Referral bias in ALS epidemiological studies.
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ABSTRACT:
SUBMITTER: Logroscino G
PROVIDER: S-EPMC5901916 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIonizing radiation is an established carcinogen, but risks from low-dose exposures are controversial. Since the Biological Effects of Ionizing Radiation VII review of the epidemiological data in 2006, many subsequent publications have reported excess cancer risks from low-dose exposures. Our aim was to systematically review these studies to assess the magnitude of the risk and whether the positive findings could be explained by biases.MethodsEligible studies had mean cumulative doses of less than 100 mGy, individualized dose estimates, risk estimates, and confidence intervals (CI) for the dose-response and were published in 2006-2017. We summarized the evidence for bias (dose error, confounding, outcome ascertainment) and its likely direction for each study. We tested whether the median excess relative risk (ERR) per unit dose equals zero and assessed the impact of excluding positive studies with potential bias away from the null. We performed a meta-analysis to quantify the ERR and assess consistency across studies for all solid cancers and leukemia.ResultsOf the 26 eligible studies, 8 concerned environmental, 4 medical, and 14 occupational exposure. For solid cancers, 16 of 22 studies reported positive ERRs per unit dose, and we rejected the hypothesis that the median ERR equals zero (P = .03). After exclusion of 4 positive studies with potential positive bias, 12 of 18 studies reported positive ERRs per unit dose (P = .12). For leukemia, 17 of 20 studies were positive, and we rejected the hypothesis that the median ERR per unit dose equals zero (P = .001), also after exclusion of 5 positive studies with potential positive bias (P = .02). For adulthood exposure, the meta-ERR at 100 mGy was 0.029 (95% CI = 0.011 to 0.047) for solid cancers and 0.16 (95% CI = 0.07 to 0.25) for leukemia. For childhood exposure, the meta-ERR at 100 mGy for leukemia was 2.84 (95% CI = 0.37 to 5.32); there were only two eligible studies of all solid cancers.ConclusionsOur systematic assessments in this monograph showed that these new epidemiological studies are characterized by several limitations, but only a few positive studies were potentially biased away from the null. After exclusion of these studies, the majority of studies still reported positive risk estimates. We therefore conclude that these new epidemiological studies directly support excess cancer risks from low-dose ionizing radiation. Furthermore, the magnitude of the cancer risks from these low-dose radiation exposures was statistically compatible with the radiation dose-related cancer risks of the atomic bomb survivors.
| S-EPMC8454205 | biostudies-literature
Project description:BackgroundStudies examining total gestational weight gain (GWG) and outcomes associated with gestational age (GA) are potentially biased. The z-score has been proposed to mitigate this bias. We evaluated a regression-based adjustment for GA to remove the correlation between GWG and GA, and compared it to published weight-gain-for-gestational-age z-scores when applied to a study sample with different underlying population characteristics.MethodsUsing 65 643 singleton deliveries to normal weight women at 12 US clinical sites, we simulated a null association between GWG and neonatal mortality. Logistic regression was used to estimate approximate relative risks (RR) of neonatal mortality associated with GWG, unadjusted and adjusted for GA, and the z-score, overall and within study sites. Average RRs across 5000 replicates were calculated with 95% coverage probability to indicate model bias and precision, where 95% is nominal.ResultsUnder a simulated null association, total GWG resulted in a biased mortality estimate (RR = 0.87; coverage = 0%); estimates adjusted for GA were unbiased (RR = 1.00; coverage = 94%). Quintile-specific RRs ranged from 0.97-1.03. Similar results were observed for site-specific analyses. The overall z-score RR was 0.97 (84% coverage) with quintile-specific RRs ranging from 0.64-0.90. Estimates were close to 1.0 at most sites, with coverage from 70-94%. Sites 1 and 6 were biased with RRs of 0.66 and 1.43, respectively, and coverage of 70% and 80%.ConclusionsAdjusting for GA achieves unbiased estimates of the association between total GWG and neonatal mortality, providing an accessible alternative to the weight-gain-for-gestational-age z-scores without requiring assumptions concerning underlying population characteristics.
| S-EPMC4818698 | biostudies-literature
Project description:BackgroundResearchers face challenges in patient recruitment, especially for rare, fatal diseases such as amyotrophic lateral sclerosis (ALS). These challenges include obtaining sufficient statistical power as well as meeting eligibility requirements such as age, sex, and study proximity. Similarly, persons with ALS (PALS) face difficulty finding and enrolling in research studies for which they are eligible.ObjectiveThe aim of this study was to describe how the federal Agency for Toxic Substances and Disease Registry's (ATSDR) National ALS Registry is linking PALS to scientists who are conducting research, clinical trials, and epidemiological studies.MethodsThrough the Registry's online research notification mechanism (RNM), PALS can elect to be notified about new research opportunities. This mechanism allows researchers to upload a standardized application outlining their study design and objectives, and proof of Institutional Review Board approval. If the application is approved, ATSDR queries the Registry for PALS meeting the study's specific eligibility criteria, and then distributes the researcher's study material and contact information to PALS via email. PALS then need to contact the researcher directly to take part in any research. Such an approach allows ATSDR to protect the confidentiality of Registry enrollees.ResultsFrom 2013 to 2019, a total of 46 institutions around the United States and abroad have leveraged this tool and over 600,000 emails have been sent, resulting in over 2000 patients conservatively recruited for clinical trials and epidemiological studies. Patients between the ages of 60 and 69 had the highest level of participation, whereas those between the ages of 18 and 39 and aged over 80 had the lowest. More males participated (4170/7030, 59.32%) than females (2860/7030, 40.68%).ConclusionsThe National ALS Registry's RNM benefits PALS by connecting them to appropriate ALS research. Simultaneously, the system benefits researchers by expediting recruitment, increasing sample size, and efficiently identifying PALS meeting specific eligibility requirements. As more researchers learn about and use this mechanism, both PALS and researchers can hasten research and expand trial options for PALS.
| S-EPMC8693186 | biostudies-literature
Project description:For studies examining risk factors of sexually transmitted infections (STIs), confounding can stem from characteristics of partners of study subjects, and persist after adjustment for the subjects' individual-level characteristics. Two conditions that can result in confounding by the subjects' partners are: (C1) partner choice is assortative by the risk factor examined and, (C2) sexual activity is associated with the risk factor. The objective of this paper is to illustrate the potential impact of the assortativity bias in studies examining STI risk factors, using smoking and human papillomavirus (HPV) as an example. We developed an HPV transmission-dynamic mathematical model in which we nested a cross-sectional study assessing the smoking-HPV association. In our base case, we assumed (1) no effect of smoking on HPV, and (2) conditions C1-C2 hold for smoking (based on empirical data). The assortativity bias caused an overestimation of the odds ratio (OR) in the simulated study after perfect adjustment for the subjects' individual-level characteristics (adjusted OR 1·51 instead of 1·00). The bias was amplified by a lower basic reproductive number (R 0), greater mixing assortativity and stronger association of smoking with sexual activity. Adjustment for characteristics of partners is needed to mitigate assortativity bias.
| S-EPMC9150526 | biostudies-literature
Project description:mRNA abundance microarrays are the most widely used high-throughput technology for functional genomics. But despite this extensive characterization, and despite their wide-spread adoption in discovery research, the uptake of microarrays as clinical diagnostic tools remains relatively limited due to sample degradation. Recently, the NuGEN WT-Ovation Pico kit was developed to resolve this problem. Here, we comprehensively evaluate its utility on two separate biological problems. First, we evaluate its effect on 12 malignant and 2 normal cervix samples. Second, we evaluate its effect on 2 normal kidney samples. In each case, we directly compare aliquots of the same RNA sample that are prepared by standard and low-input/low-RIN protocols to address three key questions. First, does the use of a low-input/degraded-sample protocol alter transcriptional profiles either globally or of specific genes? Second, do these changes alter biological conclusions drawn from the dataset? Third, are these changes biased towards specific classes of genes, either in terms of their sequence characteristics (e.g. length, GC-content) or their biological function?
2014-01-01 | GSE42764 | GEO
Project description:mRNA abundance microarrays are the most widely used high-throughput technology for functional genomics. But despite this extensive characterization, and despite their wide-spread adoption in discovery research, the uptake of microarrays as clinical diagnostic tools remains relatively limited due to sample degradation. Recently, the NuGEN WT-Ovation Pico kit was developed to resolve this problem. Here, we comprehensively evaluate its utility on two separate biological problems. First, we evaluate its effect on 12 malignant and 2 normal cervix samples. Second, we evaluate its effect on 2 normal kidney samples. In each case, we directly compare aliquots of the same RNA sample that are prepared by standard and low-input/low-RIN protocols to address three key questions. First, does the use of a low-input/degraded-sample protocol alter transcriptional profiles either globally or of specific genes? Second, do these changes alter biological conclusions drawn from the dataset? Third, are these changes biased towards specific classes of genes, either in terms of their sequence characteristics (e.g. length, GC-content) or their biological function? To achieve these goals, we selected 12 frozen cervix cancers and 2 unmatched normal samples. RNA was extracted and was split into two aliquots: one was prepared using the Nugen WT-Ovation Pico kit and the other using the standard Affymetrix Express array method. This approach allows us to directly assess the effect of sample preparation, in isolation of any other effects. These data were then pre-processed and the filtered genes were subject to linear-modeling to evaluate technical and biological variability. The resulting gene sets were then subject to pathway and sequence analysis, and LTR analysis. Lastly, to generalize these conclusions we repeated this experimental approach identically in two normal kidney samples and one RNA mixture control sample.
2014-01-01 | E-GEOD-42764 | biostudies-arrayexpress
Project description:Objective: To analyze those factors contributing to the diagnostic delay in ALS. Methods: Consecutive ALS patients were categorized as those studied in departmental hospitals and those studied in a referral ALS center. Demographic and clinical variables, together with data of the diagnostic pathway were collected. Multivariable models were used to assess their effect in the time between symptoms onset and the first neurologist visit (time symptoms-neurologist), in the time between the first neurologist visit and the diagnosis (time neurologist-diagnosis) and in the diagnostic delay. Results: 166 ALS patients with a median diagnostic delay of 11.53 months (IQR: 6.68, 15.23) were included. The median diagnostic delay was 8.57 months (5.16, 11.61) in the referral center vs. 12.08 months (6.87, 16.8) in departmental centers. Bulbar onset, fast progression rate, upper motor neuron predominant phenotype and an early referral to the neurologist were associated with a shorter time between symptoms-neurologist. Being studied in a referral center was associated with a shorter time between neurologist-diagnosis. Comorbidities, familial ALS, bulbar onset, early referral to the neurologist and being studied in a referral center were associated with a shorter diagnostic delay. For patients studied in departmental hospitals, fast progression rate was also strongly associated with a shorter time between neurologist-diagnosis and diagnostic delay. Conclusion: Unmodifiable factors (comorbidities, familial ALS, bulbar onset, and progression rate) as well as modifiable factors (early referral to the neurologist and the evaluation in an ALS referral center) have an independent effect in the diagnostic delay. The universalization of ALS Units is probably the most efficient measure to reduce the diagnostic delay.
| S-EPMC7775542 | biostudies-literature
Project description:BackgroundThe described phenotype of the polycystic ovary syndrome (PCOS) has been primarily based on findings in a referred (self or otherwise) population. It is possible that the phenotype of PCOS would be different if the disorder were to be detected and studied in its natural (unbiased) state.ObjectiveOur objective was to compare the phenotype of PCOS detected in an unselected population with that identified in a referral population.ParticipantsParticipants included 292 PCOS patients identified at a tertiary care outpatient facility (referral PCOS) and 64 PCOS women (unselected PCOS) identified through the screening of a population of 668 seeking a pre-employment physical. Among the women undergoing a pre-employment physical, 563 did not demonstrate features of the disorder (unselected controls). All PCOS subjects met the National Institutes of Health 1990 criteria for the disorder.Main outcome measuresWe estimated prevalence of obesity and severity of disease burden.ResultsReferral PCOS subjects had greater mean body mass index and hirsutism score and higher degrees of hyperandrogenemia, were more likely to be non-Hispanic White (83.90%), and demonstrated a more severe PCOS subphenotype than unselected PCOS or unselected controls. The prevalence of obesity and severe obesity in referral PCOS was 2.3 and 2.5 times greater than estimates of the same in unselected PCOS and 2.2 and 3.8 times greater than estimates in unselected controls, respectively. Alternatively, unselected PCOS subjects had a prevalence of obesity and severe obesity and a mean body mass index similar to those of the general population from which they were derived.ConclusionThe phenotype of PCOS, including the racial/ethnic mix, severity of presentation, and rate of obesity, is affected significantly by whether the PCOS subject arises from a referral population or through unselected screening, likely reflecting the degree of patient concern and awareness and access to healthcare.
| S-EPMC3667270 | biostudies-literature
Project description:What are the most important and treatable pathogenic mechanisms in C9orf72-FTD/ALS? Model-based efforts to address this question are forging ahead at a blistering pace, often with conflicting results. But what does the human neuropathological literature reveal? Here, we provide a critical review of the human studies to date, seeking to highlight key gaps or uncertainties in our knowledge. First, we engage the C9orf72-specific mechanisms, including C9orf72 haploinsufficiency, repeat RNA foci, and dipeptide repeat protein inclusions. We then turn to some of the most prominent C9orf72-associated features, such as TDP-43 loss-of-function, TDP-43 aggregation, and nuclear transport defects. Finally, we review potential disease-modifying epigenetic and genetic factors and the natural history of the disease across the lifespan. Throughout, we emphasize the importance of anatomical precision when studying how candidate mechanisms relate to neuronal, regional, and behavioral findings. We further highlight methodological approaches that may help address lingering knowledge gaps and uncertainties, as well as other logical next steps for the field. We conclude that anatomically oriented human neuropathological studies have a critical role to play in guiding this fast-moving field toward effective new therapies.
| S-EPMC6546170 | biostudies-literature
Project description:BackgroundWhen analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated.ResultsWe show that when estimating the performance of classifiers on low signal datasets (i.e. those which are difficult to classify), which are typical of many prognostic microarray studies, commonly used performance measures can suffer from a substantial negative bias. For error rate this bias is only severe in quite restricted situations, but can be much larger and more frequent when using ranking measures such as the receiver operating characteristic (ROC) curve and area under the ROC (AUC). Substantial biases are shown in simulations and on the van 't Veer breast cancer dataset. The classification error rate can have large negative biases for balanced datasets, whereas the AUC shows substantial pessimistic biases even for imbalanced datasets. In simulation studies using 10-fold cross-validation, AUC values of less than 0.3 can be observed on random datasets rather than the expected 0.5. Further experiments on the van 't Veer breast cancer dataset show these biases exist in practice.ConclusionStratification bias can substantially affect several performance measures. In computing the AUC, the strategy of pooling the test samples from the various folds of cross-validation can lead to large biases; computing it as the average of per-fold estimates avoids this bias and is thus the recommended approach. As a more general solution applicable to other performance measures, we show that stratified repeated holdout and a modified version of k-fold cross-validation, balanced, stratified cross-validation and balanced leave-one-out cross-validation, avoids the bias. Therefore for model selection and evaluation of microarray and other small biological datasets, these methods should be used and unstratified versions avoided. In particular, the commonly used (unbalanced) leave-one-out cross-validation should not be used to estimate AUC for small datasets.
| S-EPMC2211509 | biostudies-literature