| 1 |
Studer et al. 3838. Kaerlev L, Kolstad HA, Hansen AM, Thomsen JF, Kærgaard A, Rugulies R, et al. Are risk estimates biased in follow-up studies of psychosocial factors with low base-line participation? BMC Public Health 2011; 11:539.
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To evaluate differences in substance use between late and early respondents, non-consenters or silent refusers, and whether converting former non-respondents can reduce non-response bias |
Baseline information |
Logistic model |
Late respondents presented a midway pattern of substance use higher than early respondents, but lower than non-consenters |
| 2 |
Kaerlev et al. 3939. Langley JD, Lilley R, Wilson S, Derrett S, Samaranayaka A, Davie G, et al. Factors associated with non-participation in one or two follow-up phases in a cohort study of injured adults. Inj Prev 2013; 19:428-33.
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To examine bias on the association between occupational stressors and mental health due to non-participation in a prospective cohort |
Secondary data |
Survival model |
Proportions of gender, age, employment status, sick leave and hospitalization for affective disorders were different in respondents and non-respondents, but low participation at baseline was not associated with mental health outcome |
| 3 |
Langley et al. 4040. Alkerwi A, Sauvageot N, Couffignal S, Albert A, Lair M-L, Guillaume M. Comparison of participants and non-participants to the ORISCAV-LUX population-based study on cardiovascular risk factors in Luxembourg. BMC Med Res Methodol 2010; 10:80.
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To evaluate factors associated with non-participation in two follow-up contacts of a prospective cohort study of injury outcomes |
Baseline information |
Poisson model |
Non-participation in the closest follow-up contact did not mean non-participation in the next contact; sociodemographic factors were the most important for non-participation |
| 4 |
Alkerwi et al. 4141. Langhammer A, Krokstad S, Romundstad P, Heggland J, Holmen J. The HUNT study: participation is associated with survival and depends on socioeconomic status, diseases and symptoms. BMC Med Res Methodol 2012; 12:143.
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To evaluate the representativeness of the sample with respect to the population and compare characteristics of participants and non participants |
Baseline information |
Logistic model |
Non-participants were similar to participants in gender and place of residence; younger people were under-represented while adults and elderly were over-represented; no discriminating health profiles were detected |
| 5 |
Langhammer et al. 4242. Eriksson A-K, Ekbom A, Hilding A, Ostenson C-G. The influence of non-response in a population-based cohort study on type 2 diabetes evaluated by the Swedish Prescribed Drug Register. Eur J Epidemiol 2012; 27:153-62.
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To study potential participation bias for common symptoms, diseases and socioeconomic status and mortality by participation status |
Secondary data, mailed questionnaire. |
Negative binomial and survival models |
Questionnaire answers indicated higher prevalences of cardiovascular diseases, diabetes mellitus and psychiatric disorders among non-participants; registry data showed higher mortality and lower socioeconomic status among non-participants |
| 6 |
Eriksson et al. 4343. Osler M, Kriegbaum M, Christensen U, Holstein B, Nybo Andersen A-M. Rapid report on methodology: does loss to follow-up in a cohort study bias associations between early life factors and lifestyle-related health outcomes? Ann Epidemiol 2008; 18:422-4.
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To assess selective non-response in population-based cohort study on type 2 diabetes, using the population-based drug register for the Stockholm Diabetes Prevention Program |
Secondary data |
Logistic model |
At baseline, non-participants and participants were similar. At follow-up, risks were higher among non-participants |
| 7 |
Osler et al. 4444. Buckley B, Murphy AW, Glynn L, Hennigan C. Selection bias in enrollment to a programme aimed at the secondary prevention of ischaemic heart disease in general practice: a cohort study. Int J Clin Pract 2007; 61:1767-72.
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To evaluate changes in association measures in early-life aspects and later health outcomes due to non-response in a follow-up survey |
Secondary data |
Logistic model and comparison of odds ratios between respondents and complete cohort |
A low response rate at age 50 years was related to having a single mother at birth, low educational attainment at age 18, and low cognitive function at ages 12 and 18. The risk of alcohol overuse and tobacco-related diseases was also highest among non-respondents |
| 8 |
Buckley et al. 4545. Schmidt CO, Raspe H, Pfingsten M, Hasenbring M, Basler HD, Eich W, et al. Does attrition bias longitudinal population-based studies on back pain? Eur J Pain 2011; 15:84-91.
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To assess baseline differences in participation in a secondary prevention of ischemic heart disease program |
Secondary data |
Logistic model |
Enrollment was lower for women in general and for men with uncontrolled total cholesterol level |
| 9 |
Schmidt et al. 4646. Martikainen PT, Valkonen T. Excess mortality of unemployed men and women during a period of rapidly increasing unemployment. Lancet 1996; 348:909-12.
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To identify back-pain-related indicators that could predict attrition in longitudinal studies |
Baseline information |
Logistic model |
The best predictors of attrition were age and baseline response behavior. No bias was found in relation to back pain indicators |
| 10 |
Martikainen et al. 4747. Holden L, Ware RS, Passey M. Characteristics of nonparticipants differed based on reason for nonparticipation: a study involving the chronically ill. J Clin Epidemiol 2008; 61:728-32.
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To estimate impact on social class inequalities in health due to non-response |
Secondary data |
Linear regression model |
Higher social class employees and women were more likely to participate, and sickness absence was higher in non-respondents. Social classes differences did not impact sickness absence in participants or non-participants |
| 11 |
Holden et al. 4848. Taylor AW, Dal Grande E, Gill T, Chittleborough CR, Wilson DH, Adams RJ, et al. Do people with risky behaviours participate in biomedical cohort studies? BMC Public Health 2006; 6:11.
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To explore reasons for non-participation in a chronic disease management program |
Secondary data |
Logistic and multinomial model |
Reasons for loss-to-follow-up were: refusals – related to older age, female gender and heart failure; untraceable people – younger, single, indigenous; and death – older individuals, male, who had cancer or heart failure |
| 12 |
Lissner et al. 1818. Boshuizen HC, Viet AL, Picavet HSJ, Botterweck A, van Loon AJM. Non-response in a survey of cardiovascular risk factors in the Dutch population: determinants and resulting biases. Public Health 2006; 120:297-308.
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To describe 32 years of follow-up of a cohort of women receiving several health examinations |
Baseline information, home visits to non-respondents |
Linear regression model |
Among the 64% of survivors, non-participants and home visited subjects were similar in regard to anthropometry and blood pressure, and both groups were similar to participants in social indicators |
| 13 |
Stang et al. 1717. Lissner L, Skoog I, Andersson K, Beckman N, Sundh V, Waern M, et al. Participation bias in longitudinal studies: experience from the Population Study of Women in Gothenburg, Sweden. Scand J Prim Health Care 2003; 21:242-7.
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To compare recruitment strategies and baseline characteristics of participants and non-participants |
Sample of the population |
Frequencies comparison |
Nonparticipants were more often smokers and of lower social class. A regular relationship with a partner was more frequent among participants |
| 14 |
Goldberg et al. 2121. Walker M, Shaper AG, Cook DG. Non-participation and mortality in a prospective study of cardiovascular disease. J Epidemiol Community Health 1987; 41:295-9.
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To evaluate several variables associated with participation in the French GAZEL cohort |
Baseline information |
Mixed effects logistic model |
Male and older employees in managerial position or retired presented higher response rates. Smoking and alcohol drinking predicted lower participation. Health problems were strong predictors of attrition |
| 15 |
Taylor et al. 4949. Alonso A, Seguí-Gómez M, de Irala J, Sánchez-Villegas A, Beunza JJ, Martínez-Gonzalez MA. Predictors of follow-up and assessment of selection bias from dropouts using inverse probability weighting in a cohort of university graduates. Eur J Epidemiol 2006; 21:351-8.
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To analyze the association between health-related and socio-demographic indicators and participation in a biomedical cohort study |
Sample of the population |
Frequencies comparison |
Cohort participants were similar to the source population, except for alcohol consumption, which, at an intermediate to high risk level was more frequent among participants |
| 16 |
Alonso et al. 5050. Bergman P, Ahlberg G, Forsell Y, Lundberg I. Non-participation in the second wave of the PART study on mental disorder and its effects on risk estimates. Int J Soc Psychiatry 2010; 56:119-32.
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To evaluate potential predictors of retention in a cohort study and selection bias effect in rate ratio estimates due to loss-to-follow-up |
Baseline information |
Inverse probability weight logistic model |
Several variables (age, smoking, marital status, obesity, past vehicle injury and self-reported history of cardiovascular disease) were associated with the probability of attrition. Obesity, when adjusted for confounding, was similarly associated with hypertension in models with and without inverse probability weight |
| 17 |
Knudsen et al. 2020. Goldberg M, Chastang JF, Zins M, Niedhammer I, Leclerc A. Health problems were the strongest predictors of attrition during follow-up of the GAZEL cohort. J Clin Epidemiol 2006; 59:1213-21.
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To evaluate characteristics such as health status and specific health problems of non-participants in population-based study, and the potential resulting bias in association measures |
Secondary data |
Survival model, simulation |
Nonparticipants were twice as likely to receive disability pensions (outcome) than participants, and even more if the pension was received for mental disorders. Simulation excluding participants with a similar profile to non-participants reduced the association between common mental disorders and the outcome |
| 18 |
Manjer et al. 3030. Van Loon AJM, Tijhuis M, Picavet HSJ, Surtees PG, Ormel J. Survey non-response in the Netherlands: effects on prevalence estimates and associations. Ann Epidemiol 2003; 13:105-10.
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To investigate the effect of non-participation on cancer incidence and mortality |
Secondary data, mailed health survey |
Survival model |
Non-participants presented lower cancer incidence prior to recruitment and higher cancer incidence during recruitment. The proportion of participants in the cohort reporting better health was higher than in the mailed survey |
| 19 |
Barchielli & Balzi 2525. Putnam RD. Tuning in, tuning out: the strange disappearance of social capital in America. PS Polit Sci Polit 1995; 28:664-83.
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To analyze the effect on mortality of non-response in a smoking prevalence survey |
Secondary data |
Poisson model, life table method |
All causes mortality was significantly higher among non-respondents, with higher risks for smoking related causes |
| 20 |
Bergman et al. 5151. Petersen MA, Pedersen L, Groenvold M. Does nonparticipation in studies of advanced cancer lead to biased quality-of-life scores? J Palliat Med 2009; 12:1023-8.
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To analyze the consequences of attrition in three years after baseline in the PART study |
Baseline information, sample of non-respondents |
Logistic model |
Variables associated with non-participation – low income and education, non-Nordic origin and marital status – were related with depressive mood as well in the first wave |
| 21 |
Petersen et al. 5252. Rao RS, Sigurdson AJ, Doody MM, Graubard BI. An application of a weighting method to adjust for nonresponse in standardized incidence ratio analysis of cohort studies. Ann Epidemiol 2005; 15:129-36.
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To investigate wether terminally ill patients’ reported quality-of-life scores should be adjusted for non-participation bias |
Baseline information |
Imputation methods for missing data |
Significant underestimation of symptoms in 4 out of 30 comparisons suggest that imputation of quality-of-life scores of non-participants in palliative care is biased based on the available predictors |
| 22 |
Rao et al. 5353. Haring R, Alte D, Völzke H, Sauer S, Wallaschofski H, John U, et al. Extended recruitment efforts minimize attrition but not necessarily bias. J Clin Epidemiol 2009; 62:252-60.
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To propose a method based on propensity scores to analytically reduce bias due to non-response |
Secondary data |
Propensity score based on baseline information and data imputation |
Among the respondents, there was a higher frequency of women, Caucasian, married and younger people. Differences due to the proposed weighting scheme were small |
| 23 |
Haring et al. 5454. Drivsholm T, Eplov LF, Davidsen M, Jørgensen T, Ibsen H, Hollnagel H, et al. Representativeness in population-based studies: a detailed description of non-response in a Danish cohort study. Scand J Public Health 2006; 34:623-31.
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To determine attrition predictors and evaluate the effect of extensive recruitment procedures on attrition and bias |
Baseline information |
Logistic model |
The main predictors for attrition were late recruitment at baseline, unemployment, low educational level, female gender, and smoking. However attrition bias was not associated with health-related indicators |
| 24 |
Van Loon et al. 3131. Jacobsen TN, Nohr EA, Frydenberg M. Selection by socioeconomic factors into the Danish National Birth Cohort. Eur J Epidemiol 2010; 25:349-55.
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To investigate possible response bias in prevalence estimation and association measures |
Baseline information |
Logistic model |
Respondents, as compared to non-respondents, presented higher socioeconomic status, better subjective health and healthier behaviors. The association measures were similar in respondents and the entire population source |
| 25 |
Drivsholm et al. 5555. Young AF, Powers JR, Bell SL. Attrition in longitudinal studies: who do you lose? Aust N Z J Public Health 2006; 30:353-61.
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To compare participants at the 20-year follow-up study with non-participants, and to investigate the representativeness of both groups in relation to the population source |
Secondary data |
Logistic model |
Participation decreased to 65% in the 20th follow-up year, when non-participants had lower socioeconomic status, worse health profile and higher mortality rate than participants |
| 26 |
Jackson et al. 1616. Stang A, Moebus S, Dragano N, Beck EM, Möhlenkamp S, Schmermund A, et al. Baseline recruitment and analyses of nonresponse of the Heinz Nixdorf Recall Study: identifiability of phone numbers as the major determinant of response. Eur J Epidemiol 2005; 20:489-96.
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To compare participants with complete clinical examinations to those with just home interview in the the ARIC study |
Baseline information |
Frequencies comparison |
Response rates was similar for white participants, both male and female, and in all study centers. In general, respondents presented higher socioeconomic status and health, but differences were smaller for women |
| 27 |
Veenstra et al. 2929. Manjer J, Carlsson S, Elmståhl S, Gullberg B, Janzon L, Lindström M, et al. The Malmö Diet and Cancer Study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer Prev 2001; 10:489-99.
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To assess association between health status at baseline and nonresponse; to analyze survival in a 5-year follow-up |
Secondary data |
Logistic model |
Among respondents, prevalence of coronary heart disease was higher. However, their mortality was lower than noncontacts |
| 28 |
Young et al. 5656. Caetano R, Ramisetty-Mikler S, McGrath C. Characteristics of non-respondents in a US national longitudinal survey on drinking and intimate partner violence. Addict 2003; 98:791-7.
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To describe factors associated with attrition in a longitudinal study with three age cohorts of women |
Baseline information |
Logistic model |
Variables associated with loss-to-follow-up were: education (lower), non-English-speaking origin, current smoker, poorer health and difficulty managing their income, varying according to cohort age |
| 29 |
Caetano et al. 5757. Hara M, Higaki Y, Imaizumi T, Taguchi N, Nakamura K, Nanri H, et al. Factors influencing participation rate in a baseline survey of a genetic cohort in Japan. J Epidemiol 2010; 20:40-5.
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To identify characteristics of non-respondents in a survey among couples on violence and drinking |
Secondary data |
Logistic model |
Male non-respondents were younger, less educated, more often unemployed and drinkers. Among women, having been an abuse victim during childhood increased response |
| 30 |
Garcia et al. 2828. Veenstra MY, Friesema IHM, Zwietering PJ, Garretsen HFL, Knottnerus JA, Lemmens PHHM. Lower prevalence of heart disease but higher mortality risk during follow-up was found among nonrespondents to a cohort study. J Clin Epidemiol 2006; 59:412-20.
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To evaluate attrition in a Spanish population-based cohort |
Baseline information |
Logistic model |
Death and moving to another town were the main reasons of nonresponse. Refusals were associated with working status (disabled and retired) and place of birth (other regions of Spain or in foreign countries); emigration with civil status, age and education as well |
| 31 |
Hara et al. 5858. Montgomery MP, Kamel F, Hoppin JA, Beane Freeman LE, Alavanja MCR, Sandler DP. Effects of self-reported health conditions and pesticide exposures on probability of follow-up in a prospective cohort study. Am J Ind Med 2010; 53:486-96.
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To examine factors influencing the recruitment in a study collecting genetic data |
Baseline information |
Logistic model |
Sex (male) and age (younger) presented lower participation rates. The survey location (easy access to participants’ residence) and reminders sent to subjects significantly improved the participation rate |
| 32 |
Kjoller & Thoning 3333. Carter KN, Imlach-Gunasekara F, McKenzie SK, Blakely T. Differential loss of participants does not necessarily cause selection bias. Aust N Z J Public Health 2012; 36:218-22.
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To analyze trends in nonresponse and assess bias on morbidity prevalence |
Secondary data |
Logistic model |
Refusals increased 4.3% in seven years (from 1987 to 1994). Nonrespondents were defined by a combination of sociodemographic characteristics. Nonrespondents hospital admission rates were higher than respondents six months before data collection, and similar afterwards |
| 33 |
Jacobsen et al. 3232. Kjøller M, Thoning H. Characteristics of non-response in the Danish Health Interview Surveys, 1987-1994. Eur J Public Health 2005; 15:528-35.
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To evaluate associations between socioeconomic factors and participation in the Danish National Birth Cohort |
Secondary data |
Frequencies comparison |
Groups with low socioeconomic status were underrepresented as compared to the background population |
| 34 |
Montgomery et al. 5959. Jousilahti P, Salomaa V, Kuulasmaa K, Niemelä M, Vartiainen E. Total and cause specific mortality among participants and non-participants of population based health surveys: a comprehensive follow up of 54 372 Finnish men and women. J Epidemiol Community Health 2005; 59:310-5.
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To investigated potential bias due to non-participation in the follow-up of a large cohort study on pesticide applicators |
Secondary data |
Logistic model |
Non-respondents at follow-up were younger, less educated, with lower body mass index and poorer health behaviors but better health conditions, and lower pesticide use. Estimates of exposure-disease associations did not present strong bias |
| 35 |
Jousilahti et al. 6060. May AM, Adema LE, Romaguera D, Vergnaud A-C, Agudo A, Ekelund U, et al. Determinants of non-response to a second assessment of lifestyle factors and body weight in the EPIC-PANACEA study. BMC Med Res Methodol 2012; 12:148.
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To evaluate total and cause specific mortality comparing participants cohort study |
Secondary data |
Survival model |
At eight year follow up, mortality of non-participating men and women was higher than participating, except for smoking related causes |
| 36 |
May et al. 6161. Batty GD, Gale CR. Impact of resurvey non-response on the associations between baseline risk factors and cardiovascular disease mortality: prospective cohort study. J Epidemiol Community Health 2009; 63:952-5.
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To evaluate potential predictors of non-response that are available at baseline (socio-economic-demographic, health, )follow-up duration and contact strategies |
Baseline information |
Logistic model |
Age (younger), sex (male), marital status (single), poorer health conditions, and undernourishment or obesity were associated with non-response |
| 37 |
Batty & Gale 6262. Dugué P-A, Lynge E, Rebolj M. Mortality of non-participants in cervical screening: register-based cohort study. Int J Cancer 2014; 134:2674-82.
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To investigated variables associated with non-response and its impact on the association measures of several known risk factors and cardiovascular mortality |
Secondary data |
Survival model |
The non-participants had higher CVD mortality than participants. However, the association measures between the risk factors evaluated and the mortality was not affected by non-response |
| 38 |
Dugue et al. 6363. Benfante R, Reed D, MacLean C, Kagan A. Response bias in the Honolulu Heart Program. Am J Epidemiol 1989; 130:1088-100.
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To estimate excess mortality comparing participants and non-participants in cervical screening |
Secondary data |
Survival model |
All cause mortality and HPV-related mortality was higher for non-participants in cervical screening, and the hazard ratio increased over time |
| 39 |
Hara et al. 2323. Ferrie JE, Kivimäki M, Singh-Manoux A, Shortt A, Martikainen P, Head J, et al. Non-response to baseline, non-response to follow-up and mortality in the Whitehall II cohort. Int J Epidemiol 2009; 38:831-7.
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To evaluate the healthy volunteer effect comparing mortality rates among respondents and nonrespondents |
Secondary data |
Poisson model |
Mortality was higher among nonrespondents for all causes studied, although with different effects according do sex. The relative risk varied as well according to the length of follow-up |
| 40 |
Benfante et al. 6464. François Y, Truan P, Gmel G. Response rate and analysis of non-responses in a cohort study. Soz Praventivmed 1997; 42:186-91.
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To investigate differences between participants and nonparticipants and the potential introduction of bias in the association measures |
Secondary data |
Frequencies comparison |
Total mortality, cancer mortality, and coronary heart disease incidence rates were higher in non-examined men, but the differences decreased over time. No bias was found |
| 41 |
Ferrie et al. 2424. Barchielli A, Balzi D. Nine-year follow-up of a survey on smoking habits in Florence (Italy): higher mortality among non-responders. Int J Epidemiol 2002; 31:1038-42.
|
To evaluate association between nonresponse at baseline and missing follow-up contacts and general mortality, and mortality by socioeconomic position |
Secondary data |
Survival model |
Non-response at baseline and at any follow-up contact was associated with doubling the mortality hazard |
| 42 |
François et al. 6565. David MC, van der Pols JC, Williams GM, Alati R, Green AC, Ware RS. Risk of attrition in a longitudinal study of skin cancer: logistic and survival models can give different results. J Clin Epidemiol 2013; 66:888-95.
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To demonstrate how it is possible to obtain a satisfactory rate of participation in a cohort study, and to compare participants and nonparticipants |
Baseline information |
Frequencies comparison |
The main factors associated with the response rate were: linguistic region, age, income, civil status, educational and alcohol/drugs consumption |
| 43 |
Walker et al. 2222. Hara M, Sasaki S, Sobue T, Yamamoto S, Tsugane S. Comparison of cause-specific mortality between respondents and nonrespondents in a population-based prospective study: ten-year follow-up of JPHC Study Cohort I. Japan Public Health Center. J Clin Epidemiol 2002; 55:150-6.
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To compare the mortality rates and the demographic characteristics between participants and nonparticipants |
Secondary data |
Frequencies comparison |
Non-participants were younger, more likely to be unmarried and work in less skilled jobs. Their mortality rates were higher in the first three years of follow-up, decreasing afterwards. CVD mortality was similar in both groups |
| 44 |
David et al. 6666. Froom P, Melamed S, Kristal-Boneh E, Benbassat J, Ribak J. Healthy volunteer effect in industrial workers. J Clin Epidemiol 1999; 52:731-5.
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To assess the performance of two different models with two end points each, in analyzing loss-to-follow-up |
Secondary data |
Logistic and survival model |
Survival models performed better than logistic models |
| 45 |
Froom et al. 6767. Bopp M, Braun J, Faeh D, Gutzwiller F; Swiss National Cohort Study Group. Establishing a follow-up of the Swiss MONICA participants (1984-1993): record linkage with census and mortality data. BMC Public Health 2010; 10:562.
|
To investigate the healthy volunteer effect in an occupationally cohort of male industrial employees |
Secondary data |
Survival model |
All cause mortality hazard ratio was higher in nonparticipants, and the difference persisted up to 8 years of follow-up |
| 46 |
Bopp et al. 6868. Criqui MH, Barrett-Connor E, Austin M. Differences between respondents and non-respondents in a population-based cardiovascular disease study. Am J Epidemiol 1978; 108:367-72.
|
To evaluate feasibility and quality of linkage procedure in providing follow-up information |
Secondary data |
Survival model |
Linkage success was independent of any variables. Losses in 10 years were 4.7%. Participants of the study had lower mortality than the general population |
| 47 |
Criqui et al. 6969. Lindsted KD, Fraser GE, Steinkohl M, Beeson WL. Healthy volunteer effect in a cohort study: temporal resolution in the Adventist Health Study. J Clin Epidemiol 1996; 49:783-90.
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To evaluate differences in cardiovascular health status according to participation in a population based study |
Baseline information, non-respondents telephone interview |
Frequencies comparison |
Non-respondents presented more CVD but did not differ on known hypertension. Impact on prevalence estimates was small due to low proportion of non-response |
| 48 |
Lindsted et al. 7070. Thygesen LC, Johansen C, Keiding N, Giovannucci E, Grønbaek M. Effects of sample attrition in a longitudinal study of the association between alcohol intake and all-cause mortality. Addict 2008; 103:1149-59.
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To assess the healthy volunteer effect comparing mortality rates between the respondents to a small questionnaire with respondents to a full detailed questionnaire |
Secondary data |
Survival model |
Hazard ratio for different mortality causes was larger for non-respondents, but the difference decreased over time |
| 49 |
Thygesen et al. 7171. Vestbo J, Rasmussen FV. Baseline characteristics are not sufficient indicators of non-response bias follow up studies. J Epidemiol Community Health 1992; 46:617-9.
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To estimate the effect of drop-out on the association between alcohol intake and mortality |
Secondary data |
Poisson model |
Loss to-follow-up was associated with increased mortality and incidence rates of heart disease, some cancers, and liver diseases related to alcohol intake |
| 50 |
Vestbo & Rasmussen 72
|
To evaluate if baseline characteristics could provide sufficient information about non-response bias |
Secondary data |
Logistic model |
At baseline, respondents and non-respondents presented similar profiles (smoking, lung function and respiratory symptoms). However, non-respondents had larger rates of hospital admission due to respiratory diseases, indicating that equal baseline profile does not protect against non-response bias |
