Group Sequential Study Designs in Genetic-Epidemiological Case-Control Studies

2003 ◽  
Vol 56 (1-3) ◽  
pp. 63-72 ◽  
Author(s):  
Inke R. König ◽  
Andreas Ziegler
Keyword(s):  
Case Control ◽  
Case Control Studies ◽  
Group Sequential ◽  
Sequential Study ◽  
Study Designs

scholarly journals A probability model for evaluating the bias and precision of influenza vaccine effectiveness estimates from case-control studies

2014 ◽  
Vol 143 (7) ◽  
pp. 1417-1426 ◽  
Author(s):  
M. HABER ◽  
Q. AN ◽  
I. M. FOPPA ◽  
D. K. SHAY ◽  
J. M. FERDINANDS ◽  
...  
Keyword(s):  
Influenza Vaccine ◽  
Medical Care ◽  
Randomized Clinical Trials ◽  
Probability Model ◽  
Control Design ◽  
Case Control ◽  
Vaccine Effectiveness ◽  
Case Control Studies ◽  
Respiratory Illnesses ◽  
Study Designs

SUMMARYAs influenza vaccination is now widely recommended, randomized clinical trials are no longer ethical in many populations. Therefore, observational studies on patients seeking medical care for acute respiratory illnesses (ARIs) are a popular option for estimating influenza vaccine effectiveness (VE). We developed a probability model for evaluating and comparing bias and precision of estimates of VE against symptomatic influenza from two commonly used case-control study designs: the test-negative design and the traditional case-control design. We show that when vaccination does not affect the probability of developing non-influenza ARI then VE estimates from test-negative design studies are unbiased even if vaccinees and non-vaccinees have different probabilities of seeking medical care against ARI, as long as the ratio of these probabilities is the same for illnesses resulting from influenza and non-influenza infections. Our numerical results suggest that in general, estimates from the test-negative design have smaller bias compared to estimates from the traditional case-control design as long as the probability of non-influenza ARI is similar among vaccinated and unvaccinated individuals. We did not find consistent differences between the standard errors of the estimates from the two study designs.

Study designs which can demonstrate and test causation

2017 ◽  
Author(s):  
Mark Elwood
Keyword(s):  
Cohort Studies ◽  
Case Control ◽  
Control Group ◽  
Case Control Studies ◽  
Retrospective Case ◽  
Community Based ◽  
Cross Sectional ◽  
Healthcare Interventions ◽  
Intervention Trials ◽  
Study Designs

This chapter presents study designs which can test and show causation. Cohort and intervention studies compare people exposed to an agent or intervention with those unexposed or less exposed. Case-control studies compare people affected by a disease or outcome with a control group of unaffected people or representing a total population. Surveys select a sample of people, not chosen by exposure or outcome. Cohort studies may be prospective or retrospective; case-control studies are retrospective; surveys are cross-sectional in time, but retrospective or prospective aspects can be added. In part two, strengths, weaknesses and applications of these designs are shown. Intervention trials, ideally randomised, are the prime method of assessing healthcare interventions; special types include crossover trials and community-based trials. Non-randomised trials are noted. The strengths and weaknesses of cohort studies, case-control studies, and surveys are shown.

scholarly journals Do Case-Control Studies Always Estimate Odds Ratios?

2020 ◽  
Author(s):  
Jeremy A Labrecque ◽  
Myriam M G Hunink ◽  
M Arfan Ikram ◽  
M Kamran Ikram
Keyword(s):  
Incidence Rate ◽  
Case Control Study ◽  
Case Control ◽  
Small Sample ◽  
Incidence Density ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Rate Ratios ◽  
Study Designs ◽  
Control Study

Abstract Case-control studies are an important part of the epidemiologic literature, yet confusion remains about how to interpret estimates from different case-control study designs. We demonstrate that not all case-control study designs estimate odds ratios. On the contrary, case-control studies in the literature often report odds ratios as their main parameter even when using designs that do not estimate odds ratios. Only studies using specific case-control designs should report odds ratios, whereas the case-cohort and incidence-density sampled case-control studies must report risk ratio and incidence rate ratios, respectively. This also applies to case-control studies conducted in open cohorts, which often estimate incidence rate ratios. We also demonstrate the misinterpretation of case-control study estimates in a small sample of highly cited case-control studies in general epidemiologic and medical journals. We therefore suggest that greater care be taken when considering which parameter is to be reported from a case-control study.

Epidemiology

2017 ◽  
Author(s):  
Sadie Costello ◽  
Jennifer M. Cavallari ◽  
David H. Wegman ◽  
Marie S. O’Neill ◽  
Ellen A. Eisen
Keyword(s):  
Case Control ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Information Selection ◽  
Basic Principles ◽  
Potential Source ◽  
The Common ◽  
Key Aspects ◽  
Study Designs

This chapter describes the basic principles of epidemiology, emphasizing the aspects most relevant to studies of health effects from occupational and environmental exposures. Numerous examples are provided of how epidemiology can be used to identify and quantify the relations between recent or long-term exposure and health outcomes, such as prevalence or incidence of disease, injury, or mortality. The chapter describes the common study designs, including cohort studies, case-control studies, and cross-sectional studies, with examples of their application. Key aspects of exposure assessment and characterizing and quantifying exposure, are described. The three types of bias in epidemiology, information, selection, and confounding, are defined as well as the healthy worker effect, a potential source of bias unique in occupational studies. Study designs and analytic methods that can reduce or eliminate specific types of bias are also described. Finally, the chapter provides guidance on how to interpret the results of studies, with an eye toward causal inference.

Evidence Hierarchy

2020 ◽  
pp. 61-77
Author(s):  
Julie Sin
Keyword(s):  
Case Control ◽  
Weight Of Evidence ◽  
Case Control Studies ◽  
Cause And Effect ◽  
Making Sense ◽  
Course Of Action ◽  
Basic Concepts ◽  
The Many ◽  
Study Designs

This chapter is about making sense of evidence from research studies from a commissioner and evidence-user perspective. A basic evidence hierarchy is described for general orientation to the concept that some study designs are more reliable than others in attempting to understand cause and effect, and there is orientation to the main study types in the hierarchy (randomized trials, cohort and case-control studies, etc.) Clearly it is the overall weight of evidence for a particular course of action that is important, although familiarity with basic concepts of study robustness remains useful in itself for making sense of the many items of evidence that present in everyday practice. The value of evidence from studies addressing non cause and effect type questions (for example seeking understanding about behaviours and beliefs) is discussed. The role of the evidence-user in making use of these concepts and enabling evidence informed practice is also described.

Imaging of depressive disorders

2020 ◽  
pp. 797-806
Author(s):  
Guy M. Goodwin ◽  
Michael Browning
Keyword(s):  
Depressive Disorders ◽  
Case Control ◽  
Structure And Function ◽  
Small Sample ◽  
Case Control Studies ◽  
Brain Structure And Function ◽  
Small Sample Sizes ◽  
And Function ◽  
Study Designs ◽  
And Control

Neuroimaging techniques have been used extensively to compare brain structure and function between patients with, or at risk of, depression and control subjects. The goal of this work has largely been to identify pathophysiological processes in depression. However, progress in this field has been limited by the heterogeneity of patient populations, the use of small sample sizes, and an overreliance on case-control studies. These limitations have increasingly been acknowledged with recent work collecting much larger samples and employing a variety of study designs, including those able to stratify patient populations. This chapter reviews imaging studies in depression, highlighting both outstanding questions and promising recent findings.

Reducing sample sizes in genome scans: Group sequential study designs with futility stops

2003 ◽  
Vol 25 (4) ◽  
pp. 339-349 ◽  
Author(s):  
Inke R. König ◽  
Helmut Schäfer ◽  
Andreas Ziegler ◽  
Hans-Helge Müller
Keyword(s):  
Sample Sizes ◽  
Group Sequential ◽  
Genome Scans ◽  
Sequential Study ◽  
Study Designs

scholarly journals Optimized Group Sequential Study Designs for Tests of Genetic Linkage and Association in Complex Diseases

2001 ◽  
Vol 69 (3) ◽  
pp. 590-600 ◽  
Author(s):  
Inke R. König ◽  
Helmut Schäfer ◽  
Hans-Helge Müller ◽  
Andreas Ziegler
Keyword(s):  
Genetic Linkage ◽  
Complex Diseases ◽  
Group Sequential ◽  
Sequential Study ◽  
Study Designs
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