the accuracy of the straight-leg-raising test in the diagnosis of intervertebral disc hernia.
| S-EPMC117243 | biostudies-literature
Project description:ObjectivesTo assess the methods and reporting of systematic reviews of diagnostic tests.Data sourcesSystematic searches of Medline, Embase, and five other databases identified reviews of tests used in patients with cancer. Of these, 89 satisfied our inclusion criteria of reporting accuracy of the test compared with a reference test, including an electronic search, and published since 1990.Review methodsAll reviews were assessed for methods and reporting of objectives, search strategy, participants, clinical setting, index and reference tests, study design, study results, graphs, meta-analysis, quality, bias, and procedures in the review. We assessed 25 randomly selected reviews in more detail.Results75% (67) of the reviews stated inclusion criteria, 49% (44) tabulated characteristics of included studies, 40% (36) reported details of study design, 17% (15) reported on the clinical setting, 17% (15) reported on the severity of disease in participants, and 49% (44) reported on whether the tumours were primary, metastatic, or recurrent. Of the 25 reviews assessed in detail, 68% (17) stated the reference standard used in the review, 36% (9) reported the definition of a positive result for the index test, and 56% (14) reported sensitivity, specificity, and sample sizes for individual studies. Of the 89 reviews, 61% (54) attempted to formally synthesise results of the studies and 32% (29) reported formal assessments of study quality.ConclusionsReliability and relevance of current systematic reviews of diagnostic tests is compromised by poor reporting and review methods.
| S-EPMC1553548 | biostudies-literature
Project description:Survival bias is a long-recognized problem in case-control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or "prevalence-incidence bias." It occurs in case-control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case-control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.
| S-EPMC6150474 | biostudies-literature
Project description:ImportanceSystematic reviews of medical imaging diagnostic test accuracy (DTA) studies are affected by between-study heterogeneity due to a range of factors. Failure to appropriately assess the extent and causes of heterogeneity compromises the interpretability of systematic review findings.ObjectiveTo assess how heterogeneity has been examined in medical imaging DTA studies.Evidence reviewThe PubMed database was searched for systematic reviews of medical imaging DTA studies that performed a meta-analysis. The search was limited to the 40 journals with highest impact factor in the radiology, nuclear medicine, and medical imaging category in the InCites Journal Citation Reports of 2021 to reach a sample size of 200 to 300 included studies. Descriptive analysis was performed to characterize the imaging modality, target condition, type of meta-analysis model used, strategies for evaluating heterogeneity, and sources of heterogeneity identified. Multivariable logistic regression was performed to assess whether any factors were associated with at least 1 source of heterogeneity being identified in the included meta-analyses. Methodological quality evaluation was not performed. Data analysis occurred from October to December 2022.FindingsA total of 242 meta-analyses involving a median (range) of 987 (119-441 510) patients across a diverse range of disease categories and imaging modalities were included. The extent of heterogeneity was adequately described (ie, whether it was absent, low, moderate, or high) in 220 studies (91%) and was most commonly assessed using the I2 statistic (185 studies [76%]) and forest plots (181 studies [75%]). Heterogeneity was rated as moderate to high in 191 studies (79%). Of all included meta-analyses, 122 (50%) performed subgroup analysis and 87 (36%) performed meta-regression. Of the 242 studies assessed, 189 (78%) included 10 or more primary studies. Of these 189 studies, 60 (32%) did not perform meta-regression or subgroup analysis. Reasons for being unable to investigate sources of heterogeneity included inadequate reporting of primary study characteristics and a low number of included primary studies. Use of meta-regression was associated with identification of at least 1 source of variability (odds ratio, 1.90; 95% CI, 1.11-3.23; P = .02).Conclusions and relevanceIn this systematic review of assessment of heterogeneity in medical imaging DTA meta-analyses, most meta-analyses were impacted by a moderate to high level of heterogeneity, presenting interpretive challenges. These findings suggest that, despite the development and availability of more rigorous statistical models, heterogeneity appeared to be incomplete, inconsistently evaluated, or methodologically questionable in many cases, which lessened the interpretability of the analyses performed; comprehensive heterogeneity assessment should be addressed at the author level by improving personal familiarity with appropriate statistical methodology for assessing heterogeneity and involving biostatisticians and epidemiologists in study design, as well as at the editorial level, by mandating adherence to methodologic standards in primary DTA studies and DTA meta-analyses.
| S-EPMC10905313 | biostudies-literature
Project description:Advances in sequencing technology allow assessing the impact of rare variation on common disorders. For this purpose, methods combine rare variants across a gene and compare an aggregate statistic between cases and controls. However, sequencing many individuals is costly. Hence, it is necessary to identify case samples that are most likely to result in powerful tests under realistic model assumptions. Power can be increased by selecting cases that are highly likely to carry risk variants. As rare variants that contribute to the heritability of a disease co-segregate among affected family members, selecting cases that have affected family members may increase the power of rare variant tests considerably. Here I compare sequencing random cases to cases ascertained to have affected family members. I quantify the power of the different approaches and provide criteria for sample selection under different models of inheritance. Under a model of multiplicative gene-gene interaction, a sample of random cases has to be 2-16-fold larger to achieve the same power as a sample of cases ascertained to have affected family members. However, in traits with high heritability this power gain can be reduced or even reversed under models of additive gene-gene interaction. Hence study designs should depend on the studied disease's heritability and on the available sample size. I also show that selecting cases that share both chromosomes identical by descent with an affected sibling at candidate regions can result in a further power gain.
| S-EPMC3449077 | biostudies-literature
Project description:Genome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective p-values with Bonferroni correction. Since commercially available genomic arrays interrogate hundreds of thousands or even millions of loci simultaneously, many causal yet undetected loci are believed to exist because the conditional power to achieve a genome-wide significance level can be low, in particular for markers with small effect sizes and low minor allele frequencies and in studies with modest sample size. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) resulting in correlated marker test statistics can be incorporated into multi-marker hypothesis tests, thereby increasing power to detect association. Herein, we establish a theoretical benchmark by quantifying the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known. Using that genotype correlations within an LD block translate into an asymptotically multivariate normal distribution for score test statistics, we develop a set of weights for the markers that maximize the non-centrality parameter, and assess the relative loss of power for other approaches. We find that the method of Conneely and Boehnke (2007) based on the maximum absolute test statistic observed in an LD block is a practical and powerful method in a variety of settings. We also explore the effect on the power that prior biological or functional knowledge used to narrow down the locus of the causal marker can have, and conclude that this prior knowledge has to be very strong and specific for the power to approach the maximum achievable level, or even beat the power observed for methods such as the one proposed by Conneely and Boehnke (2007).
| S-EPMC3863805 | biostudies-other
Project description:INTRODUCTION:Many diagnostic accuracy studies are never reported in full in a peer-reviewed journal. Searching for unpublished studies may avoid bias due to selective publication, enrich the power of systematic reviews, and thereby help to reduce research waste. We assessed searching practices among recent systematic reviews of diagnostic accuracy. METHODS:We extracted data from 100 non-Cochrane systematic reviews of diagnostic accuracy indexed in MEDLINE and published between October 2017 and January 2018 and from all 100 Cochrane systematic reviews of diagnostic accuracy published by December 2018, irrespective of whether meta-analysis had been performed. RESULTS:Non-Cochrane and Cochrane reviews searched a median of 4 (IQR 3-5) and 6 (IQR 5-9) databases, respectively; most often MEDLINE/PubMed (n = 100 and n = 100) and EMBASE (n = 81 and n = 100). Additional efforts to identify studies beyond searching bibliographic databases were performed in 76 and 98 reviews, most often through screening reference lists (n = 71 and n = 96), review/guideline articles (n = 18 and n = 52), or citing articles (n = 3 and n = 42). Specific sources of unpublished studies were searched in 22 and 68 reviews, for example, conference proceedings (n = 4 and n = 18), databases only containing conference abstracts (n = 2 and n = 33), or trial registries (n = 12 and n = 39). At least one unpublished study was included in 17 and 23 reviews. Overall, 39 of 2082 studies (1.9%) included in non-Cochrane reviews were unpublished, and 64 of 2780 studies (2.3%) in Cochrane reviews, most often conference abstracts (97/103). CONCLUSION:Searching practices vary considerably across systematic reviews of diagnostic accuracy. Unpublished studies are a minimal fraction of the evidence included in recent reviews.
| S-EPMC7317757 | biostudies-literature
Project description:Ten years after completion of the Human Genome Project, progress towards making "personalized medicine" a reality has been slower than expected. The reason is twofold. Firstly, the science is more difficult than expected. Secondly, limited progress has been made in aligning economic incentives to invest in diagnostics. This paper develops nine case studies of "success" where diagnostic tests are bringing personalized medicine into clinical practice with health and economic impact for patients, healthcare systems, and manufacturers. We focus on the availability of evidence for clinical utility, which is important not only for clinicians but also for payers and budget holders. We find that demonstrating diagnostic clinical utility and the development of economic evidence is currently feasible (i) through drug-diagnostic co-development, and (ii) when the research is sponsored by payers and public bodies. It is less clear whether the diagnostic industry can routinely undertake the work necessary to provide evidence as to the clinical utility and economic value of its products. It would be good public policy to increase the economic incentives to produce evidence of clinical utility: otherwise, opportunities to generate value from personalized medicine-in terms of both cost savings and health gains-may be lost.
| S-EPMC4251388 | biostudies-other