Abstract
In a longitudinal setup, as opposed to equi-spaced count responses, there are situations where an individual patient may provide successive count responses at unevenly spaced time intervals. These unevenly spaced count responses are in general accompanied with covariates information collected at the response occurring time points. Here, the responses and covariates are complete as opposed to certain longitudinal data subject to non-response or missing. The regression analysis of this type of unevenly spaced longitudinal count data is not adequately discussed in the literature. In this paper we propose a dynamic model for unevenly spaced longitudinal Poisson counts and demonstrate the computation of correlations among such count responses through an example with T = 4 time intervals such as 4 weeks as the duration of the longitudinal study. Here, if an individual patient reports a problem (in terms of counts) say at time intervals 1, 3, and 4 (i.e., in first, third and fourth weeks); then 3 count responses collected at these 3 times/weeks would be unevenly spaced. Clearly, this individual had nothing to report at time point 2, i.e., in second week, and hence these 3 responses are considered to be complete. Here, we emphasize that this ‘no response’ in the second week for the individual, is, neither a missing response (or so-called non-response) nor can it be quantified as a zero count because no probability can be assigned for a non-existing event. As far as the total number of time intervals is concerned it can be large but it is usually small in a longitudinal setup. However, for accuracy of correlations, one can make each interval small leading to a large value of T. For inferences, the regression parameters are estimated by using the well known GQL (generalized quasi-likelihood) approach. For the estimation of the unevenly spaced pair-wise correlation index parameters we use a standardized method of moments. The performance of the proposed estimation approaches are examined through an intensive simulation study. The results of this paper should be useful to bio-medical practitioners either currently dealing with this type of unevenly spaced count data or planning for data collection on a similar study.
Similar content being viewed by others
References
Al-Oshi, M.A. and Alzaid, A.A. (1987). First order integer valued autoregressive (INAR(1)) process. Journal of Time Series Analysis 8, 261–275.
Freeland, R. K. and McCabe, B. P. M. (2004). Forecasting discrete valued low count time series. International Journal of Forecasting 20, 427–434.
Jowaheer, V. and Sutradhar, B. C. (2002). Analysing longitudinal count data with overdispersion. Biometrika 89, 389–399.
Leppik, E. I., Dreifuss, E. F., Bowman, E. T., Santilli, E. N., Jacobs, E. M., Crosby, E. C., Cloyd, E. J., Stockman, E. J. and Gutierres, E. A. (1985). A double-blind crossover evaluation of progabide in partial seizures. Neurology 35, 285.
McKenzie, E. and Some, A R M A (1988). models for dependent sequences for Poisson counts. Advances in Applied Probability 20, 822–835.
Oyet, A. and Sutradhar, B. C. (2013). Longitudinal modeling of infectious disease. Sankhya B, The Indian Journal of Statistics 75, 319–342.
Sutradhar, B. C. (2003). An overview on regression models for discrete longitudinal responses. Statistical Science 38, 377–393.
Sutradhar, B. C. (2008). On forecasting counts. Journal of Forecasting 27, 109–129.
Sutradhar, B. C. (2010). Inferences in generalized linear longitudinal mixed models. Canadian Journal of Statistics, Special issue 38, 174–196.
Sutradhar, B. C. (2011). Dynamic mixed models for familial longitudinal data. Springer, New York.
Sutradhar, B. C. (2013). Inference progress in missing data analysis from independent to longitudinal setup. Springer Lecture Notes in Statistics, Sutradhar, B. C. (ed.), p. 95–116.
Sutradhar, B. C. and Bari, W. (2007). On generalized quasilikelihood inference in longitudinal mixed model for count data. Sankhya B 69, 671–699.
Sutradhar, B. C. and Zheng, N. (2018). Inferences in binary dynamic fixed models in a semi-parametric setup. Sankhya B 80, 263–291.
Thall, P. F. and Vail, S. C. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657–671.
Winkelmann, R. (2008). Econometric analysis of count data. Springer, New York.
Xu, S. and Grunwald, J.R. H. (2007). Analysis of longitudinal count data with serial correlation. Biometrical Journal 49, 416–428.
Acknowledgements
The authors are grateful to the Editor, Associate Editor and three referees whose comments and suggestions contributed to improvements in the quality of the paper. The research was partially supported by grants from the Natural Sciences and Engineering Research Council of Canada.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix: Moment Equations for Pair-Wise Correlation Index Parameters
Appendix: Moment Equations for Pair-Wise Correlation Index Parameters
Moment Estimating Equation for ρ 13
Based on the same approach used for ρ12 (see Eq. 4.1)), the moment equation for ρ13 may be constructed as
where
and
Moment Estimating Equation for ρ 14
Similar to Eq. 5.1, the moment equation for ρ14 is given by
where
and the corresponding expected function fs(ρ14) has the formula
Moment Estimating Equation for ρ 23
In this case,
where
and the corresponding expected function is given by
Moment Estimating Equation for ρ 24
This moment equation is written as
where
and
Moment Estimating Equation for ρ 34
The moment equation for the last correlation index parameter ρ34 is written as
where
and fs(ρ34) has the formula
Rights and permissions
About this article
Cite this article
Oyet, A.J., Sutradhar, B.C. Analyzing Unevenly Spaced Longitudinal Count Data. Sankhya B 83, 342–373 (2021). https://doi.org/10.1007/s13571-019-00200-2
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13571-019-00200-2
Keywords and phrases
- Dynamic models
- Estimation and forecasting
- Longitudinal event
- Pair-wise dynamic dependence between adjacent responses
- Regression effects
- Unevenly spaced occurrences