Sterne JA, Hernan MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919. pmid:27733354 Tourangeau R, Yan T. Sensitive questions in surveys. Psychol Bull. 2007;133(5):859–83. pmid:17723033 Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004;33(1):30–42. pmid:15075143 If you’re interested in purchasing this case, then consider two things before making your decision.

Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45(6):1866–86. pmid:28108528 Cosmos DB provides various consistency levels, ranging from strong to eventual consistency. Strong consistency ensures all replicas have the latest data version but may introduce additional latency. On the other hand, eventual consistency provides better performance but may lead to occasional stale data. How can different metrics be combined in meta-analysis? There are two issues to consider, one conceptual, one more technical. When studies report different ratio metrics, for example, hazard ratios, risk ratios, or odds ratios, they may be combined ignoring the differences in metrics. This may be appropriate depending on which study designs were included (cohort studies or case-control studies) and how participants were sampled in case-control studies [ 84, 85]. As a general rule, the different ratio metrics can be combined if the outcome under study is rare (<5%), which is often the case in etiologic studies. If the outcome is not rare, researchers must be more careful because the odds ratio will substantially overestimate the relative risk. This property of the odds ratio is a reflection of the fact that for non-rare outcomes, the odds is larger than the risk (for example, if the risk is 0.8, the corresponding odds is 4). Funding: ME was supported by special project funding (Grant No. 174281) from the Swiss National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371(9612):569–78. pmid:18280327Sterne JA, Egger M. Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. J Clin Epidemiol. 2001;54(10):1046–55. pmid:11576817 Gielen C, Dekkers O, Stijnen T, Schoones J, Brand A, Klautz R, et al. The effects of pre- and postoperative fibrinogen levels on blood loss after cardiac surgery: a systematic review and meta-analysis. Interact Cardiovasc Thorac Surg. 2014;18(3):292–8. pmid:24316606 Mountjoy E, Davies NM, Plotnikov D, Smith GD, Rodriguez S, Williams CE, et al. Education and myopia: assessing the direction of causality by mendelian randomisation. BMJ. 2018;361:k2022. pmid:29875094

The assessment of methodological aspects of studies is a crucial component of any systematic review. Observational studies may yield estimates of associations that deviate from true underlying relationships due to confounding or biases. Meta-analyses of observational studies may therefore produce ‘very precise but equally spurious’ results [ 41].Petersen I, Douglas I, Whitaker H. Self controlled case series methods: an alternative to standard epidemiological study designs. BMJ. 2016;354:i4515. pmid:27618829 Azure Cosmos DB pricing is based on several factors: provisioned throughput, storage consumed, and data transfer. It offers various pricing models, such as provisioned throughput, serverless, and reserved capacity, allowing customers to choose the most suitable option for their workload. The provisioned throughput model provides predictable performance with options for manual or automatic scaling, while the serverless model offers automatic scaling based on demand, suitable for sporadic workloads. Additionally, reserved capacity allows customers to save costs by committing to a specific capacity for longer. Data transfer costs are determined by the volume of data transferred in and out of Azure Cosmos DB. In case-control studies, exposures are compared between people with the outcome of interest (cases) and people without (controls) [ 3]. The design is especially efficient for rare outcomes. Ponjoan A, Blanch J, Alves-Cabratosa L, Marti-Lluch R, Comas-Cufi M, Parramon D, et al. Effects of extreme temperatures on cardiovascular emergency hospitalizations in a Mediterranean region: a self-controlled case series study. Environ Health. 2017;16(1):32. pmid:28376798 Build a Job Winning Data Engineer Portfolio with Solved End-to-End Big Data Projects. Consistency Levels