# zbMATH — the first resource for mathematics

Meeting the assumptions of inverse-intensity weighting for longitudinal data subject to irregular follow-up: suggestions for the design and analysis of clinic-based cohort studies. (English) Zbl 07235648
Summary: Clinic-based cohort studies enroll patients on first being admitted to the clinic, and follow them as part of usual care, with interest being in the marginal mean of the outcome process. As the required frequency of follow-up varies among patients, these studies often feature irregular visit times, with no two patients sharing a visit time. Inverse-intensity weighting has been developed to handle this, however it requires that the visit process be conditionally independent of the outcome given the observed history. When patients schedule visits in response to changes in their health (for example a disease flare), the conditional independence assumption is no longer plausible, leading to biased results. We suggest additional information that can be collected to ensure that conditional independence holds, and examine how this might be used in the analysis. This allows clinic-based cohort studies to be used to determine longitudinal outcomes without incurring bias due to irregular follow-up.
##### MSC:
 92B15 General biostatistics 62P10 Applications of statistics to biology and medical sciences; meta analysis 92C50 Medical applications (general)
##### Software:
CRAN; MEMSS; Muhaz; S-PLUS
Full Text:
##### References:
 [1] Buzkova, P., Brown, E., and John-Stewart, G. (2010). Longitudinal data analysis for generalized liner models under participant-driven informative follow-up: an application in maternal health epidemiology. American Journal of Epidemiology, 171:189-197. [2] Lin, H., Scharfstein, D., and Rosenheck, R. (2004). Analysis of longitudinal data with irregular, outcome-dependent follow-up. Journal of the Royal Statistical Society, Series B, 66:791-813. · Zbl 1046.62118 [3] Pullenayegum, E., and Lim, L. (2016). Longitudinal data subject to irregular observation: A review of methods with a focus on visit processes, assumptions, and study design. Statistical Methods in Medical Research, 25:2992-3014. [4] Lin, D., and Ying, Z. (2001). Semiparametric and nonparametric regression analysis of longitudinal data. Journal of the American Statistical Association, 96:103-113. · Zbl 1015.62038 [5] Lin, D., and Ying, Z. (2003). Semiparametric regression analysis of longitudinal data with informative dropouts. Biostatistics, 4:385-398. · Zbl 1154.62330 [6] Liang, Y., Lu, W., and Ying, Z. (2009). Joint modeling and analysis of longitudinal data with informative observation times. Biometrics, 65:377-384. · Zbl 1165.62084 [7] Sun, J., Sun, L., and Liu, D. (2007). Regression analysis of longitudinal data in the presence of informative observation and censoring times. Journal of the Americal Statistical Association, 102:1397-1406. · Zbl 1332.62383 [8] Sun, L., Mu, X., Sun, Z., and Tong, X. (2011a). Semiparameteric analysis of longitudinal data with informative observation times. Acta Mathematicae Applicatae Sinica - English Series, 27:29-42. · Zbl 1207.62091 [9] Sun, L., Song, X., and Zhou, J. (2011b). Regression analysis of longitudinal data with time-dependent covariates in the presence of informative observation and censoring times. Journal of Statistical Planning and Inference, 141:2902-2919. · Zbl 1213.62072 [10] Buzkova, P., and Lumley, T. (2007). Longitudinal data analysis for generalized linear models with follow-up dependent on outcome-related variables. The Canadian Journal of Statistics, 35:485-500. · Zbl 1143.62041 [11] Lange, J. M., Hubbard, R. A., Inoue, L. Y., and Minin, V. N. (2015). A joint model for multistate disease processes and random informative observation times, with aapplication to electronic medical records data. Biometrics, 7:90-101. · Zbl 1419.62384 [12] Muller, H. G., and Wang, J. L. (1994). Hazard rate estimation under random censoring with varying kernels and bandwidths. Biometrics, 50:61-76. · Zbl 0824.62097 [13] Boyd, A., Kittelson, J., and Gillen, D. (2012). Estimation of treatment effect under nonproportional hazards and conditionally independent censoring. Statistics in Medicine, 31:3504-3515. [14] Lam, C., Manlhiot, C., Pullenayegum, E., and Feldman, B. (2011). Efficacy of intravenous ig therapy in juvenile dermatomyositis. Annals of the Rheumatic Diseases, 70:2089-2094. [15] Bode, R., Klein-Gitelman, M., and Miller, M. (2003). Disease activity score for children with juvenile dermatomyositis: reliability and validity evidence. Arthiritis and Rheumatism, 49:7-15. [16] Pinheiro, J., and D. Bates. 2000. Mixed-Effects Models in S and S-PLUS. New York: Springer. · Zbl 0953.62065 [17] Pullenayegum, E., and Feldman, B. (2013). Doubly robust estimation, optimally truncated inverse-intensity weighting and increment-based methods for the analysis of irregularly observed longitudinal data. Statistics in Medicine, 32:1054-1072. [18] Anderson, P., O. Borgan, R. Gill, and N. Keiding. 1993. Statistical Models based on Counting Processes. New York: Springer. [19] Newey, W. (1990). Semiparametric efficiency bounds. Journal of Applied Econometrics, 5:99-135. · Zbl 0705.62033 [20] Gentleman, R. (2015). Package ’muhaz’: A package for producing a smooth estimate of the hazard function for censored data. Technical Report. Comprehensive R Archive Network. [21] Tan, K., French, B., and Troxel, A. (2014). Regression modeling of longitudinal data with outcome-dependent observation times: extensions and comparative evaluation. Statistics in Medicine, 33:4770-4789. [22] Pullenayegum, E. (2016). Multiple outputation for the analysis of longitudinal data subject to irregular observation. Statistics in Medicine, 35:1800-1818.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.