11  Sensitivity

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It is unclear how many proxies (k) should be selected to be included in the propensity score model as there is no clear guideline in the literature.

11.1 Sensitivity analysis for k

11.1.1 Create propensity score formula

Hence we iterate the process (change k parameter in get_prioritised_covariates function in step 4) and obtain odds ratio (exponentiation of log-OR) for each k. We varied k from 10 to 90.

Tip

Find out where OR estimates stabilizes

11.2 Sensitivity analysis for n

Tip

We varied n from 10 to 90, remaining everything else constant

Hence we iterate the process (change n parameter in get_candidate_covariates function step 2) and obtain odds ratio (exponentiation of log-OR) for each n. We varied n from 10 to 90.

Tip

Find out where OR estimates stabilize

Literature suggested that this restriction of n can be detrimental (Schuster, Pang, and Platt 2015). Hence in the original analysis we chose n such that that is larger than available empirical covariates.

Schuster, Tibor, Menglan Pang, and Robert W Platt. 2015. “On the Role of Marginal Confounder Prevalence–Implications for the High-Dimensional Propensity Score Algorithm.” Pharmacoepidemiology and Drug Safety 24 (9): 1004–7.