Use of multiple imputation to correct for nonresponse bias in a survey of urologic symptoms among African-American men.

The Flint Men's Health Study is an ongoing population-based study of African-American men designed to address questions related to prostate cancer and urologic symptoms. The initial phase of the study was conducted in 1996-1997 in two stages: an interviewer-administered survey followed by a clinical examination. The response rate in the clinical examination phase was 52%. Thus, some data were missing for clinical examination variables, diminishing the generalizability of the results to the general population. This paper is a case study demonstrating the application of multiple imputation to address important questions related to prostate cancer and urologic symptoms in a data set with missing values. On the basis of the observed clinical examination data, the American Urological Association Symptoms Score showed a surprising reduction in symptoms in the oldest age group, but after multiple imputation there was a monotonically increasing trend with age. It appeared that multiple imputation corrected for nonresponse bias associated with the observed data. For other outcome measures-namely, the age-adjusted 95th percentile of prostate-specific antigen level and the association between urologic symptoms and prostate volume-results from the observed data and the multiply imputed data were similar.

[1]  D. Rubin Multiple imputation for nonresponse in surveys , 1989 .

[2]  D. Rubin,et al.  Statistical Analysis with Missing Data , 1988 .

[3]  D. Rubin,et al.  Multiple Imputation for Nonresponse in Surveys , 1989 .

[4]  J. Oesterling,et al.  The prevalence of prostatism: a population-based survey of urinary symptoms. , 1993, The Journal of urology.

[5]  C G Chute,et al.  Serum prostate-specific antigen in a community-based population of healthy men. Establishment of age-specific reference ranges. , 1993, JAMA.

[6]  H A Guess,et al.  Natural history of prostatism: impact of urinary symptoms on quality of life in 2115 randomly selected community men. , 1994, Urology.

[7]  S Greenland,et al.  A critical look at methods for handling missing covariates in epidemiologic regression analyses. , 1995, American journal of epidemiology.

[8]  S. Crawford,et al.  A comparison of anlaytic methods for non-random missingness of outcome data. , 1995, Journal of clinical epidemiology.

[9]  R M Groves,et al.  Advances in strategies for minimizing and adjusting for survey nonresponse. , 1995, Epidemiologic reviews.

[10]  A. Borkowski,et al.  Prostate-specific antigen in black men. , 1996, Lancet.

[11]  J. Shao,et al.  The jackknife and bootstrap , 1996 .

[12]  D. Rubin Multiple Imputation After 18+ Years , 1996 .

[13]  A. Borkowski,et al.  Prostate-specific antigen in black men , 1996, The Lancet.

[14]  J. Moul,et al.  Age-specific reference ranges for serum prostate-specific antigen in black men. , 1996, The New England journal of medicine.

[15]  D. Heitjan,et al.  Annotation: what can be done about missing data? Approaches to imputation. , 1997, American journal of public health.

[16]  J. Cerhan,et al.  Prostate Cancer Trends 1973-1995, SEER Program National Cancer Institute. , 1999 .

[17]  W. Tierney,et al.  Multiple imputation in public health research , 2001, Statistics in medicine.

[18]  John Van Hoewyk,et al.  A multivariate technique for multiply imputing missing values using a sequence of regression models , 2001 .

[19]  J. Wei,et al.  Age-specific distribution of serum prostate-specific antigen in a community-based study of African-American men. , 2001, Urology.

[20]  J. Wei,et al.  The natural history of lower urinary tract symptoms in black American men: relationships with aging, prostate size, flow rate and bothersomeness. , 2001, The Journal of urology.

[21]  K. Cooney,et al.  Potential selection bias in a community-based study of PSA levels in African-American men. , 2001, Journal of clinical epidemiology.

[22]  Trevillore E. Raghunathan,et al.  IVEware: Imputation and Variance Estimation Software User Guide , 2002 .

[23]  Orton,et al.  Multiple Imputation in Practice , 2001 .