def print_summary(self, decimals=2, **kwargs):
        """
        Print summary statistics describing the fit, the coefficients, and the error bounds.

        Parameters
        -----------
        decimals: int, optional (default=2)
            specify the number of decimal places to show
        kwargs:
            print additional metadata in the output (useful to provide model names, dataset names, etc.) when comparing
            multiple outputs.

        """
        justify = string_justify(18)
        print(self)
        print("{} = {}".format(justify("number of subjects"), self.event_observed.shape[0]))
        print("{} = {}".format(justify("number of events"), np.where(self.event_observed)[0].shape[0]))
        print("{} = {:.{prec}f}".format(justify("log-likelihood"), self._log_likelihood, prec=decimals))
        print(
            "{} = {}".format(
                justify("hypothesis"),
                ", ".join(
                    "%s != %d" % (name, iv) for (name, iv) in zip(self._fitted_parameter_names, self._initial_values)
                ),
            )
        )

        for k, v in kwargs.items():
            print("{} = {}\n".format(justify(k), v))

        print(end="\n")
        print("---")

        df = self.summary
        print(
            df.to_string(
                float_format=format_floats(decimals),
                formatters={"p": format_p_value(decimals), "exp(coef)": format_exp_floats(decimals)},
            )
        )