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Introduction: Repair of Tympanic Membrane (TM) is one of the most common surgeries performed by the otologists. Literature reveals that Indian surgeons have contributed substantially in the research on techniques and graft materials used for the repair of tympanic membrane, though no review has been written so far highlighting their contributions. Aim: To summarize and analyse the contributions of Indian authors who have used different graft materials for repair of TM and their studies listed in Medline search. Materials and Methods: A literature review was conducted using a Medline search using keywords of ‘myringoplasty’ and ‘tympanoplasty’ with ‘India’ on 30th June 2016. A total of 243 articles were found listed onwards from year 1998. Out of these 50 articles in which type 1 tympanoplasty or myringoplasty was performed using different graft materials were selected. The content of each abstract was studied in order to identify studies related to topic. Results: Authors have experimented with a variety of tissues as graft materials. Temporalis Fascia (TF) has been most widely used in 58.6% studies as graft material. The next popular graft is tragal perichondrium. The graft take up rates varied from 68.5% to 100%, while method of reporting of hearing gain in most studies was inconsistent amongst studies, though most studies have reported achievement of serviceable hearing of < 25 dB in most patients. Conclusion: TF was the most prefered material due to anatomic proximity, light material and strength. It was followed in popularity by tragal perichondrium and tragal cartilage. All graft materials have given satisfactory hearing results.

Survival data is a special form of time to event data that is often encountered while modelling risk. The classical Cox proportional hazard model, that is popularly used to analyse survival data, cannot be used for modelling risk when the proportional hazard assumption is violated or when there is recurrent time to event data. In this context we conducted this narrative review to develop an algorithm for selection of advanced methods of analysing survival data in the above-mentioned situations. Findings were synthesized from literature retrieved from searches of Pubmed, Embase, and Google Scholar. Existing literature suggest that for non-proportionality, especially due to categorical predictors stratified Cox model may be useful. An accelerated failure time model is applicable in case of different follow-up time among different experimental groups and the median time to event is the outcome of interest instead of hazard. Extended Cox models and marginal models are used in case of multivariate ordered failure events and the type of model depends upon the presence of clustering and nature of ordering. In the presence of heterogeneity, a shared frailty model is used that is analogous to mixed models. More advanced models, including competing risk and multistate models are required for modelling competing risk, multiple states and multiple transitions. Joint models are used for multiple time dependent outcomes with different attributes. We have developed an algorithm based on the review for appropriate model selection to curb the challenge of modeling survival data and the algorithm is expected to help the naïve researchers in analysing survival data.