Abstract
Background: Case-control studies have been used extensively in determining the aetiology of rare diseases. However, case-control studies often suffer from participation bias in the control group, resulting in biased odds ratios that cause problems with interpretation. Participation bias can be hard to detect and is often ignored. Methods: Population data can be used in place of the possibly biased control group, to investigate whether participation bias may have affected the results in previous studies, or in place of controls in future studies. We demonstrate this approach by reanalysing and comparing the results of two case-control studies: Type 1 diabetes in Yorkshire children and stroke in Indian adults. Findings: Using population data to represent the control groups reduced the width of the confidence intervals given in the original studies and confirmed the findings for the two diabetes risk factors used; caesarean birth (odds ratio (OR) = 2.12 (1.53, 2.95) compared with 1.84 (1.09, 3.10)) and amniocentesis (OR = 3.38 (2.09, 5.47) compared with 3.85 (1.34, 11.04)). The three stroke risk factors investigated were found to have increased odds ratios when using population data; hypertension (OR = 5.645 (5.639, 5.650) compared with 3.807 (2.114, 6.856)), diabetes (OR = 12.212 (12.200, 12.224) compared with 3.473 (1.757, 6.866)) and smoking (OR = 5.701 (5.696, 5.707) compared with 2.242 (1.255, 4.005)). Interpretation: Participation bias can greatly affect the results of a study and cause some potential risk factors to be over-or underestimated. This approach allows previous studies to be investigated for participation bias and presents an alternative to a control group in future studies, while improving precision.
Highlights
Participation bias, a subset of selection bias, affects many study types and is often ignored by authors [1]
The diabetes data set used was taken from a case-control study [11], which had recorded cases of children under 16 years diagnosed with insulin-dependent diabetes mellitus (IDDM), or Type 1 diabetes, while resident in the area of the former Yorkshire Regional Health Authority, since 1978, with data collected 1993-1994
Participation bias can cause the results from studies to be inaccurate [5], especially in case-control studies where certain potential controls are more likely to participate than others
Summary
Participation bias, a subset of selection bias, affects many study types and is often ignored by authors [1]. It is well documented that case-control studies can be affected by participation bias in the control group [2]-[4], which can result in an over- or underestimation of odds ratios [5]. Routine data has become more widely available; partially due to advances in technology, increased routine data collection and emphasis on data sharing, along with the recent move towards and focus on Big Data Linked data sources such as hospital episode statistics (HES) [6], the clinical practice research database (CPRD) [7] and Research One [8] are allowing information to be shared more and further research to be carried out. We propose the use of population data in place of control data, along with the case data from a case-control study. We present a method to reduce the amount of bias from the control group; which can be used in place of controls in future case-control studies to save time and resources, or as an approach to evaluate the results from previous studies
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