Using logit panel data modeling to study important factors affecting delayed completion of adjuvant chemotherapy for breast cancer patients

Abstract

In analysing panel data in which the dependent variable is a binary choice variable taking values 1 or 0 for success or failure respectively, it is feasible to consider the conditional probabilities of the dependent variable. Under strict exogeneity, this conditional probability equals the expected value of the dependent variable. This treatment calls for a nonlinear function which will ensure that the conditional probability lies between 0 and 1, and such functions yield the probit model and the logit model. This paper discusses an estimation of nonlinear logit panel data model with fixed effects. There are two main estimators for such models: 'unconditional maximum likelihood' and 'conditional maximum likelihood'. Application study was designed to determine the most important factors affecting delayed completion of adjuvant chemotherapy among patients with breast cancer and adjuvant chemotherapy improvement outcomes of patients with breast cancer to determine the relationship between time to chemotherapy and outcome according to breast cancer. The optimal timing from beginning to the end of chemotherapy is known (three months). We hypothesized that prolonged time to chemotherapy would be associated with adverse outcomes. Delayed time to chemotherapy was defined as more three months from the first dose and the last dose of chemotherapy. The study results show that the conditional fixed effects logit estimator is efficient and better than the unconditional pooling and unconditional fixed effects logit estimators. And we find that the most important factors affecting delayed completion of adjuvant chemotherapy among patients are haemoglobin, platelets, and alanine transaminase.